NORTHWESTERN UNIVERSITY

Semantic Knowledge of Eminent Jazz Performers: A Study on the Impact of Community Affiliation and Expertise

A DISSERTATION

SUBMITTED TO THE GRADUATE SCHOOL

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

for the degree

DOCTOR OF PHILOSOPHY

Field of Music Theory and Cognition

By

Caroline Anson Davis

EVANSTON, ILLINOIS

June 2010

2

© Copyright by Caroline Anson Davis 2010 All Rights Reserved

3 ABSTRACT

Semantic Knowledge of Eminent Jazz Performers:

A Study on the Impact of Community Affiliation and Expertise

Caroline Anson Davis

How does our knowledge about music influence the way we interpret it? Previous

research in music cognition has approached the role of cognitive representations in the active

processing of musical stimuli (Meyer, 1956; Lerdahl & Jackendoff, 1983; Deliège 1989, 1991,

1992; Krumhansl, 1990; Deutsch, 1999). Such studies have revealed the effect of musical

features on implicit responses to music; however, they have not commented on how the content

and structure of semantic knowledge about the music – associative meaning – impacts the

listening process. The structure and function of this knowledge system also seems to depend on

experience. Studies on expertise and cultural influences on music cognition suggest that listeners

with similar experiences and affiliations have similar representations of musical structure

(Castellano et al., 1984; Kippen, 1987; Huron & Ollen, 2003; Thompson, 2004; Bar-Yosef,

2007). However, these studies have relied on indirect evidence, dealing with listeners’ implicit

responses rather than attempting to detail listeners’ explicit knowledge structures.

The primary purpose of this dissertation was to model the content and structure of

associative knowledge for a specialized domain of music, namely, that of eminent jazz

performers. In so doing, it relied on self-reflections and explicit responses from professional jazz

musicians in several local music communities, who have years of experience listening and

performing. Initial focus group interviews revealed that musicians tended to describe excerpts by

referring to names of other musicians and by discussing broad characteristics of these

performers. Therefore, a subsequent study asked participants to associate musicians’ names with

4 15-second excerpts of familiar recordings (association task), as well as to match musical

descriptors to performer-name prompts (descriptor-matching task). Social network analysis

(SNA) techniques were used to group participants into musical communities to determine the

effect of community affiliation on the content and structure of associative knowledge.

Results pointed to differentiated knowledge for each excerpt and performer prompt, and

implied that community affiliation, expertise, and several demographic variables impacted the

content and structure of this knowledge. Specifically, the results demonstrated differences

between community affiliation groups on the association task and between expertise groups on

the descriptor-matching task. These higher-level cognitive structures were related to previously

held theories in cognitive psychology (Rosch, 1975b; Medin & Shaffer, 1978), suggesting that

associative musical meaning is content-specific, hierarchically organized, and specialized to the

listener’s experience.

5 ACKNOWLEDGEMENTS

I would like to thank the Graduate School and the School of Music at Northwestern for

the monetary and academic support I have received for the past five years. Especially at the early

stages of my career at Northwestern, when I was heavily involved in music performance and

music studies, I experienced nothing but positive encouragement from both the administration

and faculty.

I offer my sincere gratitude to the musicians I have known and played with in Chicago

and to those who dedicated their time and energy to my research. I would especially like to thank

Bobby Broom and Geof Bradfield, who have each provided insightful comments on the matters

of jazz scholarship. I am also indebted to the following musicians who have communicated with

me on a musical level, which is possibly one of the best gifts I could have hoped for during this

process: James Davis, Sean McCluskey, Jeff Greene, Jon Deitemyer, Matthew Golombisky,

Dave Miller, Quin Kirchner, Katie Wiegman, and Leslie Beukelman.

I would also like to extend my thanks to those who have encouraged me in the Music

Theory/Cognition program. I feel so lucky to have had the pleasure to learn and work with one of

the most positive and innovative scholars I have ever known, Richard Ashley. His ability to push

me has been one of the greatest gifts I have experienced as a developing scholar. I would also

like to extend my thanks to Robert Gjerdingen, who has always been available and willing to

identify with my ideas by relating them to his incredibly vast knowledge. I owe a debt of

appreciation to the following graduate students, who have attended my presentations, graciously

provided suggestions on my work, and simply asked how I am doing: Kyung Myun Lee, Ji Chul

Kim, Jung Nyo Kim, Ives Chor, Dana Hamblet Strait, Alexandra Parbery-Clark, Ben Duane, Ben

Anderson, and Karen Chan.

6 My deepest thanks go to those with whom I have had personal friendships and

relationships during this process. My mother has always provided me with unconditional love

and support from all angles, but she has especially encouraged during the most challenging times

of my life. I am grateful for long conversations with my father, who has offered his insight on

meditation and consciousness that have propelled me forward. My closest friends, Danny

Mekonnen, Megan Martens, Haley Kitts, Bianca Hooman, Katie Wiegman, Leslie Beukelman,

Jennifer Swanson, Sean McCluskey, Matthew Golombisky, and Dave Miller, have endured long

conversations during some of the rockiest roads of my life, and for that I am eternally grateful for

them. Finally, I wish to thank James Davis, who has supported me with all the depth of his spirit.

Your love, silliness, serious cooking skills, and ability to sit through my rants have reminded me

of what is important in life. You are and always will be my best friend.

7 TABLE OF CONTENTS

Abstract .......................................................................................................................3 Acknowledgements......................................................................................................5 Table of Contents.........................................................................................................7 List of Tables.............................................................................................................10 List of Figures............................................................................................................13 Chapter 1: Introduction ..............................................................................................14 Introduction and Chapter Overview................................................................15 Musical Meaning............................................................................................18 Concepts and Developments of Expertise .......................................................24 Context and Coordination in Performance...........................................29 Purpose and Questions of the Study................................................................34 Operational Terminology and Methodological Overview................................35 Author Reflexivity..........................................................................................38 Study Limitations ...........................................................................................41 Chapter Summary and Dissertation Overview.................................................41 Chapter 2: Literature Review Introduction: Review of Purpose and Chapter Overview.................................43 Varieties of Mental Representation.................................................................44 Introduction ........................................................................................44 Models of Semantic Knowledge in Memory .......................................45 Models of Cognitive Processing..........................................................54 Feature- Versus Concept-Driven Processing Models ...........................56 Integrative Processing Models ............................................................60 The Impact of Social Group and Culture on Cognitive Behavior.....................65 Introduction ........................................................................................65 Social Groups and Behavior................................................................67 Culture and Cognition.........................................................................73 Cognitive Representations and Processing of Music .......................................78 Introduction ........................................................................................78 Models of Music Representation and Processing.................................80 Referential and Associative Representations of Music ........................86 Social Groups, Culture, and Music .................................................................91 Introduction ........................................................................................91 Social Influences on Musical Experience ............................................92 Culture, Music, and Cognition ............................................................99 Professional Musicians ..................................................................... 104 Chapter Summary......................................................................................... 109

8 Chapter 3: Research Methods and Design Introduction: Restatement of Purpose and Chapter Overview ....................... 111 Methodological Overview ............................................................................ 112 Focus Group Interviews................................................................................ 113 Participants ....................................................................................... 114 Materials and Procedure.................................................................... 116 Results .............................................................................................. 119 Discussion and Relevance to the Main Study .................................... 123 Main Study: Concepts for Eminent Jazz Performers ..................................... 126 The Network Approach..................................................................... 126 Conceptualization Tasks ................................................................... 129 Pilot Study: Participants.................................................................... 132 Pilot Study: Materials and Procedure ................................................ 133 Eminent Performer Study: Participants.............................................. 137 Eminent Performer Study: Materials and Procedure .......................... 137 Hypotheses................................................................................................... 139 Chapter Summary......................................................................................... 142

Chapter 4: Data Analysis and Results Introduction: Review of Goals and Chapter Overview .................................. 144 Collaborator Task......................................................................................... 145 Overview.......................................................................................... 145 Analysis Procedures.......................................................................... 145 Results .............................................................................................. 152 Summary of Results.......................................................................... 157 Association Task .......................................................................................... 158 Overview.......................................................................................... 158 Analysis Procedures.......................................................................... 158 Results: Categories, Frequency and Agreement Scores ..................... 162 Participant Attribute Effects.............................................................. 170 Ratings and Accuracy ....................................................................... 177 Summary of Results.......................................................................... 183 Descriptor-Matching Task ............................................................................ 186 Overview.......................................................................................... 186 Analysis Procedures.......................................................................... 186 Results .............................................................................................. 188 Participant Attribute, Accuracy, and Influence Rating Effects ........... 191 Summary of Results.......................................................................... 192 Comparison of Participant Attribute Influences ............................................ 193 Chapter Summary......................................................................................... 194 Chapter 5: Discussion and Conclusions Introduction: Review of Objectives and Chapter Overview........................... 196 Interpretation of Results ............................................................................... 197

9 Collaborator Task: Network Properties of Jazz Communities............ 197 Jazz Communities as Attribute-Related Clusters ............................... 200 Association Task: Semantic Memory for Eminent Jazz Performers ... 203 Association Task: Organization of Semantic Memory....................... 208 Attribute-Based Contexts of Associative Representation ................... 212 Descriptor-Matching Task: Cognitive Instantiations of Performers ... 217 Descriptor-Matching Task Attribute-Based Influence on Performer Representations................................................................................. 220 Suggestions for Future Research................................................................... 222 Practical Implications for Music Educators................................................... 225 Conclusion ................................................................................................... 227 Tables ...................................................................................................................... 229 Figures..................................................................................................................... 259 References ............................................................................................................... 276 Appendix A: Focus Group Background Survey........................................................ 304 Appendix B: Focus Group Study Circle Diagrams ................................................... 306 Appendix C: Name Associations.............................................................................. 318

10 List of Tables

3.1 Focus Groups: Participant Demographics .................................................. 115

3.2 Focus Group Recordings ........................................................................... 117

3.3 Discourse Analysis Symbols...................................................................... 118

3.4 Focus Group Discussion ............................................................................ 229

3.5 Focus Group Description of Excerpts ........................................................ 237

3.6 Pilot Study: Participant Demographics....................................................... 133

3.7 Pilot Study Excerpts .................................................................................. 135

3.8 Eminent Jazz Performer Study Excerpts .................................................... 138

3.9 Pilot and Eminent Jazz Performer Study Descriptors ................................. 245

4.1 Participant Attributes................................................................................. 150

4.2 Attribute Recoding .................................................................................... 151

4.3 Geodesic Counts Between Participants ...................................................... 246

4.4 Geodesic Distances Between Participants .................................................. 249

4.5 Degree-Degree Correlations Between Participants..................................... 252

4.6 Hierarchical-Clustering Iterations .............................................................. 256

4.7 Girvan-Newman Partitions ........................................................................ 257

4.8 Density Values for Participants.................................................................. 258

4.9 Community Affiliation Groups by HC Groups ANOVA............................ 155

4.10 Community Affiliation Groups by GN Clusters ANOVA .......................... 155

4.11 Age Groups by Network Properties Cross-tabulations................................ 156

4.12 Experience Groups by GN Clusters Cross-tabulation ................................. 156

4.13 Network Properties by Preferred Performance Styles Cross-tabulations ..... 157

11 4.14 Instrument Codes....................................................................................... 159

4.15 Criteria Coding Strategies.......................................................................... 160

4.16 Name Associations with Frequency Scores Greater than 5......................... 163

4.17 Name Association Agreement Scores ........................................................ 164

4.18 Excerpts by Name Association Agreement Scores ANOVA ...................... 164

4.19 Instrument Association Frequency Scores.................................................. 165

4.20 Instrument Associations by Frequency Scores ANOVA............................. 166

4.21 Instrument Association Agreement Scores................................................. 167

4.22 Instrument Association Agreement Scores by Excerpts ANOVA............... 167

4.23 Association Criteria Frequency Scores....................................................... 168

4.24 Association Criteria Frequency Scores by Excerpt ANOVA ...................... 168

4.25 Association Criteria Agreement Scores...................................................... 169

4.26 Association Criteria Agreement Scores by Excerpts ANOVA.................... 169

4.27 Age Groups by Instrument Associations Cross-tabulation.......................... 170

4.28 Age Groups by Association Criteria Cross-tabulation ................................ 170

4.29 Instrument Groups by Instrument Associations Cross-tabulation................ 171

4.30 Instrument Groups by Association Criteria Cross-tabulation...................... 171

4.31 Experience Groups by Instrument Associations Cross-tabulation............... 172

4.32 Experience Groups by Association Criteria Cross-tabulation ..................... 172

4.33 Education Groups by Instrument Associations Cross-tabulation................. 173

4.34 Education Groups by Association Criteria Cross-tabulation ....................... 173

4.35 Performance Style Groups by Instrument Associations Cross-tabulation.... 174

4.36 Performance Style Groups by Association Criteria Cross-tabulation .......... 174

12 4.37 HC Groups by Instrument Associations Cross-tabulation........................... 175

4.38 HC Groups by Association Criteria Cross-tabulation ................................. 176

4.39 GN Clusters by Instrument Associations Cross-tabulation ......................... 176

4.40 GN Clusters by Association Criteria Cross-tabulation................................ 176

4.41 Community Affiliation Groups by Association Criteria Cross-tabulation... 177

4.42 Typicality and Influence Ratings ............................................................... 178

4.43 Performer Identification Accuracy............................................................. 179

4.44 Performer Instrument Categories ............................................................... 180

4.45 Musical Descriptors and Codes.................................................................. 187

4.46 Descriptor-Prompt Matches ....................................................................... 189

4.47 Descriptor-Matching Agreement Scores .................................................... 190

4.48 Comparison of Influential Factors on Categorical Data.............................. 193

4.49 Comparison of Influential Factors on Continuous Data.............................. 194

13 List of Figures

1.1 Performance and Gricean Maxims: Bass Solo..............................................31

2.1 Semantic Network Structure ........................................................................48

4.1 Example of a Matrix in Social Network Analysis....................................... 146

4.2 Professional Jazz Musician Collaborator Network ..................................... 259

4.3 Professional Jazz Musician Collaborator Network in Clusters.................... 260

4.4 Louis Armstrong Associations Network .................................................... 261

4.5 Ornette Coleman Associations Network .................................................... 262

4.6 John Coltrane Associations Network ......................................................... 263

4.7 Miles Davis Associations Network ............................................................ 264

4.8 Duke Ellington Associations Network ....................................................... 265

4.9 Herbie Hancock Associations Network...................................................... 266

4.10 Coleman Hawkins Associations Network .................................................. 267

4.11 Billie Holiday Associations Network......................................................... 268

4.12 Charles Mingus Associations Network ...................................................... 269

4.13 Thelonious Monk Associations Network ................................................... 270

4.14 Wes Montgomery Associations Network ................................................... 271

4.15 Charlie Parker Associations Network......................................................... 272

4.16 Jaco Pastorius Associations Network ......................................................... 273

4.17 Max Roach Associations Network ............................................................. 274

4.18 Sonny Rollins Associations Network......................................................... 275

14 CHAPTER 1

INTRODUCTION

Fly me to the moon and let me sing among the stars, Let me see what spring is like on Jupiter and Mars,

In other words, hold my hand, in other words, baby kiss me.

Fill my heart with song, and let me sing for evermore, You are all I long for, all I worship and adore,

In other words, please be true, in other words, I love you.

– Bart Howard

How do we, as listeners, interpret a song like Fly Me to the Moon? Old standards from

the Great American Songbook bring to mind associative images, based on different kinds of

interpretation. First, Howard’s lyrics denote an elated sense of jubilation in the presence of one’s

partner. However, this emotion seems to be presented against a backdrop of slight sorrow,

suggested by the phrase “please be true.” Harmonically speaking, these combined views of joy

and sadness are musically supported by a vacillation between major and minor tonalities.

Emotionally, the writer wants to experience assurance from his partner, and he communicates

this desire with the plea, “to be true.” Other meaningful aspects of this standard can be explored

by referring to particular versions of it. Among the many recordings of this song, the 1964

version by Frank Sinatra and the Count Basie band stands out as a prototype. The genre of the

recording, due to its instrumentation, focus on improvisation, and overall timbre, is jazz. The

light drums and flute introduction, followed by the delayed entrance of a triumphant big band

almost demand it to be a dance number. As a musician who has played in many wedding bands, I

can attest to its popularity as a “first dance” number for newlyweds. In addition, since the lyrics

imply a level of long-term commitment, the song is appropriate for a wedding. Finally, the

15 performers on the Sinatra-Basie recording bring to mind particular autobiographical and

historical references. Ol’ Blue Eyes not only had a particularly cunning voice, but also chose

eclectic career moves, including memorable performances with the famed Rat Pack and

purported affiliations with the mafia (Rojek, 2004). Count Basie and his band, local to Kansas

City and Chicago, were known for their innovative approach to big band composition, based

mostly upon simple riffs and variations. The mixture of these associative interpretations forms a

composite in the listener’s mind and guide present or future interpretations.

As suggested by this brief reading of Fly Me to the Moon, music presents the opportunity

for multiple associations to arise in memory. The lyrics imply particular emotions and mood

states, the Sinatra-Basie recording places the song in an established genre and function, and the

performers remind the listener of the performers’ autobiographies and of events in history. This

dissertation explores the role of such associations in familiar jazz recordings and determines their

relationship to experiential variables such as community involvement and expertise.

Introduction and Chapter Overview

Prior research has contemplated the involvement of cognitive mechanisms in musical

processing, including the instantiation of structural patterns common to multiple forms of music,

given the inherent perceptual capacities of the human mind (see, for example, Lerdahl &

Jackendoff, 1983; Sloboda, 1985; Dowling & Harwood, 1986; Krumhansl, 1990; Deutsch,

1999). The majority of such studies have focused on surface-level musical features, such as pitch

or harmony, and have used controlled experimental paradigms to test participants’ responses to

different aspects of the chosen musical dimensions. The stimuli employed are typically

unfamiliar pieces of music, taken from the traditional Western canon. Although these paradigms

16 have been altered somewhat due to recent methodological discussions (Leman & Schneider,

1997; Purwins & Hardoon, 2009), experimental studies of this kind continue to be the mainstay

of research in music cognition. One implication from these studies is that interpretations of

music are based on intuitive principles of grouping and organization of concrete musical features

rather than on associations and information about performers or composers. These studies’

conclusions suggest that implicit, rather than explicit knowledge guides listeners’ impressions of

music. By drawing this connection, these studies ignore the importance of overt associative

thinking patterns that are available to our immediate awareness.

Research in music cognition also typically displays a focus on general tendencies instead

of individual differences within and between populations. In more recent years the interest in

accumulated experience and sociocultural variables has started to be a significant strand in the

systematic study of music (Castellano et al., 1984; Kippen, 1987; Huron & Ollen, 2003;

Thompson, 2004; Bar-Yosef, 2007), although it is not a new concept (Meyer, 1956). Of these

variables, Leonard Meyer (1956) argued:

Music is not a “universal language.” The languages and dialects of music are many. They vary from culture to culture, from epoch to epoch within the same culture, and even within a single epoch and culture…Witness the fact that in our own culture the devotees of “serious” music have great difficulty in understanding the meaning and significance of jazz and vice versa (p. 62).

In addition to this theoretical backdrop, Meyer also agreed with the importance of uncovering

musical universals across cultures. Such commonalities can be seen as a thread connecting

research by music theorists and psychologists; by studying them, a researcher can acknowledge

the importance of cultural and individual differences, but focus his efforts on musical universals

to highlight those differences. Cross-cultural and social group studies can in fact reveal a number

17 of similarities in processing and representation of musical structures (Krumhansl, 1990), but

they also have the potential to reveal slight differences in musical perception, meaning, and

notation (Walker, 1978, 1987, 1997).

This dissertation proposes that studies on the representation and processing of music may

benefit from an alternative focus. It asks questions such as: What associations, not directly

explained by perception of musical features, do people utilize when they listen to music? What

explicit knowledge and memory structures are involved in the processing of familiar, as opposed

to unfamiliar, music? And, especially, how do sociocultural affiliations and expertise-related

factors influence these associative structures?

This project pursues a novel and distinctive approach, concentrating on the activation of

explicit rather than implicit knowledge1 during the processing of familiar music. Specifically, it

attempts to examine the content and structure of semantic knowledge for eminent jazz

performers and to assess the influence of sociocultural affiliations on the generation of musical

meaning, via these explicit cognitive systems. By relying on participants’ self-reflections of their

cognitive processes, the methodology used in this study demonstrates how listeners associate

referential concepts and categories with musical stimuli. Listeners’ activation and retrieval of

semantic knowledge is shown to be reliant on a set of abstract, high-level cognitive processes

rather than on concrete, low-level perceptual features. I begin this chapter with a discussion of

types of musical meaning, helping to frame the present study within previously established

theories. This is followed by an explanation of this study’s specific interest in musicians,

1 The distinction I draw between these two forms of knowledge is similar to that proposed by Dienes and Perner (1999): “The most important type of implicit knowledge consists of representations that merely reflect the property of objects or events without predicating them of any particular entity. The clearest cases of explicit knowledge of a fact are representations of one’s own attitude of knowing that fact…knowledge capable of such fully explicit representation provides the necessary and perhaps sufficient conditions of conscious knowledge” (p. 752).

18 focusing on my rationale for studying experts and on the relationship between musicians’

performance practices and their construction of musical meaning. The purpose, research

questions, conceptual terminology, and methodology of the present study are then presented in

brief overview. Finally, I will discuss the general impact of author reflexivity and personal

experience in relation to the present work.

Musical Meaning

The concept of musical meaning is ancient (Plato, The Laws, Book III) and rooted in

monographs of composers and music aestheticians (Hanslick, 1891; Stravinsky, 1936). In the

modern era, Leonard Meyer was one of the first musicologists to speculate on the relationship

between cognitive principles of perception, meaning, and emotion in music. In the first chapter

of his seminal text, Emotion and Meaning in Music (1956), Meyer stated that his purpose was to

determine “what constitutes musical meaning” (p. 1). He furthered this initiative by investigating

the psychological interplay between construction of meaning and deviation from common

musical patterns. As a result, he introduced a distinctive framework for the concept of musical

meaning. Theoretically, Meyer differentiated two primary types of meaning, each dependent on

its referent: absolute, which is internally contained in the musical work, and referential, which

points to external ideas or mental states. According to Meyer, both add to the composite meaning

ascribed to a musical work, and neither is more important than the other, even though Meyer

himself primarily explored absolute properties throughout his oeuvre (Meyer, 1967; 1973; 1989).

In Emotion and Meaning in Music, Meyer further distinguished two “aesthetic positions”:

formalists, who insist that meaning arises from the comprehension of patterns in the work, and

expressionists, who believe that meaning can be explained by physical or emotional reactions,

19 ancillary to the work itself. Meyer then distinguished three other types of meaning:

hypothetical, evident, and determinate. The first type arises within large-scale stylistic

constraints, the second within moment-to-moment musical “gestures” during real-time

perception, and the third by associating the first two outside of real-time (p. 37). Using this

gamut of terms, Meyer set the stage for a multi-level, hierarchically organized system of

meaning, and thus for the processing of music. He located himself in the camp of both formalist

and absolute expressionists, generally concentrating on the meaning born of one’s moment-to-

moment perception of deviations from stable, memory- and knowledge-driven expectations

regarding musical structure and process. This supported his belief in a “triadic relationship”

between the “stimulus, that to which the stimulus points, and the conscious observer” (p. 34),

directly related to philosopher Charles Peirce’s (1931-1958) triadic model of the representamen

(the sign), interpretant (the interpretation of the sign), and object (for which the sign stands).

In a later article, Meyer (1967, Part One), reformulated his previous approach via

concrete examples of expectation probability, governed by information theory. To clarify the

relationship between typical patterns within style systems, Meyer expanded upon two terms:

designative meaning as pointing to a nonmusical concept – the “character of a work” – and

embodied meaning as pointing to a musical concept – “expectations about musical events”

(1967, p. 7). He asserted that listeners form musical expectations on the basis of their

“psychological processes ingrained as habits in the perceptions, dispositions, and responses,” or

stylistic knowledge (p.7); and that, “…each musical experience…modifies, though perhaps only

slightly, the internalized probability system (the habit responses) upon which prediction

depends” (p. 47). Even though Meyer’s theories relied on listeners’ presumed knowledge of

learned style systems via the “history of culture, art, and the artist” (1967, p. 63), he did not

20 detail how these style systems were organized and represented in memory, nor did he

comment on the way in which these systems are retrieved. Instead, in this and two of his other

texts (1956; 1967; 1989), he concentrated more on distinct musical features that contributed to

the experience of emotion and meaning of the work, composer, and style system. His theories

assumed that listeners experience hypothetical, evident, and determinate meanings both during

and after hearing the work, given the pattern of musical devices in the work itself as well as

compiled memories and knowledge of musical patterns. In a later article that considered a

different combination of psychology and music, Meyer (1980) developed the idea of a musical

“archetype,” similar to the cognitive psychological notion of a schema, or a unified conceptual

chunk for a set of items or ideas. According to Meyer, the archetype relied on descriptions of

distinct musical parameters that contribute to a sense of what the music expresses; however,

Meyer did not discuss this higher-level essence of music as much as the moment-to-moment,

low-level features of music.

As an alternative to Meyer’s expectation-driven perspective, Eric Clarke (2005)

approached the study of music ecologically, through the emphasis of associations between the

structure of the environment and perceptual experience. Primarily influenced by James Gibson’s

ecological perspective of visual perception (1979), Clarke focused three aspects of perception:

1. Listeners are active in their perception via a process of orientation, 2. Listeners create and

adapt to musical systems, and 3. Listening experience is gained via both passive and active

learning, creating multiple forms of representation. To support these assertions, Clarke rejected

the dominant information-processing view, which tends to rely primarily on bottom-up

processing mechanisms and largely ignores the role of action in perception. He stated that

“people seem to be aware of supposedly “high-level” features much more directly and

21 immediately than the lower-level features that a standard information-processing account

suggests they need to process first” (p. 16). Clarke noted the ways in which listeners’ comments

on music reflect overall musical messages; listeners tend, for example, to discuss genre and

emotion rather than scales and dynamics. He described semantic knowledge as built-in,

distributed systems that rely on ecological circumstances (e.g. auditory, physiological, and

cultural) for accessibility and retrieval. As the driving force in this equation, Clarke situated his

theory within the connectionist view of cognition:

perceptual and cognitive processes can be modeled as the distributed property of a whole system, no particular part of which possesses any “knowledge” at all, rather than as the functioning of explicit rules operating on fixed storage addresses which contain representations or knowledge stores (p. 26).

Distinctly influenced by Artificial Intelligence (AI), this approach views cognition as a network

of related nodes (or units of information) that could be activated at any given moment, given

appropriate contextual circumstances. The musical example Clarke provided dealt with

preferences for melodies with certain properties, such as those “which start and finish on the

same note, generally move in a stepwise manner, but contain at least two intervals of a major

third or more” (p. 27). According to Clarke, these melodies tend to sound more “correct,” and

thus listeners activate the nodes pertaining to those features more than melodies that do not.

Given these comments, Clarke created a distinct and convergent position with regard to

methodology, one that considered the environmental context and construction of musical

experience as actively shaped by the listener.

In both their theories, Meyer and Clarke implied that musical meaning is actively

constructed by the listener via a set of active cognitive mechanisms. As such, it is often likened

22 to language because it communicates meaning via a system of user-designed syntactic

principles (Longfellow, 1835; Sloboda, 1985; Aiello, 1994; Patel, 2003). However, a widely

accepted system of musical meaning does not seem to exist, because of the process of

interpretation – meaning is imposed by the listener (Meyer, 1956; Clarke, 2005). Instead of

relating musical meaning to structural components of language, such as syntax, it may be more

fruitful to consider its relationship to semiotic principles of language (Burkholder, 2007). In the

words of polymath Theodore Adorno:

Music aspires to be a language without intention. But the demarcation line between itself and the language of intentions is not absolute; we are not confronted by two wholly separate realms. There is a dialectic at work. Music is permeated through and through with intentionality…Music points to true language in the sense that content is apparent in it, but it does so at the cost of unambiguous meaning, which has migrated to the languages of intentionality (1956, p. 3).

Music’s meaning, then, is heavily contingent on a listener’s interpretive activity. This tension

between absolute and intentional meanings is often the cause of heated debates on the

evolutionary functions of music (Pinker, 1997, p. 524-5). Pinker argued that art, including music,

serves the mental “circuitry” of pleasure and implied that absolute meanings of art are more

biologically than psychological oriented. On the other hand, Adorno noted: “Music finds the

absolute immediately, but at the moment of discovery it becomes obscured, just as too powerful

a light dazzles the eyes, preventing them from seeing things which are perfectly visible” (p. 4).

As indicated in this colloquial observation, music presents opportunities, or potential moments,

for constructing meaning.

23 The search for a typology of meaning still pervades the scholarship of music. In a

recent study on the neurological correlates of musical semantics, Koelsch and colleagues (2004)

opened their article by categorizing four subtypes of musical meaning:

i) Meaning that emerges from a connection across different frames of reference suggested by common patterns or forms

ii) Meaning that arises from the suggestion of a particular mood iii) Meaning that results from extra-musical associations iv) Meaning that can be attributed to the interplay of formal

structures in creating patterns of tension and resolution (p. 302).

Elements of Koelsch’s classification echo the theoretical descriptions presented by both Meyer

and Clarke, but the terminology is slightly different. Related to, but distinct from Koelsch’s

strategy, this study uses yet another typology of musical meaning, related to advances provided

by Meyer and Clarke:

i) Meaning that results from abstract “higher-level” concepts ii) Meaning that is actively retrieved from semantic knowledge

systems in memory, referential in nature iii) Meaning that arises from a particular sociocultural state of

mind and relies on accessibility of previously learned style systems

Point i) relates directly to Clarke’s observation that listeners are more likely to discuss genre and

emotion rather than timbre and dynamics. The notion of higher-level semantic knowledge in

memory, specifically related to language-specific concepts, will be elaborated upon in the next

chapter. These concepts are most clearly related to Meyer’s notion of referential meaning,2 or

that which is indirectly related to the music itself. The influence of listeners’ sociocultural

mindsets on their construction of musical meaning will also be detailed in chapter 2; this

connects directly to the notion of ecological contexts described by Clarke.

2 As Koelsch (2004) stated, referential meaning is often called “extra-musical association.”

24 Concepts and Developments of Expertise

Because the meaning systems described above imply a certain level of involvement and

knowledge of the musical domain, this study is primarily concerned with the ways in which

professional musicians, those who contemplate music on a daily basis, construct musical

meaning. Musical representations are more detailed and accessible for those who interact with

music on a deep and consistent basis. I would not hold that musicians have more sophisticated

knowledge structures than nonmusicians, but that the “global qualities” of their thought

processes demonstrate the use of cognitive heuristics, or developed knowledge structures for

problem solving (Minskey & Papert, 1974, p. 59). Experts frequently interact with their domain

of interest with heightened attention and memory as well as active engagement in “pushing the

boundaries” (Schneider, 1985; Alexander, 2003, p.12); this is as true of professional musicians

as it is with experts in other domains. Given their experience with listening and performing,

musicians are able to process music automatically and with minimal cognitive workload

(Schlaug, 2003; Bangert et al., 2003). The way in which these expert processes shape cognition

is often overlooked in studies on music perception; therefore, the following sections will

elaborate on the professional practices that constitute the basis of such expertise.

Some of the most basic modes of learning – imitation, repetition, elaboration – aid in

retention and formation of knowledge in any domain (Dawkins, 1976; Blackmore, 1998).

According to Dawkins (1976), imitation leads to promulgation of memes, or culturally

transmitted beliefs, intentions, and values. Other researchers have expanded this concept to

include additional kinds of experience; for example, Blackmore (1998) argued that the use of

imitation to solve problems is an innate function of humans, compared to birds and primates who

are not capable of this level of integration. It is easy to see that music makes much use of

25 patterns of repetition, and musicians contribute to the propagation of standardized norms by

imitating these patterns. Adorno (1941) speculated on the elements that reinforce standardization

in popular and jazz music:

Imitation offers a lead for coming to grips with the basic reasons for it [standard patterns]. The musical standards of popular music were originally developed by a competitive process. As one particular song scored a great success, hundreds of others sprang up imitating the successful one. The most successful hits, types, and “ratios” between elements were imitated, and the process culminated in the

crystallization of standards (p. 443).

Arguably, these processes added to the grammaticality and lexicality of music, such that standard

patterns form conventionalized systems that define certain genres or styles. The performer, who

synthesizes what she has heard before to present a specialized viewpoint, contributes to a form of

what Adorno referred to as “natural” music. Adorno asserted that past experiences, including

songs introduced during childhood and melodies from a given time period, form the standardized

elements of natural music. He also believed that jazz was the most “drastic example of

standardization of presumably individualized features” and simplified stylistic patterns down to a

set of repetitive, recognizably accessible schemes:

Even though jazz musicians still improvise in practice, their improvisations have become so “normalized” as to enable a whole terminology to be developed to express the standard devices of individualization…Improvisations…are confined within the walls of the harmonic and metric scheme. In a great many cases, such as the “break” of pre-swing jazz, the musical function of the improvised detail is determined completely by the scheme: the break can be nothing other than a disguised cadence (p. 445).

Although he focused more on issues of commercialism and accessibility in popular music,

Adorno’s claims were loosely based on the assumption that pop and jazz musicians search for

sources of influence, incorporating previously explored patterns into their own music, thus,

26 resulting in a body of repetitive, accessible, commercial art. These parameters add to the

formation of musical identities, not as entities that mark elements of personal identity, but as

units of musical patterns and influences that add to the music itself (MacDonald et al., 2002).3

In the jazz idiom, musicians approach the development of identities in a variety of ways,

such as learning from older musicians, attending jam sessions, listening to recordings,

transcribing patterns, practicing with collaborators, and memorizing repertoire (Berliner, 1994).

Because this process involves repetition, imitation, and elaboration of previous ideas, the young

musician faces a truly daunting responsibility with regard to her future professionalism. Often

the choice of who and what to imitate provides the musician with the set of tools – semantic and

procedural knowledge – to use, given a standard contextualization. Synthesizing such musical

influences engages the musician’s conscious mind to tailor a unique semantic knowledge system,

or elaborated network of related concepts, structured to meet the goals of the musician.

My own observations of musicians in performance situations reflect the claims presented

above. Since I have been exposed to my own and others’ processes of immersion in the

professional world of music, I have noticed that our social practices expose the development of

musical identities. One anecdote helps to illustrate the relationship between verbalized

knowledge and musical identity: during a set break, a musician made a comment about a

recording playing on the speakers in the restaurant, “damn, that was when Tain was playin’ on

Zildjians.” Some of the musicians sitting near the table, including myself, acknowledged his

observation by referring to the group on the recording, but none questioned the motivation

3 MacDonald, Hargreaves, and Miell (2002) have distinguished between two types of musical identity: identities in music, or “those aspects of musical identities that are socially defined within given cultural roles and musical categories,” and music in identities, or “how we use music as a means or resource for developing other aspects of our individual identities” (p. 2). The present study is more concerned with the former.

27 behind this colloquial, yet sophisticated remark. As I retired home that evening I reflected

upon this interchange, finding myself astonished at the minute details musicians know about

their influences – in this case the brand of cymbal played by drummer Jeff ‘Tain’ Watts – and the

nonchalant manner in which they communicate that detailed information to others. What

motivated this musician to become so familiarized with Watts’ cymbal choices throughout his

career, and why did he find it necessary to express it to other musicians? It is my belief that his

observation represents his passion for hearing important nuances in one of his influences and

communicated his desire for others to be informed of this passion.4 Indeed, Wilson and

MacDonald (2005) suggested that musicians’ talk indicates “negotiative processes of identity

construction” (p. 344). In this study, verbalizations contained information that characterized the

speaker’s placement within a genre, social group, or value-laden institution. In a related study,

MacDonald and Wilson (2005) found that views on improvisation included two prominent

views: an interpretation of a composition, or the integration of practiced patterns (licks) and

spontaneous creation. Differing views were also found for the concepts of swing, collaboration,

instruments, and social as well as professional context in jazz. The authors stated that “…being a

jazz musician is one of a number of possible musical identities for these musicians, one that

allows them to perceive themselves as a group” (p. 412, emphasis theirs). The ethnomusicologist

Ben Sidran (1971) assessed jazz as an oral culture, which communicates “a direct reflection of

the immediate environment and of the way in which members of the oral community relate that

environment” (p. 10, emphasis his). In light of this theoretical backdrop, the anecdote above can

be interpreted as an act of identity construction in a primarily oral culture.

4 The last point may be the best explanation, as this musician is known to be upfront about his knowledge of Jeff ‘Tain’ Watts as well as cymbal brands. In addition, he is endorsed by Sabian Cymbals, which was originally the parent company of Zildjian cymbals, but is now a rival manufacturer.

28 Many jazz musicians experience these identity-forming processes early on in their

careers. In Thinking in Jazz, Paul Berliner (1994) included a passage on the process of realizing

one’s influences, via focused practice:

Gary Bartz “basically learned one thing” from each of the musicians who assisted him—“saxophone technique” from one, “dynamics and articulation” from another, “chords” from a third. Similarly, an aspiring pianist learned the general principles of jazz theory from Barry Harris, discovered “how to achieve the independence of both hands and how to create effective left hand bass lines” under pianist Jaki Byard’s tutelage, and expanded his repertory with someone else (pp. 51-52).

Identity shaping, then, requires processes of feature extraction, selection, and comprehensive

integration; features like technique, chords, articulation, and lines mentioned by Bartz. George

Lewis (2008) identified important early experiences of musicians in the Chicago-formed

Association for the Advancement of Creative Musicians (AACM), including informal mentoring,

churchgoing, and family communal performances. In some cases, young musicians learned about

potential influences by listening to what their family members listened to:

[Jodie] Christian’s father’s brother-in-law ‘had a collection of records in the thirties of all the blues players, which would be a collector’s item now…When I’d come to the house, he always played them’ (p. 11).

As this interview statement suggests, the availability of resources changes with social and

cultural context, so developing musicians are not always in control of their identities. Lewis also

mentioned the role of educators in shaping early musical development. In the case of some of the

musicians in the AACM, they were privy to the tutelage of Captain Walter Dyett, who not only

encouraged students to practice multiple instruments in changing contexts, but also created

unique performance opportunities, almost to the point of extremism. Since Dyett has been

described as a “commanding leader and a demanding taskmaster,” with respect to the traditional

29 model of learning jazz, it is perhaps the case that some of later members of the AACM acted

in opposition to his teachings (Wang, 2003, p. 1). Another venue for development discussed at

length by both Lewis and Berliner is the jazz jam session, which provides opportunities for

spontaneous musical communication and performing standardized repertoire. Jam sessions also

offer musicians a chance to network with others, potentially forming groups of their own, based

on mutual experiences and preferences (Berliner, 1994; Lewis, 2008). All these elements shape

musicians to varying degrees, and they come about through hours of formative work in solitary

practice, in which learners decompose, integrate, polish, and maintain music they wish to

perform (Chaffin & Imreh, 1997, 2002).

Multifaceted layers of learning through imitation and elaboration complicate the burden

of integrating a learner’s influences into a solidified unit. Studies on narrative analysis of life

stories have advocated that the process of communicating these units, as musical identities,

results in the “making of the self” (McAdams, 1993). As will be evident in the next section, these

resultant musical identities are realized in how musicians describe and perform music.

Context and Coordination in Performance

In addition to the verbalization of personal histories and experiences, musical identities

can arise within the context of a performance. A previous study, conducted by myself and

Richard Ashley (2005), considered the relationship between patterns in live performance and

shared intentions as expressed in a post-performance discussion. The study’s purpose was to

understand the way in which shared knowledge of jazz patterns is realized in musical

improvisations. After videotaping a professional trio’s live performance at a local venue, an

interview was conducted, which focused on the following: “When you present an idea, do you

30 assume that another musician will respond? How so?” A detailed analysis of one chorus of a

bass solo as part of a performance of Jerome Kern’s All the Things You Are revealed the

musicians’ shared interpretations of meaning in the form of uptake and agreement, similar to the

content and structure of conversation (Austin, 1962; Searle, 1969; Grice, 1975; Clark, 1996).

Musical phrases (utterances) presented by the bass player were acknowledged (uptake) and

responded to (agreement) by other members in the ensemble in appropriate ways and were

further analyzed by applying principles of Gricean pragmatics.

Grice (1975) distinguished between what is said versus what is implicated in the

communicative medium of language. According to his theory, speakers participate in

conversations based on cooperation and implied, shared purposes. Grice called this the

cooperative principle and further wrote that when participants enter into a conversation, they

agree to “make [your] conversational contribution such as is required, at the stage at which it

occurs, by the accepted purpose or direction of the talk exchange in which [you] are engaged” (p.

45). As suggested by the live performance data, jazz musicians engage a similar set of principles

during performance – although moment-to-moment goals relate more to anticipation and

coordination to dynamic events than do post-performance interpretations. Berliner (1994) asked

professional musicians to comment on performance experiences and further described the

understanding of these processes:

Saxophonist Lee Konitz also ‘wants to relate to the bass player and the piano player and the drummer, so that I know at any given moment what they are all doing. The goal is always to relate as fully as possible to every sound that everyone is making…At different points, I will listen to any particular member of the group and relate to them as directly as possible in my solo’ (p. 362).

31 As shown by this purposeful way of relating to band members, musicians present and respond

to musical ideas in ways similar to Grice’s maxims of conversation (1975): make your

contribution as informative as is required for the current talk exchange (maxim of quantity), do

not say that for which you lack adequate evidence (maxim of quality), make your contributions

relevant (maxim of relevance), and be brief, avoiding obscurity and ambiguity (maxim of

manner). An example of this in practice considered by Grice in an earlier work elucidates these

maxims:

A and B are talking about a mutual friend, C, who is now working in a bank. A asks B how C is getting on in his job, and B replies: Oh quite well, I think; he likes his colleagues, and he hasn’t been to prison yet. (1967a, p. 24).

Speaker A has implied that either friend C is a dishonest person who steals money, tempted by

the context of a bank or that his statement is a joke. Context and the knowledge of the involved

parties allows the hearer to reach appropriate conclusions based on the information provided.

The video performance and interview data from the jazz trio provide musical analogues. In one

example, the bass player presented a repeating syncopated idea (figure 1.1), which the drummer

interpreted as an implication – a request for a response – prompting him to play along with the

bassist, while maintaining a slight variant of the original statement.

Figure 1.1: Performance and Gricean Maxims: Bass Solo

32 In both the musical and conversational examples, the musician (or speaker) presented a vague

statement (utterance) that could be construed in a variety of ways, depending on the context. We

see that participants’ interactions depended on the use of domain-specific knowledge structures.

In the conversation, perhaps B was a comedian and presented a joke about friend C; likewise, in

the musical exchange, perhaps the bassist was known to play repetitive syncopated ideas until

the drummer accentuated them, thus communicating his acknowledgement of the statement.

To determine the influence of shared knowledge structures on performed interactions in

jazz, we added a stage of analysis based on Herbert Clark’s concept of grounding, a process by

which people seek out mutual knowledge, or common ground, during a shared activity. Clark

and Brennan (1991) applied the concept of grounding to speech in conversation and devised the

steps to determine common ground between actors. Their system assumed that “they [actors]

cannot even begin to coordinate on content without assuming a vast amount of shared

information…mutual knowledge, mutual beliefs, and mutual assumptions” (p. 127). In order to

communicate, actors must display their understanding in some form of response, such as

acknowledgement, continued attention, and relevant relating. Similar processes unfold in a

musical performance. In addition to the musical evidence provided by the musicians in the live

performance, their interview statements revealed aspects of the mutual knowledge necessary for

performing music at various levels of experience. The phrase “coming down to their level” was

used in several instances and indicated that these musicians were aware of a hierarchically-

organized typology of response in performance. In other words, less-experienced musicians may

require more information or time to respond in desirable and appropriate ways. With these

constructs in mind, the interview statements contributed to a general model, based on shared

knowledge structures, for analyzing jazz musicians’ interpretations during live performance.

33 Moreover, it provided some initial foundations to understand the ways in which performing

musicians deal with moment-to-moment aspects of interaction.

Relationships of Clark’s work to processes in jazz performance have been depicted in

Berliner’s (1994) descriptions of jazz performance, although they are not explicitly referenced.

Regarding harmony, Berliner stated, “they [musicians] constantly interpret one another’s ideas,

anticipating them on the basis of the music’s predetermined harmonic events,” (p. 394). On

forming a repetitive rhythmic framework, Berliner noted that “striking a groove” is not just about

a “shared sense of beat,” but also a sense of “emotional empathy” (p. 350) structuring subtle time

and tempo changes. Further, on repertoire and structure he wrote, musicians “…depend on their

knowledge of each other’s generation or style period and musical personality to anticipate the

ideas their counterparts are likely to perform in particular sections of the composition” (p. 357).

Although Berliner discussed musical exchanges at length, one gap in his research was the lack of

systematic observation in nonmusical exchanges. When musicians exchange information on their

influences, or “talk shop,” they often assume that their conversational counterparts possess

similar knowledge representations. For example, the drummer in the aforementioned anecdote

assumed the others around him knew that Zildjians were a brand of cymbal and that elements of

the drummer’s sound were evident in the recording. He also assumed that we, as perceptive

musicians, were interested in Jeff ‘Tain’ Watts’ history as a performing musician, especially his

hardware choices. Given these assumptions, his actions implied that he was communicating his

ideas with a shared “community of practice,” a group of related individuals with similar

perceptions and cognitive thought processes (Lave & Wenger, 1991). Communities of practice

and context provide a backdrop for sharing experiences, whether they are performed or

verbalized.

34 Purpose and Questions of the Study

The purpose of this study is to determine the content and structure of musicians’ shared

knowledge systems for eminent jazz performers. Influential precursors to this work include

semantic network studies based on concrete category perception and interpretation of meaningful

stimuli, which will be further explored in chapter 2 (Collins & Quillian, 1969; Rosch, 1973;

Medin & Ross, 1981). While formulating this study, I conducted several anthropological studies

with members of several jazz and improvised music communities in Chicago, which set the stage

for the forthcoming chapters’ research questions. As will be explained in chapter 3, I also

interviewed two groups of musicians to illustrate the importance of listening and to form the

empirical design of this study. I collected both qualitative and quantitative data in these sessions

to present converging evidence for musicians’ social and semantic knowledge representations.

The main tasks in the present study are divided into two portions, based on normalized

methodological techniques for each topic.

Social Network Analysis: Do musicians belong to communities of practice?

1. What social structures, such as communities and subgroups, are quantitatively visible by asking musicians to name and describe their collaborators?

2. Is there a relationship between closeness and the amount of time spent discussing and listening to music with one’s musical collaborators?

Associative Semantic Knowledge: What is the content of associative representations for music?

3. Do participants display similar interpretations for eminent jazz performers? 4. What parameters determine the structured networks of meaning for eminent

performers?

Affiliation and Cognition: Do collaborative affiliations influence cognitive representations?

5. What is the connection between musicians’ collaborative networks and their interpretations of eminent performers? In other words, is there a relationship between communities of practice (community) and mental representations (cognition)?

35 Musicians have been idealized as people with distinct personas, values, and daily activities –

reading a recent copy of Downbeat or Jazziz will illustrate the ways in which writers summarize

the thoughts and activities of musicians into two-page interviews. A recent documentary entitled

Musician (Kraus & Davis, 2007) detailed the day-to-day activities that structure an improvising

musician’s life; however, there is a notable lack of focus on the shared knowledge structures that

musicians use in performance and discussion. To date, studies on jazz musicians have

overemphasized what musicians do rather than how and why they are able to do them. To

investigate this issue, an in-depth look at musicians’ interpretations of meaningful stimuli is

attempted here, to uncover what, how, and why musicians associate meaning with music.

Understanding these processes is intended to provide a way to forge ties with other disciplines

such as music education, promoting a sense of how educators instantiate learning objectives and

assessment procedures.

Operational Terminology and Methodological Overview

Due to the interdisciplinary nature of this project, it is helpful to include a brief

explanation of the terminology used. Two concepts will be considered here: mental

representation and community affiliation. Originally used as a term to describe the structure and

formal properties of conceptual thinking, the term mental representation is often associated with

the terms concept, internal representation, mental model, instantiation, schemata, and percept

(Turing, 1950; Stich, 1983). This study pairs mental representation with concepts of semantic

knowledge and interpretation, placing emphasis on content and structure. Thus, mental

representation will be used interchangeably with the terms semantic knowledge, concept,

category, or conceptualization, and describes the set of ideas that define a word, person, image,

36 or object. This definition closely resembles that of a complex idea, posited by John Locke

(1690):

Ideas thus made up of several simple ones put together, I call complex; such as are beauty, gratitude, a man, an army, the universe; which, though complicated of various simple ideas, or complex ideas made up of simple ones, yet are, when the mind pleases, considered each by itself, as one entire thing, and signified by one name (Book II, xii, p. 1).

Although the core concept is essentially the same, cognitive psychologists have since sought a

more empirically derived definition for a concept, as an “internal representation that enables the

individual to determine the category membership of objects in the world” (Thelen & Smith,

1996; p.162). Thus, conceptual knowledge provides information on the perception and sensation

of objects in the world, whether they are abstractly or concretely defined.

This project also adopts an integrated view of the term community affiliation, informed

by research on cultural groups in psychology. Culture is used interchangeably with community.

Culture is viewed not as embodying a unified set of ideals, but rather as a distributed network of

potential meanings. This definition is influenced by modern cognitive anthropological notions of

culture as “shared aspects of cognitive representations” rather than structurally homogenous

systems (Romney & Moore, 1998; p. 321). Thus, someone who is labeled as a part of a

community has access to a certain network of knowledge, and being more involved increases the

extent to which they rely on these knowledge structures. Therefore, a synthesis of cognitive

psychological and cultural perspectives characterizes this view of community affiliation.

Aside from those contributing to the musicological literature on jazz culture and politics,

few scholars have developed systematic methodologies to understand both social and mental

processes of professional, modern jazz musicians (Merriam & Mack, 1960; Becker, 1963;

37 Berliner, 1994; Monson, 1996; Jackson, 1998). Recently, musicologists have painted

ethnographic portraits of modern musicians in ethnic genres such as Brazilian and Jewish folk

music, concentrating on their means of survival, repertoire development, and identity in

performance (Packman, 2007; Rapport, 2006). As an alternative, this study uses systematic

measures of social network analysis (SNA) to model the structure of musician communities in a

local area. While participating in several communities in the Chicago jazz and improvised music

scene, I have noticed that most musicians tend to form relationships with a select number of

collaborators. Since I myself have ties to musicians who are influenced by the jazz tradition, I

used my personal connections to ask approximately two-hundred musicians to participate. Fifty-

one musicians, all of whom are full-time musicians, participated in the final experiment.

The primary goal of the present study is to bridge the gap between social and cognitive

studies in music psychology as well as provide a backdrop for the modeling of musicians’

semantic knowledge. In so doing, a broad range of methodological procedures are incorporated.

Social network analysis provides several useful methods for analyzing communities of practice

by asking people to name and evaluate their relationships with others (Wasserman & Faust,

1994). Data are typically represented in the form of nodes (actors) and links (ties) to represent

interrelationships in a larger community. SNA theorists adhere to the notion that actors and their

behaviors are “interdependent rather than independent, autonomous units,” and that ties formed

between actors are “channels for transfer or “flow” of resources (either material or nonmaterial)”

(Wasserman & Faust, 1994, p. 4). Thus, participants were asked about collaborations with other

musicians in Chicago by providing the names of twenty musicians and asking them to evaluate

these names on two scales: how often they discuss music with, and how well they know, each

named musician. These data were entered into the SNA analysis programs UCINET (Borgatti et

38 al., 2002) and Netdraw (Borgatti, 2002), which locate, structure, and interpret clusters of

actors with statistical grouping techniques.

Unrelated to the SNA methods, techniques used to study semantic knowledge provided

the second form of data. Traditional methods for investigating the structure and content of

semantic knowledge make use of reaction times, spatial diagramming, sorting paradigms, and

free association studies; I incorporated the last of these procedures in this study. The free

association task used here required participants to name musicians who came to mind after

listening to a musical excerpt and to reflect upon those associations. A second cognitive task was

employed which asked participants to choose three musical terms to describe a given musician.

The analysis from the answers to these questions included network diagramming as well as

descriptive and inductive statistical procedures to determine the content and structure of

participants’ semantic representations.

Author Reflexivity

Anthropologists and sociologists who conduct research “in the field” note the importance

of reflexivity,5 or the process of coming to terms with the researcher’s personal histories,

perceptions, and biases that shape analyses and interpretations (Peshkin, 1994; Bourdieu, 2003;

Becker, 1963). Commenting on the role of subjectivity in the process of research, Peshkin (1994)

noted,

5 Other processes similar to reflexivity are “subjectivity” (Peshkin, 1994) and participant objectivation (Bourdieu, 2003).

39 Subjectivity operates throughout the entire research process, beginning with the choice of what we study, including our methods for data collecting and our analysis of data, and ending with the conclusions we draw. To be sure, there is not a one-to-one relationship between my affective state, my biography, and my history and my choice of topic, the conclusions I reach, and so forth. But the relationship is very far from random. I can’t always predict what you’ll study if I know you well, but I can understand why you study what you study if I do know you well. With this understanding, I can know something that is much worth knowing: what kind of stake you have in your research topic, if not in reaching particular outcomes (p. 50).

Peshkin confirmed that realizing the effect of one’s readings of observations and data often

characterizes the entirety of the research and writing process, as I myself have come to

understand. As a social scientist, Bourdieu emphasized the awareness of the author’s placement

in the work, or the “objectivation of the subject of objectivation, of the analyzing subject – in

short, of the researcher herself” (p. 282). Throughout this process, Bourdieu believed, we reveal

our “academic unconscious,” our tendencies of categorization and interpretation that tend to

affect all aspects of research, analysis, and written prose. Further, as we come to know ourselves

better, we move closer to the goals and questions propelling our research forward.

Although it was quite necessary, analyzing my position in the field was an arduous and,

at times, vexing process. I have been a saxophonist for fifteen years; the last ten of these I have

also been an aspiring scholar. The latter goal occupied the majority of my time during the

planning and writing of this project, but I tended to incorporate some mélange of practicing,

performing, or attending shows into my daily schedule. As I settled into this routine, I likewise

began to interact more frequently with musicians in various scenes,6 mostly in styles of jazz and

improvised music. My interactions often brought me to a state of frustration, because as a

6 Musicians use the colloquialism “scene” interchangeably with community.

40 participant and an also observer of professional musicianship, I had conflicting passions of

mind and spirit. On the one hand, my emotional spirit desired performing and networking

experiences (usually at a later hour of the day) but the intellectual part of me wished to explore

and dissect the meaning of these interactions. I reached heightened moments on the bandstand,

but often questioned the conditions which gave rise to those moments, thereby hindering the

state of flow I desired. This is not to say that as a professional musician one does not experience

the intellectual process of meaning construction; on the contrary, musicians study and interpret

music just as much as academics. I also experienced many instances of an “I’d-rather-be-doing”

phenomenon, especially when one identity significantly took over the other. Ultimately, the

synthesis of these opposing identities aided in the development of a topic and research agenda I

found to be stimulating to both of my musical “sides”.

Finally, as a “closet ethnomusicologist,” I have sometimes endured long hours of writing

field notes. Looking back in the notebook I kept during the formation of this project, I found that

the questions began as: How are musicians able to talk about recordings with such ease? Do they

listen in the same ways? How do they decide who to play with? Do similar listening styles

inform the decisions of who to play with? Of course, these questions look quite different from

the questions posed for this dissertation, and they developed via a rigorous process of

compromise. My questions were transformed by considerations of traditional scientific methods,

the current state of music cognition as an academic field, and also the schedules and personal

goals of professional musicians.7 I came to realize later that I had indeed experienced, as

predicted by Bourdieu (2003), a “conversion of the whole person” (p. 292).

7 Specifically, I found social network analysis to be a personally designed puzzle, where musicians provided the information for community boundaries instead of my active placement of musicians into the scenes to which I thought they belonged. Methodologically, I was fair to those who participated by

41 Study Limitations

The study of professionals is a difficult process, as one is often limited by who is

available and willing to participate. I contacted approximately three hundred musicians in

Chicago; about a third of whom responded with interest, leading to about one quarter finally

participating. Since the study was conducted using local resources in Chicago, some of my

results may be influenced by geographical characteristics, such as the way the modest cost of

living allows many musicians to play music without earning much. Furthermore, my participants

may not have frequently listened to the eminent musicians that I included in the excerpts, in

comparison to more modern or genre-crossing artists. The decision to include mainstream jazz in

my research materials was made to attempt to have a list of musicians with whom everyone

would be familiar, regardless of their regular performance genre. Thus, it would be inappropriate

to make a one-to-one mapping of my participants’ understanding of eminent musicians to those

to whom they listen most frequently. It would be further inaccurate to assume that these broad

processes of collaboration and listening generalize to all music communities, as local

geographical context undoubtedly plays a significant role in both practices.

Chapter Summary and Dissertation Overview

Through the use of a multifaceted methodological approach, this study attempts to model

the content and structure of associations in professional musicians’ semantic memory and

speculate on the influence of community affiliation on these conceptual systems. The group

interviews and experiments were designed to shed light on cognitive processes of musical respecting location, time, and monetary issues. Finally, I asked close friends in the community to comment on my interpretation of the data and shared findings with participants in the form of updates on my dissertation website.

42 interpretation. I hope that elucidation of these mental operations will fill the gap in the

research literature on musicians’ approaches to meaning construction, especially in the realm of

associative knowledge.

The second chapter will include a review of literature in four relevant areas: 1) semantic

networks, 2) mental representation in music, 3) social groups, culture, and communities of

practice, and 4) the interaction of social groups, culture, and communities with semantic

networks and mental representations. My methodology will be outlined in chapter 3, and will

focus on sampling, collection, and coding procedures. I will also address the challenges of

converging multiple approaches and procedures. I will present the results from the study in

chapter 4. The results from the four tasks will be presented in both separate and convergent

ways. In the fifth and final chapter, I will consider the results within an integrated framework for

cognitive representations and speculate on how this study addresses practical issues for music

educators.

43 CHAPTER 2

LITERATURE REVIEW

Introduction: Review of Purpose and Chapter Overview

The purposes of the present study are to understand associative dimensions of music

cognition and to investigate the influence of sociocultural affiliations and expertise on these

dimensions. Since these goals have not been specifically addressed in the literature, this study’s

research questions involve four broad areas:

Mental Representations and Cognitive Processing How are memories represented in the mind, and how do they effect

cognitive processing? Social Group, Culture, and Cognition How do social group and cultural affiliations influence cognitive processing? Musical Mental Representations and Cognitive Processing How is music represented in the mind, and how are these representations involved in the processing of music? Social Group, Culture, and Music Cognition What are the influences of social group and cultural affiliation on the cognitive processing of music?

This chapter is organized around these four areas, with each of its four sections outlining

research that pertains to the questions above. Because the field of cognitive psychology has

reached somewhat different areas than those in music cognition, I start by reviewing studies in

psychology concerned with varieties of mental representations, summarizing three models of

semantic knowledge in memory. Studies on cognitive processing are also included, which bring

together notions of cognitive systems of memory with those of interpretation. Next, I review

studies on the relationship between cognitive processing and two affiliation variables – social

44 group and culture. The section on music touches upon theories and empirically-driven models

of music processing, referring to ideas of semantic knowledge for musical features. I conclude

the review by examining the few studies that consider the effect of social group and culture on

cognitive structures in music. At the end of the chapter, I tie together the four topics and briefly

summarize their relevance to the present study.

Varieties of Mental Representation

Introduction

The study of mental representations has persisted from early philosophy up to the modern

conception of memory systems in cognitive psychology (Aristotle, deAnima, 402a, Hamlyn ed.;

Kant, 1781/1787; Tulving, 1972). Uncovering the structures and functions of knowledge was the

central concern in the early musings, while more recent research has focused on the following

question: how is knowledge represented, and how does it affect the way we interpret stimuli?

Cognitive psychological theories referred to “coding systems,” or “…the person’s manner of

grouping and relating information about his world…constantly subject to change and

reorganization,” and viewed the mind as a system of active reinterpretation (Bruner, 1957; p. 46).

These ideas have been influenced by theoretical underpinnings set forth by Gestalt psychologists,

such that stimulus parts and their associations were considered the building blocks of the

viewer’s unique and holistic interpretation, based on sets of additive features (Wertheimer,

1924). As understood in these earlier texts, the relationship between instantiated knowledge and

stimulus features was mysterious, especially outside the visual domain. By the mid 1900s, the

field of in cognitive psychology brought a revival of interest in the topic of knowledge

representation, particularly in human memory (Broadbent, 1957; Neisser, 1967; Tulving, 1972). I

45 will first present a number of theories of semantic knowledge content and function, and then

compare several models of organization for this memory system.

Models of Semantic Knowledge in Memory

The modern psychological distinction of a semantic system of memory, distinguished

from others, first appeared in the post-behavioral work of Endel Tulving. Although his was not

the first attempt to theorize about multiple forms of memory, he claimed that “semantic

memory,” as the term had been used in previous works, should be further separated from other

forms of memory, such as “episodic memory.” In a chapter entitled Episodic and Semantic

Memory, he stated:

Semantic memory is the memory necessary for the use of language. It is a mental thesaurus, organized knowledge a person possesses about words and other verbal symbols, their meaning and referents, about relations among them, and about rules, formulas, and algorithms for the manipulation of these symbols, concepts, and relations (1972, p. 386).

According to Tulving, semantic memory consisted of tangible objects and intangible concepts, as

opposed to personalized knowledge about moments in time.8 Thus, his primary approach to

differentiating between memory structures required a classification of memory types, rather than

a separation of unified memory stores. In fact, Tulving characterized memory structures as

highly interrelated, rather than boundary-defined.9 Tulving considered different ways to

elaborate upon the functions of episodic and semantic memory. For example, the following

statement represented the active use of semantic memory for an item of furniture: “I think that

8 Other researchers referred to semantic memory as generic and categorical memory (Hintzman, 1978; Estes, 1976). 9 Tulving placed procedural, semantic, and episodic memory systems in a class-inclusion hierarchy, with episodic as a “specialized subcategory” of semantic memory (1985, p. 386).

46 the association between the words TABLE and CHAIR is stronger than that between the

words TABLE and NOSE.” On the other hand, “I know the word that was paired with DAX in

this list was FRIGID” was a scenario paired with the engagement of episodic memory (p.387).

The latter statement relies on memory for an instance in time, while the former brings to mind

the information associated with a concept. Semantic memory dealt with representations of well-

formed categories and retrieval based on associations, or links, to these categories. This process

was thought to involve cognitive rather than autobiographical reference:

Information stored in the semantic memory system represents objects—general and specific, living and dead, past and present, simply and complex—concepts, relations, quantities, events, facts, propositions…detached from autobiographical reference…he obviously must have learned it, either directly, or indirectly, at an earlier time, but he need not possess any mnemonic information about the episode of such learning in order to retain and to use semantic information (p. 389).

Tulving thought that the function of memory representations was to aid in the processes of

knowledge retention and retrieval, which defined semantic memory as an actively shaped entity.

Early studies in neuroscience supported this idea of memory malleability as neuronal plasticity,

which seemed to be affected by persistent repetition of distributed neuronal activity (Hebb,

1949). Influenced by these neurological conceptions, memory could also be viewed as

distributed cells, which consist of “…a network of associated items which have a high

probability of producing each other” (Posner, 1973; p. 29).10 Drawing upon theoretical work in

logic and language (Pierce, 1880), this network-driven approach accounted for the experience of

elaborations of concepts11 based on their associations. For example, words associated with red

10 By using the word cells, Posner did not mean to imply that memory is a system of neurons, but rather, he used the term in the abstract sense, in order to channel the notion of a distributed network of connections. 11 Throughout this text, concept and category will be used interchangeably.

47 may be stored in memory cells that depend on the conventional meaning of the color,

producing a cluster of information activated by hearing the word or seeing the color in context

(Cohen, 1963). Although the analysis of these cells focused more on the distinction of memory

systems by their characteristics of encoding, representing, and retrieving information, the idea of

classified entities in semantic memory provided a springboard for more formalized models.

The idea of conceptual network systems was posited early on, in German psychologist

Otto Selz’s (1913, 1922) problem-solving paradigms, although his results did not necessarily

capture the complexity of semantic memory, as evidenced in chess-player problem-solving

techniques (de Groot, 1965). Given this gap in the literature, Allan Collins and M. Ross Quillian

(1969) devised a set of experiments to formulate and model the structure and organization of

semantic knowledge as associative networks. The experiments were based on earlier theoretical

assumptions presented in Quillian’s (1966) thesis on word meaning concepts, geared more

towards artificial intelligence and computer modeling of memory. Quillian’s notion of a concept

was as follows:

To summarize, a word’s full concept is defined in the model memory to be all the nodes that can be reached by an exhaustive tracing process, originating at its initial, partriarchical type node, together with the total sum of relationships among these nodes specified by within-plane, token-to-token links (1966, p. 413).

Nodes in memory are organized in hierarchical fashion and can be activated by both direct (type)

and indirect (token) nodes. All the links to a type node are dependent on dictionary and common

sense information for a particular word concept. For example, orange can be seen as a token for

the type nodes fruit or color. Quillian further classified the links as dependent on certain

relations, including super- or subordinate (“is a”), modifier, dis- or conjunctive, and residual,

which clarified the basis for a hierarchical structure. Later associative models expanded the

48 content from dictionary terms to events, episodes, and complex concepts (Collins & Quillian,

1969; Rumelhart et al., 1972). Collins, Quillian, and Rumelhart graphed models as networks,

with nodes representing the type or concept and a set of links between nodes to denote

relationships between them. In a more recent paper, Rumelhart and Todd (1993) depicted the

network structure for living things, including animals and plants (Figure 2.1).

Figure 2.1: Semantic network structure (Rumelhart & Todd, 1993, p. 15).

The model proposes more efficient mental activation of the nodes that are more proximate to the

main concept. Overall, these types of models assume that concepts are defined by networks of

potential meaning and that the potentials depend on previous activations of nodes.

49 Subsequent collaborations between Collins and Quillian (1969, 1970) presented an

experimental paradigm that related retrieval time for a word to node location in an implied

memory structure. Their approach assumed that words prime a hierarchical network and all its

associations, when subjects view and process a word. Their experiments were designed to show

the strength of association between experimentally presented items and those in memory. Their

first experimental paradigm (1969) asked participants to judge sentences on a binary truth-value

(true or false), with reaction times from the judgments taken as the dependent measure. To

account for Quillian’s original theory, the sentences were varied to include both type and token

nodes. For instance, “an oak has acorns” specified a property of the node oak, whereas “a cedar

is a tree” denoted the superset isa in the hierarchy. In addition, properties and supersets were

assigned to various levels of embeddedness, depending on the word. So, oak and cedar were

nested in the hierarchy for tree, and acorns and needles were nested in the hierarchy for oak and

cedar, respectively. In the experiment, Collins and Quillian found longer reaction times for

superset and higher-level property judgments. For example, respondents took longer to judge “a

cedar is a tree” than “a maple is a maple,” because they had to mentally “move up” a level in the

hierarchy for the former. These results also gave a better idea of the term semantic network,

which was defined as a hierarchic structure of associated concepts in memory. Although the

concept was originally used in the early development of artificial intelligence, Collins and

Quillian reestablished its position as a legitimate research topic in cognitive psychology.

The theoretical framework and experiments contributed by Collins and Quillian (1969,

1972) provided a foundation for researchers to consider various models for semantic knowledge.

Originally described as “set-theoretic” (Meyer, 1970; Schaeffer & Wallace, 1970), the feature

comparison model focused primarily on a larger set of attributes for concepts and distinguished

50 between two types of features: defining and characteristic. The former was considered more

essential to a word’s meaning than the latter on a continuum of relatedness, similar to the type

and token nodes in Quillian’s theory. Smith, Shoben, and Rips (1974) demonstrated this

difference with the word robin. Five features for robins were considered: “are bipeds” and “have

wings” were classified as defining, while “have distinctive colors,” “perch in trees,” and “are

undomesticated” were classified as characteristic features. The underlying theory viewed

information processing as a system of evaluation; thus, meaningful information was approached

and interpreted with a set of evaluative questions, such as “what does a robin have?” or “what

color is a robin?” Furthermore, the model elaborated upon previous research by interpreting

concepts as either concrete or abstract, based on the availability of defining and characteristic

features. Although supporters of this model suggested that previous research did not account for

certain feature-detection mechanisms in encoding, processing, and retrieval, Collins and Loftus

(1975) counter-argued by stating “…network models are probably more powerful than feature

models, because it is not obvious how to handle inferential processing or embedding in feature

models” (1975, p. 410). This alternative helped to focus on the summation of features, rather

than on the modeling of memory structure.

Working from similar premises, Eleanor Rosch developed an extension of the feature

comparison model. Rosch (1975b) suggested that categories drive the processing of information

and have specific internal structures, referring “to that general class of conceptions of categories

in which categories are not represented only as criterial features with clear-cut boundaries” (p.

193-194). She agreed with the notion that certain features might be more representative of

categories; but, she furthered this claim by emphasizing the idea of a prototype, an object that

encompassed defining features of a given category. The prototype was her main point of interest

51 – the “what,” rather than the “how,” of structure and content in semantic knowledge. To test

the prototype theory, she collected a series of judgments for categories of words and pictures,

including fruit, bird, vehicle, vegetable, sport, tool, toy, furniture, weapon, and clothing. Her

results showed that her participants had similar ideas of a category’s internal structure,

depending on “good” versus “bad” representations (e.g. “good” fruit: apple, “bad” fruit: lemon).

Her subsequent experiments tested category structure by priming items with matched or

mismatched categories. She then measured participants’ reaction times for true-false judgments.

As expected, “good” items were processed faster than “bad” items when primed with the

matched category, while mismatched category primes hindered response time. Given her results,

she claimed that particular items could be classified as multiple item categories, which have the

potential of being in more than one category simultaneously. This illustrated the complexity of

the decision-making process for such category tasks and implied that multiple strategies of

comprehension are in constant competition.

The spreading activation model, developed by Collins and Loftus (1975), was a third

alternative for modeling the organization of semantic knowledge. Given that Collins worked

closely with Quillian, this project assumed similar theoretical notions. These authors believed

that conceptual processing worked as a network of activations, with less substantive knowledge

on the fringe and more substantive knowledge at the heart of the network. This model’s

elaborations accounted for nodal relations beyond “isa” (superordinate) and “has” (modifier), by

including associations such as “can,” “cannot,” and “is not a” (negative superordinates). This

framework presumed that the mind searches through neighboring concepts to determine the

truth-values of sentences. Additional elaborations by Collins and Loftus included:

52 1. The conceptual (semantic) network is organized along the lines of semantic similarity.

2. The names of concepts are stored in a lexical network (or dictionary) that is organized along lines of phonemic (and to some degree orthographic) similarity. 3. A person can control whether he primes the lexical network, the semantic network, or both. (pp. 411-412).

By representing multiple subordinate concepts within one level, this model addressed the spread

of information from one node to the next and thus accounted for both association strength and

processing speed. In addition, the authors commented on feature-comparison models, claiming

“…there is no feature that is absolutely necessary for any category. For example, if one removes

the wings from a bird, it does not stop being a bird” (p. 425). Their subsequent experiments

required participants to produce the names of categories when primed with variable information,

including letters, superordinate or subordinate category names, and adjective descriptors. For

example, respondents might judge “apple” more quickly when primed with “fruit” rather than

“red” or the letter “A.” The results supported a more complex picture of retrieval mechanisms

which seemed to depend on immediate versus delayed “entrance into the category” or its cluster

(Freedman & Loftus, 1971). Since the results from these experiments were not framed under the

tenets of any particular model, the researchers who endorsed the spreading-activation model

reinterpreted the findings:

The spreading-activation theory predicts these results by assuming that when an item is processed, other items are activated to the extent that they are closely related to that item. That is, retrieving one category

member produces a spread of activation to other category members, facilitating their later retrieval (Collins & Loftus, 1975; p. 419).

Thus, Collins and Loftus placed more weight on categories spreading to each other rather than

items entering into conceptual clusters. In addition, they relied on multiple explanations to

53 understand the subtle differences in the process of retrieval, mainly in the form of processing

speed. Given this slightly different perspective, Collins, Loftus, and other researchers refined

their interpretation of conceptual processing studies by synthesizing both network and feature

driven approaches (Freedman & Loftus, 1971; Juola & Atkinson, 1971; Conrad, 1972; Loftus,

1973a, 1973b; Rips et al., 1973; Collins & Loftus, 1975).

Other approaches viewed the organization of features in memory as unified cognitive

groupings that a participant could exhibit at any given moment of perception. In his summary

text on cognition, Posner (1973) postulated three basic mechanisms for organizing knowledge:

lists, spaces, and hierarchies. According to Posner, lists included conventionally catalogued

items such as numbers and alphabets, which dominate thought processing during retrieval.

Although this was a relatively simple way of organizing information in memory, it has since

proved to be one of the most efficient and effective strategies for knowledge retrieval (DeSoto,

1961). Spaces were defined by Posner as multifaceted mental structures that represented more

than three attributes of a particular set of concepts and that were depicted as multi-dimensional

graphs. Experiments that incorporated similarity judgments or sorting tasks supported this

theory; concepts were shown to be based on evaluations of three or more attribute dimensions, as

shown determined by statistical feature-driven approaches such as multidimensional scaling or

factor analysis (Osgood et al., 1957; Romney & D’Andrade, 1964). Additionally, Posner (1973)

commented on the variability of mental structures:

54 There is little reason to suppose that the human mind is limited to any particular type of mental structure. Indeed, there is much reason to believe that structures vary with different individuals and cultures and within an individual from time to time. However, experiments do suggest that the particular format or structure which we use to store information in the memory system guides the nature of our effortless-retrieval processes and thus has important consequences for our thinking (p. 89).

He thus acknowledged the malleable character of mental structures and stood as an advocate for

the connection between memory and processing. Since this and other evidence (Tulving, 1972;

Cermak & Craik, 1979) suggested that there to be a reciprocal relationship between memory

structures and mental processing, I will now turn to this topic.

Models of Cognitive Processing

What is the function of memory in the processing of meaningful stimuli? Typically, we

tend to remember the most common categorical information from stimuli, but mental

representations may be formed from personalized reconstructions of stimuli (Bartlett, 1932;

Bruner, 1957; Posner, 1973; Loftus, 1974). Thus, meaning may not only be extracted from

explicit definitions of words and concepts, but also from a unique interpretation of the word or

concept (Medin et al., 1992, p. 336). This concept has philosophical roots in Aristotle’s idea that

the mind actively constructs thoughts related to presented stimuli in the external world (c. 350,

deAnima, 402a, Hamlyn ed.) Aristotle’s original idea inspired questions such as, how do we

interpret and comprehend stimuli around us, and what cognitive structures are useful for these

processes? Based on such early questions, the cognitive revolution brought about a renewed

interest in function and processing, in addition to content and organization of knowledge in

55 memory (Neisser, 1967). The following will include a review of cognitive processes of

abstraction as a framework for understanding the formation of semantic memory.

Tailoring an interpretation to fit a particular context is often referred to as the process of

abstraction, which likewise requires a series of personalized judgments and comparisons. John

Locke provided a succinct description of this phenomenon in An Essay Concerning Human

Understanding:

the mind makes the particular ideas received from particular objects to become general…This is called abstraction, whereby ideas taken from particular beings become general representatives of all of the same kind; and their names general names, applicable to whatever exists conformable to such abstract ideas…Thus the same colour being observed in chalk or snow, which the mind yesterday received from milk, it considers that appearance alone, makes it a representative of all of that kind; and having given it the name whiteness, it by that sound signifies the same quality wheresoever to be imagined or met with (1690; Book II, Ch. 11, 9).

Locke emphasized the processes of attending to and extracting features from a given stimulus,

resulting in an integrated picture of a concept. Aiming to apply Locke’s basic ideas to the

development of empirical methods, cognitive psychologists reconstructed the view of the mind

as a center for information processing. One of the basic tenets of the information processing view

of cognition likens the mind to a computer, with distinct units of perception, input, and central

processing as well as mechanisms of storage and retrieval in memory (Hebb, 1949; Neisser,

1967; Atkinson & Shiffrin, 1968). Mervis and Rosch (1981) described processes of abstraction

as “ways in which the cognitive system acts “creatively” on input during learning of categories

and uses the resultant categorical information to classify novel items” (p. 103). The complexity

of such a process lies in separating relevant from irrelevant features, thus, forming a “higher

order” (via a central processor) representation of the presented stimulus. Some researchers

56 presented a synthesis of artificial intelligence and behavioral psychology, using mathematical

formulae and computational modeling to explain cognitive processing (Newell & Simon, 1972;

Newell, 1982). On the contrary, many cognitive psychologists interested in attention, memory,

and knowledge pursued a different path and considered abstraction in terms of both feature

extraction and category formation (Posner & Mitchell, 1967; Schaeffer & Wallace, 1970).

Posner (1973) suggested that there were two basic approaches to abstraction; “one involves

selection of one part of the input…the other involves classification of the input into more general

categories” (p. 96). These two processes will be compared below.

Feature- versus Concept-Driven Processing Models

As previously mentioned, feature extraction12 and category formation are highly related;

however, the extent and directionality of this relationship is unclear. Many strategies for category

formation and attainment have been differentiated, based on the results from empirical reasoning,

judgment, and categorization studies (Hull, 1920; Bruner et al., 1956; Haygood & Bourne,

1965). These studies asked participants to respond to a variety of concept-learning paradigms

and provided feedback to categorized items over multiple trials. Typically, researchers assumed

that concept attainment had occurred when judgments were error-free. Stimulus features were

judged on their pertinence to the task. One of the earliest studies on this topic (Hull, 1920)

emphasized the way in which passive feature extraction and mechanisms of association

influenced the formation of concepts. This experiment required participants to study lists of

Chinese characters and syllables containing similar strokes. Responding to six lists, all with the 12 Numerous synonyms have been used to describe similar processes: feature extraction and detection have been used to describe components of machine learning and artificial intelligence, while others have referred to this human cognitive ability as cue abstraction (Juslin et al., 2003) or attribute retrieval (Mervis & Rosch, 1981).

57 same character features, participants were instructed to speak aloud each syllable-character

pair, to which feedback was provided. The results showed that participants responded faster and

more accurately with each subsequent list presentation. Hull concluded that concept learning,

based on simple methods of feature extraction, was successfully achieved during this process.

This proposed method of learning concepts is most similar to the aforementioned feature-based

models of memory. Building on Hull’s contribution, modern theories of concept formation based

the specific processes of feature extraction and combination on more active processes. To

provide an overview of concept formation and processing, three theoretical models, prototype,

exemplar, and integrative, will be compared.

One theory of concept formation posits that the human mind naturally abstracts

categories from stimuli which have no preformed segments, actively labeling and defining the

stimuli (Leach, 1964). Similarly, Rosch argued that representations contain a distributed set of

connections, most likely defined by a central tendency. In many publications, Rosch (1973,

1975a, 1975b, 1978) disputed the previously held notion of strict categories and proposed instead

the idea of fuzzy, indeterminate boundaries for categories and item membership. She asserted

that the organization of categorical knowledge is based on attribute density, or the number of

features that are central to the category. According to Rosch, items with higher relevant attribute

density are basic level categories and may be judged as the most representative examples of an

item, giving rise to typicality in category membership. Her proposal claimed that viewers

“appear to operate inductively by abstracting a prototype, the central tendency, of an item’s

conceptual distribution” a prototype which then appears to “operate in classification and

recognition of instances” (Rosch 1973, p. 329). This statement is undoubtedly influenced by

previous research on prototypes, which showed more accurate and faster classification responses

58 for novel patterns that resembled a category’s prototype (Posner & Keele, 1968). Rosch

supported the prototype theory with a series of experiments, the first of which illustrated a

facilitation effect of within category priming on same-different judgments (Rosch, 1975b). She

also showed similar effects for other stimuli including colors, lines, and numbers (Rosch, 1975a).

Rosch’s results accounted for both stimulus features and preexisting mental representations with

the notion of a cognitive “anchor,” or reference point, to which successive stimuli are compared.

An alternative model explains feature extraction and concept formation by referring to

representative instances – exemplars – in memory. Most exemplar theories argue for exact,

accurate representations of a stimulus; however, different stances have been taken with regard to

how they weight features in a given stimulus. Independent cue models, such as the weighted

feature prototype, proposed that participants attend to attributes separately and then additively

combine them in an integrated interpretation (Bransford & Franks, 1971; Reed, 1972). Reed

(1972) distinguished models based on average cue validity or distance, which require within-

category comparison. Based on categorical judgments of schematic faces, Reed concluded that

participants referred to “an abstract image or prototype to represent each category” and used it

“to classify test patterns on the basis of their similarity to the two prototypes” (p. 401). On the

other hand, interactive cue models held that viewers attend to attributes both additively and

multiplicatively, which justified more complex relationships between attributes. Medin and

Schaffer (1978) contributed a modified version of the interactive cue model called the context

theory of classification. The models’ conditions were based on both cue and contextual

information:

59 Information concerning the cue, the context, and the event are stored together in memory and that both cue and context must be activated simultaneously in order to retrieve information about the event. A change in either the cue or the context can impair the accessibility of information associated with both (p. 211).

Thus, during the processing of information, a literal instance of an item is referred to in order to

abstract category membership. The multiplicative rule specified processing facilitation for items

that were highly similar to exemplars and dissimilar to non-exemplars. The authors tested this

theory with a series of learning and transfer experiments using geometric shapes and Brunswick

faces. In the first experiment, participants were presented with geometric shapes, categorized

into two sets based four attributes (form, size, color, and position). As their learning of the

categories improved with experimenter feedback and trials, respondents were required to classify

“new” stimuli after a meaningless distracter task. They also rated how confident they felt about

their judgments. Results showed more “hits” for interactive-cue or exemplar items and “false

alarms” for independent-cue items, which supported the context model. Additional research with

Brunswick faces echoed these results and illustrated the efficiency of multiple strategies rather

than single models for concept learning situations (Medin & Smith, 1981). In general, this

research suggests that cognitive processing is analogically rather than analytically based, which

implies that new information is compared to knowledge structures in memory (Brooks, 1978).

More recent research on feature extraction employs complex computational formulas to

catalogue relevant attributes relative to a representative item (Newell & Simon, 1972; Einhorn et

al., 1979; Gigerenzer et al., 1999; Juslin et al., 2003). Computer-activated algorithms are

designed to emulate human cognitive processing; thus, many of these models map directly on to

those previously mentioned. Juslin and colleagues (2003) summarized the theory and

computational scrutiny of three models of feature extraction, the cue abstraction model (CAM),

60 the lexicographic heuristic (LEX), and the exemplar-based model (EBM). The CAM

associates and weights attributes according to their importance within a category. The authors

asserted that this process is contingent upon level of training, cue weights, and cue integration,

the latter of which is not specified by the model. Also called “specificity theory,” this model has

been implemented in studies of early learning paradigms, in which specific features of an object

were modified in direct comparison to other objects (Gibson, 1969). Processing in the form of

recognition and identification was hindered when the number of feature differences increased

(Gibson & Gibson, 1955). The LEX requires focus on a single cue, interpreted as the most

accurate, which is then used to form an interpretation (Gigerenzer et al., 1999). Finally, the EBM

supposes that a specific instance of related stimuli is formed and instantiated in memory, creating

an original context to be retrieved during the judgment stage (Nosofsky, 1992). This model is

undoubtedly influenced by the context theory of classification developed by Medin and Schaeffer

(1978). A modification, the template-matching model, or pandemonium model, assumes that

exact internal representations are used to interpret existing patterns, therefore requiring less

information about context (Selfridge, 1958; Norman, 1973). This type of paradigm is often

referred to as top-down, or conceptual-driven processing, contrasted with bottom-up, or data-

driven. (Norman & Rumelhart, 1975). These two processing strategies are often played against

each other; however, another significant body of research has commented on their integration.

Integrative Processing Models

Integrative theories support the view of multi-level processing mechanisms. Biederman

(1987) specified multiple stages in visual pattern recognition, including segmentation,

categorization, and prototypification. The results from an earlier object-recognition experiment

61 indicated that both shortened stimulus exposure and type of component deletion hindered

object recognition (Biederman et al., 1985). His devised recognition-by-component theory

suggested:

the ease with which we are able to code tens of thousands of words or objects is solved by mapping that input onto a modest number of primitives…and then using a representational system that can code and access free combinations of these primitives (Biederman, 1987; p. 145).

This system accounted for the interpretation of a stimulus by using both perceptual (new) and

conceptual (preexisting) input; thus, it necessitated both bottom-up and top-down processing in

pattern and stimulus recognition. Although not originally applied to ecologically valid situations,

interactive cue models may also fit the type of integration theorized by Biederman and others

(Medin & Schaffer, 1978; Medin & Smith, 1981). If previously learned information is

considered during processing, it may be safe to assume that the mind naturally takes advantage

of these multiple mechanisms.

Philosopher and psychologist Jerry Fodor considered multi-level processing units, or

“modules,” that combined stimulus properties and previous experience to form percepts.

According to Fodor (1983), these modular systems consist of computational subsystems that

transfer information to each other – a “trichotomous functional architecture” including

transducers, input systems, and central processors (p. 43). He thought that perception included

three phases: transduction of perceptual information, interpretation by input systems, and

mediation of perceptual and conceptual by central processors – a combination of both bottom-up

and top-down processing mechanisms. In Modularity of Mind (1983), Fodor explained nine

conditions of his integrative cognitive input system:

62 1. Input systems are domain specific. 2. The operation of input systems is mandatory. 3. There is only limited central access to the mental representations that input systems compute. 4. Input systems are fast. 5. Input systems are informationally encapsulated. 6. Input analyzers have ‘shallow’ outputs. 7. Input systems are associated with fixed neural architecture. 8. Input systems exhibit characteristic and specific breakdown patterns. 9. The ontogeny of input systems exhibits a characteristic pace and sequencing.

As suggested by these criteria, input systems modify the external properties of a stimulus by way

of internal cognitive grammars, which are tailored to each domain (e.g. hearing, sight, touch,

taste, smell). Input systems were viewed as ingrained, fixed entities, like Noam Chomsky’s

(1966) system of innate generative grammars for language processing. Fodor’s departure from

Chomsky’s theories was the idea of a central processing unit, in which beliefs and experiences

play a role in forming impressions. Fodor defined the central processor as isotropic, or connected

to unbounded knowledge systems, and Quineian, or connected to belief systems. The modularity

thesis was explained colloquially by the statement, “I couldn’t help hearing what you said.”

Fodor emphasized, “…it is what is said that one can’t help hearing, not just what is uttered” (p.

55). Even though Fodor’s main purpose was to divide cognition into distinct processing modules,

his theory ultimately supported the notion of integrative processing units in cognitive

psychology.

Other research has demonstrated individual differences in processing strategies. Bruner

and colleagues (1956) provided an in-depth look at two strategies for problem solving

experiments. Participants were asked to generate hypotheses from given information, requiring

the integration of confirming (it does have) and infirming (it does not have) attributes. They

classified the behavior of participants into two strategies: wholist/focusing and partist/scanning.

63 The participants endorsing the wholist strategy attended to an integration to formulate their

hypotheses, “maximizing information yield and reducing the strain on inference and memory”

(Bruner et al., 1956; p. 130). The alternative was the partist strategy, whereby a certain

proportion of the initial instance was catalogued and subsequently modified. The authors noted

that this may have required “either a system of note-taking or a reliance on memory” (p. 132).

Several experiments conducted by the authors showed that participants depended on one of the

strategies during problem solving, but with an overall preference for the wholist strategy. In a

later publication, Bruner (1957) discussed these strategies as “going beyond the information

given,” via processes of categorization, probabilistic learning, and utilization of formed coding

systems. Originally applied to the topics of teaching and learning, the coding system

incorporated a sequence of cognitive events:

When one goes beyond the information given, one does so by virtue of being able to place the present given in a more generic coding system

and that one essentially “reads off” from the coding system additional information either on the basis of learned contingent probabilities or learned principles of relating material (p. 49).

As evidenced in this passage, Bruner placed more importance on top-down processing units, but

an acknowledgement of integrative processing was implicitly present.

Schema13-driven models also support the notion of top-down analogic processing.

Schema theory is related to the Gestalt school of perception, which proposed that multiple parts

of a stimulus were organized and combined by the mind to form a holistic percept (Werthiemer,

1924). The developmental psychologist Jean Piaget (1926) first described this cognitive structure

as a scheme, or a complex grouping of categories related by a common theme, such as a person,

event, or place. According to Piaget, the structure could be altered by either assimilating new

13 The plural form of schema is schemata.

64 information into the scheme or by accommodating memory-bound schemes to fit new

situations. Piaget viewed schemes as actively shaped mental images or patterns of action. Since

Piaget’s early musings on schemes, other scholars have used the term schema to refer to similar

cognitive structures in memory, broadening the field of schema theory (Bartlett, 1932; Rumelhart

& Ortony, 1977). Although Bartlett (1932) explicitly wrote of his distaste for the word (p. 201),

he is often credited with the use of schemata in the process of remembering. He defined a

schema as an “organized setting,” or holistic grouping of events in memory, organized as an

active chronological reordering of past experiences. Since both physiologists and Gestalt

psychologists influenced his view on cognitive processing, Bartlett’s perspective relied on

interconnections between such groupings that were physically represented in the brain:

“…constituents of living, momentary settings belonging to the organism…not…a number of

individual events somehow strung together and stored within the organism” (p. 201). Bartlett’s

definition was built on years of case studies requiring subjects to remember and recall stories

using different methods of memorizing, such as description, repeated exposure, serial

reproduction, and picture writing. In his theory of active memory construction, Bartlett

highlighted the enabling role of social and environmental context via a process of “checking,” or

relating cognitive structures to situational features and persons. These contextual and individual-

difference considerations added a social component to the framework of schemata.

Later additions to schema theory further differentiated schemata from other forms of

representation and processing mechanisms (Norman & Rumelhart, 1975; Anderson, 1977;

Rumelhart & Ortony, 1977). Rumelhart and Ortony (1977) described the structure, orientation,

and organization of schemata as associated networks of concepts which: contain value-related

properties; perform the function of embedding; represent generic concepts varying in level of

65 abstraction; and encompass knowledge instead of definitions (p. 101). Given these

specifications, the authors described the process of perception as analogical, in which the mind

refers to previously formed schemata, and then fills in gaps with stored information. Another

possibility is that the mind forms a new schema based on integrated feature information. This

theory incorporates active reconstruction and knowledge building during schema activation. A

common example in the literature is arriving at a restaurant (Brewer, 1987). If we encounter a

restaurant without menus, the restaurant schema would be modified to include this feature. Or, a

new schema would be devised and labeled as café to deal with any similar circumstances in the

future. In this example, there is a direct relationship between the formed structure in memory and

the way an object is processed. Likewise, Rumelhart and Ortony (1975) challenged the idea of

separate memory and processing mechanisms, coupling the two to form a unified theory with

simultaneous perception and memory-retrieval. They suggested that features activate a schema,

which simultaneously affects the interpretation of those features. In light of this basic process,

schema theory can be incorporated into bottom-up, feature-driven models of perception.

Anderson (1977) echoed these claims, further arguing that comprehension involves much more

than the cataloguing of stimulus features. He also reemphasized the complexity and elaborate

nature of schemata and related the theory to practical learning situations.14

The Impact of Social Group and Culture on Cognitive Behavior

Introduction

What sociocultural and experience-related factors influence the content, structure, and

function of memory? Although not a well-researched topic in cognitive psychology, a handful of 14 It is worth noting that his ideas harkened back to Piaget’s concepts of assimilation and accommodation in learning.

66 scholars have studied the influence of group affiliation on the accessibility of semantic

memory and processing systems. Activation of these systems is influenced not only by individual

differences in motivation for retrieval (e.g. speed and accuracy), but also on contextualized

experience and knowledge. Therefore, increased knowledge in a domain may result in both

perceptual and conceptual processing differences. Two of the field’s foremost scholars on the

topic of expertise, Chase and Simon (1973a, 1973b), conducted perception and memory

experiments with novice and expert chess players. Motivated by chess-player and psychologist

Adrian De Groot’s original thesis on verbalized problem-solving in chess players, Chase and

Simon designed an experiment that tested memory for predetermined chess positions. In their

first study, Chase and Simon asked participants to memorize naturally-occurring as well as and

random chessboard positions. Their results revealed different strategies in processing for the

experts, based on chunking:

the superior performance of stronger players derives from the ability of those players to encode the position into larger perceptual chunks, each consisting of a familiar subconfiguration of pieces (p. 80).

These and other results implied that experts have the ability to consolidate a larger number of

concepts in semantic memory, which increased their density of knowledge. Moreover, this

consolidation facilitated processing time,15 freeing up mental resources to focus on moment-to-

moment changes in expectation. Experts take advantage of the contextual opportunities for

building knowledge structures in chess and use this information in performance situations; thus,

the content becomes reinforced and solidified in memory. Since these classic studies, additional

evidence of richer semantic representations for experts has been observed in other domains such

as text comprehension and medical diagnoses (Voss et al., 1980; Ericsson & Kintsch, 1995).

15 Processing time in the study was measured by observing glancing behavior.

67 Likewise, music educators have demonstrated the efficacy and speed of response from

experienced respondents in association and priming tasks, claiming that “content knowledge

facilitates the rate of retrieval of domain-specific information” (Muir-Broaddus, 1998; p. 119;

Bjorklund et al., 1990). Although these studies were somewhat limited by the younger age of

participants in the samples, their findings are similar to those in other domains, in that they show

cognitive-grouping strategies like chunking. Knowledge-specific differences may also be the

result of social and cultural context, and thus, sociocultural variables may impact the processing

of information. I will now turn to two other broad topics, social groups and culture, to consider

the role of contextual experience on perception and cognition. In addition, some characteristics

of these groups are explored for their potential influences on the structure and function of

knowledge as well as cognitive behavior.

Social Groups and Cognitive Behavior

The impact of social group affiliation on behavior is a widely studied phenomenon,

primarily stemming from the work of sociologists. Early sociological studies on this issue

centered on cases of violence and crime and included observations of gangs, delinquents, and the

homeless, as well as explanations for the organization of crime and theories of community

relationships (Anderson, 1923; Thrasher, 1927; Shaw & McKay, 1942). One of the leading

researchers in the Chicago school of sociology, Frederic Thrasher (1927), suggested that gangs

have natural histories, developed out of handed-down traditions and distinct heritages. In his

analysis, he specified that various characteristics – such as geographic territory, boundaries,

power relations, and patterns of behavior – materialize through socially-defined interactions,

memories, and personal narratives. Additional contributors to the Chicago school focused more

68 on groups’ shared values, interests, and social facilitators, which act in opposition to social

disorganization and delinquency (Shaw & McKay, 1942; Cohen & Short, 1958). Shaw and

McKay (1942) stated that “traditions of delinquency are transmitted through successive

generations of the same zone in the same way language, roles, and attitudes are transmitted” (p.

382). On the other hand, Cohen and Short (1958) hypothesized that delinquent subcultures arise

when alienated members of society, unable to attain social success, fuel their actions with their

frustrations. In Cohen’s and Short’s words, the delinquent subculture was:

…a system of beliefs and values generated in a process of communicative interaction among children similarly circumstanced by virtue of their positions in the social structure, and as constituting a solution to problems of adjustment to which the established culture provided no satisfactory solutions (p. 20).

Their definition concentrated on the characteristic beliefs of a system, rather than on the people

enforcing the system, a common mark of sociological research. Another unique feature was the

lack of emphasis on group structure, organization, and power within the group. Cohen and Short

emphasized demographics, such as age, sex, income bracket, ethnicity, and geographical

placement, as well as a group’s processes of communication and interaction, as staples in the

concept of a social group. Sociological contributions to the study of groups have been criticized

by psychologists as being too broad and focused on general group characteristics, especially

considering the differences between groups and individuals within those groups (Katz & Kahn,

1966). Nevertheless, they have had a significant impact on the concept of a group by focusing on

the importance of overall group features, structure, and traditions.

Early influential research on the impact of groups on behavior primarily targeted the

facilitation of groups. Studies spearheaded by Harvard professor George Elton Mayo at the

Western Electric Hawthorne Works in Chicago sought to explain the impetus for differing levels

69 of workplace productivity. Mayo placed small numbers of workers, or groups, into the same

work conditions, including days with earlier release, breaks, better lighting, and free meals. One

study (1933) concluded that efficiency improved in many of these conditions; however, the

effect was most pronounced when workers identified with each other. In the experiments where

workers actively formed working groups, the productivity was even higher. “The consequence,”

Mayo concluded from interview sessions, “was that they felt themselves to be participating

freely and without afterthought, and were happy in the knowledge that they were working

without coercion from above or limitation from below” (p. 64). In addition, workers felt a sense

of “security and certainty” in the group—a feeling of ‘we’re in this together.’ These and other

studies suggested that affiliation with a group results in increased motivation, productivity,16 and

emotional support, although individual preferences may override this effect (Pritchard et al.,

1988).17

The Gestalt psychologist Kurt Z. Lewin (1936) aimed to understand the influence of

group involvement on child activities, including infant stretch vectors and toddler problem-

solving direction. Over years of observations, he noticed differences between American and

German children on their “space of free movement.” Since American children were provided

with more choices than German children, they had access to a larger region of psychological and

social space. These early theories of space and social independence formed the basis for his later

work on the subject of group dynamics and conflict. It is in these papers that Lewin and his

16 Research has also suggested that working in groups results in performance loss, or a decrease in task effectiveness. This finding may be due to a phenomenon called “social loafing,” in which members of the group have unequal work-efforts and one or two people work the most (Levine et al., 1993; Shepperd, 1993). 17 Musicians experience similar ups and downs in motivation, depending on structure- and preference-related features of the group (Davidson & Good, 2002). On the other hand, there is also the notion of the musician in solitude, who relies on his own devices to motivate and propel his own creativity (Storr, 1993).

70 colleagues described groups as “sociological wholes; the unity of these sociological wholes

can be defined operationally in the same way as a unity of any other dynamic whole…by the

interdependence of its parts” (p. 73, 1939). This holistic concept was undoubtedly related to

Gestalt psychological structure; but, individual and group behavior was said to depend “upon

their situation and their peculiar position in it,” which placed emphasis on the individual’s

affiliation to the group (p. 74, 1939). It is with Lewin’s early studies that the concepts of

individual versus group identity took shape, thus affecting many later studies on group influence.

Unlike Mayo and Lewin, Muzafer Sherif and colleagues observed social processes by

manipulating groups in laboratory settings. Sherif and colleagues (1955) characterized the small

group using the following distinctions:

1. Shared motives, conducive to interaction 2. Differential effects on individual behavior 3. Group structure, with a hierarchy of status and roles, delineated as in-group 4. Set of norms or range of acceptable behavior (p. 371-372)

They expanded previous researchers’ delineations by explicating the role of group

communication and interaction in group formation. Since prior researchers had tended to focus

on two dimensions – structure and norms – that related to power distribution and behavioral

expectations in groups, Sherif and colleagues (1954) incorporated these concerns in a

comprehensive definition:

A group is defined as a social unit which consists of a number of individuals who, at a given time, stand in more or less definite interdependent status and role relationships with one another, and which explicitly or implicitly possesses a set of norms or values regulating the behavior of the individual members, at least in matters of consequence to the group (p. 8).

The authors emphasized the individual and his relation to the group, elaborating upon Lewin’s

earlier claims regarding group identity and affiliation, and hinted at the separation of group

71 versus individual behavior. In addition to setting the stage for ingroup and outgroup

boundaries, the authors outlined a number of dimensions which could be used to observe and

measure behavior in experimental group settings. They placed participants into groups in a

unique set-up, called the “robber’s cave.” Systematic observations took place in Robbers Cave

State Park in Oklahoma, where two groups, each with 12 boys, were placed in isolation.

Activities were structured to provide bonding opportunities; in essence, a controlled induction of

group-identification. The experimenters observed strong ingroup identity within five days, where

the boys adopted names, roles, and status hierarchies. The boys were then informed of

competitive activities in which the winner would receive trophies or other valuable items. Their

results demonstrated increased motivation for those who identified more with the group over the

three weeks. Moreover, the boys proceeded through at least three stages of ingroup processes: 1.

Identification with the group through communication and interaction, 2. Production of conflict

toward out-group, and 3. Reduction of friction. The study’s conclusions underscored the

importance of hierarchical structure and commonly identified goals and attitudes in group

formation. Sherif’s largest contribution was his emphasis on identification and boundary

solidification, which opened doors for new studies of groups. An additional unique aspect of this

study is how it related to real-world, naturally manipulated settings, although it still did not

maintain the ecological validity characterized by earlier sociological studies.

Following Sherif’s studies, group identification studies proliferated in social psychology.

Showing an awareness of the various combinations of demographic variables such as age,

gender, race, geographical location, and socioeconomic status, Henri Tajfel and John Turner

(1979) fleshed out the concept of group identification with social identity theory (SIT). Inspired

by the cultural milieu of discrimination and racism in the 1960s, Tajfel and Turner were

72 concerned with group conformity and influence. They were also the first to distinguish

between internal and external properties of the group; in other words, how individuals perceive

the ingroups, defined as the group(s) with which they identify, and outgroups, group(s) in which

they do not belong, but of which they still remain aware. SIT maintains that there is an

interaction between the view of the self, or self-concept, and social group membership, that

results in classification of the self and others into categories with descriptive characteristics.

Tajfel and Turner explained this with three theoretical propositions, each with its own behavioral

ramifications:

1. Individuals strive to achieve or to maintain positive social identity. 2. Positive social identity is based to a large extent on favorable

comparisons that can be made between the in-group and some relevant out-groups: the in-group must be perceived as positively differentiated or distinct from the relevant out-groups.

3. When social identity is unsatisfactory, individuals will strive either to leave their existing group and join some more positively distinct group and/or to make their existing group more positively distinct (p. 60).

A number of controlled experiments, where groups were defined by shared values, supported

these claims. Turner (1978) asked two groups of undergraduates (Arts and Sciences) to discuss

an issue, and then asked them to assess ingroup and outgroup performance. His results showed

lower and more biased verbal-intelligence ratings from the Arts students, since they self-

identified themselves as valuing verbal intelligence in their field. Additionally, different

comparison conditions (explanation of Arts as more verbally positioned versus no explanation;

and similar versus dissimilar out-group) changed the resultant ratings, such that out-group biased

ratings were not always observed. A later publication by Turner and colleagues (1987) detailed

these stages of the comparison process further, so that the role and prioritization of multiple

“levels of self” were considered. Applications of this research to ecologically-valid situations

73 associated issues of identity and group affiliation to stages of social comparison and changes

in self-esteem.

Culture and Cognition

Anthropologists are best known for studying culture, but the range of views they provide

is enormous. Modern anthropological texts tend to define culture as some combination of the

following: belief systems, ethnicity, technological availability, geographical location, and

worldview. Rather than provide a list of these cultural theories as applied to thoughts and

behavior, I will first review the notion of “culture” from the perspective of traditional

psychology and second from the perspective of cultural psychology. Throughout this discussion,

I will complement these views with examples from perception and cognition studies.

In contrast with anthropologists, psychologists typically gather data from many cultures

to support or oppose some assertion about cognitive or behavioral universals. In psychological

experiments, culture is either assumed to be a fixed category, or measured by belief and

attitudinal scales. In the former paradigm, participants are categorized into groups based on

demographic or status variables, and experimental outcomes are attributed to cultural differences

(Neville & Heppner, 1999). If participants are assigned to a particular status group, they are

rarely queried on their identification and affiliation with the group. A questionnaire may present

the survey item, “ethnicity: African, Caucasian, Asian, or European, please circle one,” and

respondents must choose from these preexisting categories. The alternative classifies respondents

on their identification with a group, set of beliefs, or attitudes, given trends discovered in the data

(Ross, 2004; Kitayama & Cohen, 2007). In such studies, instead of relying on preformed

categories, these results are used to form groups of related individuals. Even though both

74 approaches provide a useful starting point for studying cognitive behavior, the latter view sees

a group as a more dynamic, fluctuating entity.

Studies in cognitive psychology present contrasting evidence, compared to those in

anthropology or sociology, for cultural influences on memory and processing systems (Labarre,

1947; Graham & Argyle, 1975; Moore et al., 2002; Roberson et al., 2000). For instance, Labarre

(1947) detailed culture-specific trends in particular gestures, such as head movements for

indicating ‘yes’ and ‘no’: “The Semang, pygmy Negroes of interior Malaya, thrust the head

sharply forward for ‘yes’ and cast the eyes down for ‘no’” (p. 50). Emotional behavior also

appears to have cultural dependencies. On the topic of explicit emotions, one author found that a

particular African tribe associated “black laughter” with “a mistake of supposing that similar

symbols have identical meanings” (Gorer, 1935, cited in LaBarre, 1947, p. 52). Clearly, laughter

can be used to communicate more than simple amusement. Ekman and Friesen (1969) advanced

the study of culturally-specific emotions by focusing on the question of universal trends in

cultural display rules, instead of gestures. In this and later studies (Ekman, 1972), they observed

American-Japanese cultural differences in display of reactions to films. Japanese respondents

were particularly private in their display of negative emotions, especially in the presence of the

experimenter. Despite this finding, the majority of studies in Ekman’s lab (Ekman & Friesen,

1969, 1971; Izard, 1971; Ekman et al., 1987) have demonstrated universality in categorical

perception of facial expressions, including happy, sad, fearful, disgusted, and angry faces.

Likewise, reactions to films in the experiment’s alone condition were found to be similar across

cultures (Ekman & Friesen, 1969; Ekman, 1972). In contrast, James Russell (1991b) has argued

against the universal quality of emotional meanings. Russell distinguished emotional thoughts by

the way they are communicated by the culture’s lexicography. His ideas stem from the linguistic

75 relativity hypothesis, developed by the work of Edward Sapir and Benjamin Whorf (Sapir,

1929; Whorf, 1956). This theory specified that a culture’s specific lexicon determines systems of

cognitive representation and processing. Regarding the principle of relativity, Whorf stated that

…users of markedly different grammars are pointed by their grammars toward different types of observations and different evaluations of externally similar acts of observation, and hence are not equivalent as observers but must arrive at somewhat different views of the world (1956, p. 221).

This passage claims that local conditions emphasize the interaction between language and

cognitive thought. Russell (1991b) supported this theory and the non-universal quality of

emotion words with English concepts of shame and anxiety. Behavioral evidence for these

patterns in the lexicon span from differences in cultural display rules (like social norms in group

research) to conventional norms of cognitive appraisal. Russell also suggested that these forced-

choice designs have missed the mark with their predetermined, strict category boundaries for

emotions. He argued that “…some emotion categories in non-Indo-European languages differ

enough from their assumed translation equivalent in English to influence the categorization of

facial expressions” (p. 436). Here and elsewhere Russell (1993, 1994) has argued that

participants may respond more realistically to free-response or open-ended designs. Such

methodological arguments may have the power to shape later interpretations of these matters.

Contrasting results of cultural influence have also been observed in the cognitive

processing of more objective stimuli, such as colors, ethnic boundaries, and family roles.

Regardless of lexicography, it is widely accepted that color spaces are labeled similarly between

cultures (Berlin & Kay, 1969; Moore et al., 2002). Moore and colleagues (2002) asked

Taiwanese and American respondents to judge focal colors in paired comparison tasks. The

authors focused on the semantic structure of color representation, which they defined as “a

76 cognitive representation in which the meaning of terms…relative to each other is represented

in Euclidean space” (p. 7). This method of analysis attempts to deal with both between- and

within-culture differences. The study’s results demonstrated similar knowledge of colors

between cultures, with a slight amount (1.5%) of the variance due to lexicon differences.

Although the authors differentiated these cultures on the basis of their color lexicons and

cognitive access to color terminology, most of the variation in the data could be explained by

interactions between gender, task, and language. The authors stressed the importance of

individual difference and complexity in cross-cultural studies of this nature.

As opposed to color studies, evidence for culture-specific concepts in ethnicity and word

meaning suggests a sizable effect of lexicography. Gil-White (2001) contended that cultures

have distinct approaches for defining ethnic boundaries, based on descriptions of appearance,

essence, biological ancestry, and enculturation. Studies of abstract concepts also exemplifies

cross-cultural differences in category boundaries. Wober (1974) surveyed two African cultures,

the Baganda and Bataro, on their conceptual understanding of intelligence (obugezi) in their

native languages. These participants rated each concept on bipolar semantic differential scales.

Controlling for translation effects, Wober found that the Baganda linked intelligence to “mental

order,” while Batoron respondents associated it with “mental turmoil.” Ratings on three bipolar

scales (happy/sad, rare/common, and unyielding/obdurate) differed significantly between groups.

Wober suggested that enculturation and access to knowledge were the most influential factors in

these results and attributed them to societal norms of prestige and resource proximity. In an

article on familial roles, Sharifian (2003) proposed that shared conceptualizations arise from the

interaction between members in a culture or social group. Moreover, since not all individuals

77 within a culture submit to the same meanings, uniformity interacts with resultant coherence of

cultural belief systems.

A body of recent research supports the notion of distributed agreement within culture

groups. In Culture and Resource Conflict, Medin, Ross, and Cox (2006) explored the basis of

conflict and misperception between Menominee and majority cultures in Wisconsin. They

described culture as “causally distributed patterns of ideas, their public expressions, and the

resultant practices and behaviors in given ecological contexts” (p. 28). Their previously

developed cultural-consensus model (CCM) offered a statistical measure of shared knowledge

and assumed that “widely shared information is reflected by a high level of agreement across

individuals” (p.29). Romney and collaborators (1986) used the CCM to compare task results to

assess response distribution within a culture.18 Medin and colleagues (2006) claimed that

cognitive processing informs knowledge organization, as well as attitudes and belief systems

regarding meaningful stimuli. In a related experiment, Medin and collaborators (2006) asked

Menominee and majority cultures to perform several sorting and timed tasks involving

judgments on a variety of fish. In one experiment, the Menominee group sorted fish on the basis

of ecological distinctions (e.g. habitat), whereas the majority group focused on taxonomic and

goal-related characteristics (e.g. desirability and adult size). Overall, the study’s results exhibited

a shared model for the fish, but peculiarities arose in the functionality of the model. In other

words, the cultures organized their knowledge quite differently.

In their study of animals and kinship terms, Romney and Moore (1998) devised a

quantitative model of culture as a set of shared cognitive structures based on internal knowledge

representations. According to their theory, members of a culture “share similar cognitive

18 Another term for “within culture” phenomena is intra-cultural.

78 structures for common semantic domains, even abstract ones like kinship terms” (p. 332).

Cognitive structures are based on cross-cultural similarity judgments between terms, such as

grandmother and grandfather in the abstract kinship domain, or elephant and giraffe in the

concrete animal domain. Romney and colleagues observed a distinct difference in similarity

judgments between monolingual versus bilingual English speakers: the latter had higher

variability within participants than the former group. The authors interpreted these structures as

culturally shared knowledge structures, highly dependent on linguistic, social, and contextually

defined meaning systems. Furthermore, they associated their model to Searle’s (1995) idea of a

social reality, in which cultural concepts (e.g. money) are defined by institutionally bound

systems of meaning construction.

Looking at sociocultural affiliations from several viewpoints is a valuable step in

understanding the relationship between culture and cognition. From the view of traditional

psychology, we may be on our way to uncovering universal, innate capacities of the human

mind. From cultural psychology, we may be able to see culture not as a solidified entity, but as a

unique pattern of belief systems, attitudes, and behaviors, unequally distributed across a network

of individuals.

Cognitive Representations and Processing of Music

Introduction

As the psychological studies referenced above suggest, any person’s representation and

processing of stimuli depends on relevant perceptual features and interpretive knowledge

structures, which are in turn influenced by accumulated experience and cultural affiliation. Many

studies that integrate psychological and musicological approaches presume that representation

79 and processing are influenced by certain absolute properties of music, such as pitch, harmony,

rhythm, and meter19 (Lerdahl & Jackendoff, 1983; Dowling & Harwood, 1986; Krumhansl,

1990; Desain, 1992; Huron, 2006). This tendency seems to be influenced by the traditional music

theoretic view that underlying structural properties of music, most typically pitch and harmony,

account for cognitive experiences (Salzer, 1952; Schenker, 1954; Meyer, 1956).20 However, as

Thompson and colleagues (2008) recently suggested from a study on audio-visual integration,

multiple features of music are integrated to form an understanding of the work; thus, it is

difficult to distinctly parse out the direct influences of perception. Moreover, the integration of

features on one level mirrors the integration of larger unified systems of meaning, such as those

explored by Meyer (1956), Clarke (2005), and Koelsch (2004) described in Chapter 1 of this

dissertation. As suggested by Clarke, the representation and processing of music requires

multiple levels, similar to those noted previously with regard to models of semantic memory. Not

only do these models specify multiple units of embedded structures, but also, every level is

connected to every other via network-like webs of interaction. The hierarchical levels may be

situated vertically, to include subordinate, superordinate, and modifier interactions, or

horizontally, such that different types of processing are on the same level. Although this study

concentrates primarily on music’s referential (associative) meaning, influential models of music

processing will be summarized as systematic templates for the development of a modified

interactive system. Specifically, multiple levels of physiological explanations, Gestalt

19 Of course, these are not mutually exclusive, nor do they represent the entire gamut of properties in a musical work. Additional properties such as “loudness, duration, and timbre” may be included in this list (Dowling & Harwood, 1986; p. 19). 20 One of the most problematic assumptions of these models is an overdependence on accumulated knowledge of these representations (Narmour, 1977). Although these models focus more on the underlying “deep” structure of musical systems, they are directly related to Meyer’s absolutist perspective, such that meaning lies in moment-to-moment musical relationships.

80 psychological theories, and schema-driven mechanisms will be discussed.21 Following these

summaries, several theories and studies on referential musical meaning will be reviewed to

illustrate the connection between concepts22 and music. Finally, studies on the influence of

culture and accumulated knowledge will be included and followed by a discussion on the

relevance of previous research to this study.

Models of Music Representation and Processing

Early models of representation of music focused on the relationship between physical

patterns in sound, such as frequency, amplitude, and spectra, and top-down, sensory processing

mechanisms in the auditory system (Helmholtz, 1877; Schouten, 1938; Békésy, 1960; Plomp &

Levelt, 1965). This reflected the hierarchical character of processing models, where sensation is

often presumed to be the first level of perceptual experience. Pitch consonance and dissonance,

or perceptual “roughness,” was explained by the interactions between sound waves and the

resultant activations within the basilar membrane (Helmholtz 1877; Francès, 1958). Specifically,

Helmholtz posited a causal relation between anatomical structures in the ear and the salience of

certain tones. He concluded that implicit sensations affect the perception of beauty in music:

No doubt is now entertained that beauty is subject to laws and rules dependent on the nature of human intelligence. The difficulty consists in the fact that these laws and rules, on whose fulfillment beauty depends and by which it must be judged, are not consciously present to the mind, either of the artist who creates the work, or the observer who contemplates it (1877, p. 366).

21 The format of this section is slightly different than the former, such that studies on mental representations and cognitive processing will be considered simultaneously. This is generally the case for research in the field of music cognition. 22 The use of this term (concept) will be used interchangeably with the term category, unless noted otherwise.

81 Both perception and the development of musical styles were thought to be influenced by

bottom-up interactions between physical sound patterns; however, extensions of Helmholtz’s

theory presumed that these perceptual mechanisms were built-in capacities, and thus, top-down

in nature (Plomp & Levelt, 1965). Likewise, studies relating physical amplitude and

psychological loudness showed that listeners form a reference point, to which all other

experiences are compared (Stevens and Davis, 1936). “Just noticeable differences” (JND), or

thresholds of subjective distance, were determined and shown to be dependent on particular

frequency ranges (e.g. higher pitches were judged louder than lower pitches at the same

amplitude). These judgments were, however, found later to be affected by additional factors,

such as frequency interactions and masking effects, further confirming the assertion that the

integration of complex properties, on one level, affects perception and judgment of sound, on

another (Zwicker et al., 1957; Dowling & Harwood, 1986). More recent research argues that

these and other perception-based models more accurately predict goodness judgments and

expectations of listeners (Povel & Jansen, 2001).

In the latter part of the twentieth century, research turned again toward the Gestalt notion

of cognitive grouping mechanisms (Deutsch, 1975). Instead of looking to anatomical structures,

these researchers focused more on the signal and its relation to cognitive frameworks of

perception. Gestalt theory had proposed laws that affected the cognitive ability to parse stimuli

into highly related entities (Wertheimer, 1924). Dependent on physical properties of space and

time, these laws were later borrowed to illustrate perception of pitch information (Miller &

Heise, 1950; Bregman & Campbell, 1971; Dowling, 1973; Deutsch, 1975). Researchers set up

studies to test the integration of the two signals. Deustch (1975) asked participants to describe

what they heard when two contrasting, angular melodies were played simultaneously in the right

82 and left ears. Her results showed that participants heard parts of ascending and descending

major scales, instead of angular melodies, which she attributed to relations of pitch proximity.

Deutsch named this phenomenon the scale illusion and illustrated the cognitive tendency of

grouping pitches based on relative closeness in frequency. Similar perceptual illusions have been

illustrated in the perception of rhythmic and metric groupings, even in spite of interruptions in

the signal (Norman, 1967; van Noorden, 1975).

Other models emphasized the role of implicit knowledge of musical structure and

organization in the experience of music. The music theorist Fred Lerdahl and the linguist Ray

Jackendoff (1983) aimed to represent the relationship between the musical score and the

knowledge that listeners bring to a musical experience. They attributed this to a listener’s

intuitive mechanisms of grouping, structuring, and reduction of features. Based on concepts from

Gestalt psychology and transformational grammar, Lerdahl and Jackendoff devised a set of rules

based on feature grouping and metrical structure, as well as time-span and prolongational

reduction, and applied these rules to pieces from the Western classical music canon (e.g.

Schubert, Mozart, Bach, and Beethoven). The authors theorized that listeners hear hierarchical

groupings, structures, and prolongations because of patterns of tension and release, which arise

out of contextual consonance and dissonance. With certain formalized principles of voice-

leading and tonality as their foundation, Lerdahl and Jackendoff provided “prolongational trees”

based on implied patterns of tension and relaxation of pitch and rhythmic events. For example,

their prolongation reduction well-formedness rules (PRWFR) seek to illustrate the degree to

which the interaction between pitch and rhythm create long-range musical structures. Their

model was framed within traditional music theory and signified a hierarchical, feature-dependent

structural component, embedded within higher-level semantic representations of music.

83 Studies proposing empirical testing of Lerdahl and Jackendoff’s theory surfaced not

long after their work was published. Several of these studies focused on rules of segmentation

and parsing of relevant musical stimuli, but suggested feature-interaction rules that differed

slightly from those proposed by Lerdahl and Jackendoff (Deliège, 1987, 1989; Clarke &

Krumhansl, 1990; Krumhansl & Jusczyk, 1990). Deliège (1987) had participants listen to short

excerpts of instrumental music from the Western canon and asked them to illustrate section

boundaries by drawing lines between dots that represented musical events. Generally, she found

that musicians’ segmentations adhered more to “rules” than did nonmusicians and specific rules,

such as attack-point, change in dynamics, and change in timbre, were favored by both groups.

Later experiments explored rules further by manipulating the stimuli, revealing more complex

interaction between rules, and suggested additional rules of change in instrumentation and/or

sound density. A study on real-time listening (Deliège et al., 1996) solidified a theory based on

schematic cues, accessed in the process of relating musical surface structures. The authors

argued that

…the materials of one and the same piece appear to give rise to different “schematas of order” that are largely dependent on listeners’ previous musical training…cues memorized by musicians contain longer musical structures enabling the musicians more efficiency in establishing relations between musical structures during listening (p. 155).

Essentially, the saliency of surface cues allows listeners to activate larger structures, or schemata,

and form abstractions based on the information in the memory traces, or “imprints” (Deliège

1989, 1991, 1992). Unlike the theory set forth by Lerdahl and Jackendoff, this theory is most like

that shown in theories of categorization of local, rather than global features. In support of the

influence of local features, Cuddy and Badertscher (1987) found that the presence of a major

84 triad tended to influence a sense of tonality, or key. Instead of focusing on hierarchical stages

and units, this approach relies more on bottom-up, feature extraction tendencies in perception.

The psychologist Carol Krumhansl showed a similarly Gestalt-influenced approach to

modeling musical experience, by designing a technique to test the stability of tonal schemata as

applied to the contextual appropriateness of certain pitches (Krumhansl, 1990). Dubbed the

“probe tone paradigm,” this methodology first presented a tonal context, then an isolated tone,

and then asked respondents to rate the overall “fit” for the tone. Her results illustrated a

hierarchical model of pitch, suggesting, for example that certain pitches in the C major scale (e.g.

C, G, E) were judged more likely to occur in the major context than did non-scale tones (e.g. C#,

D#, F#, G#). She argued that non-scale tones were not part of the organized memory trace for C

major; and more generally, “…the quality of individual elements is determined, and sometimes

distorted, by the organizational processes operating on the configuration,” which contribute to

the contextual identity of a particular tone (p. 143). Krumhansl’s model, like that proposed by

Deliège, specifies feature extraction processing systems, such that multiple features, based on

contextual identity and psychological pitch distance, are combined additively to influence an

overall impression.

Schemata for musical events have also been found to influence the structure and function

of mental representations and the resultant processing of musical stimuli. As previously

mentioned, Deliège incorporated the abstraction of features into rule-based schemata accessed

during musical segmentation. In a more recent text on creativity, Deliège (2006) commented on

the structure and organization of these categories23 in music, directly relating them to principles

of knowledge organization explored by Eleanor Rosch (1975, 1978). The following passage 23 In this paper, Deliège refers to “categories” as representations and “schemata” as involved in processing.

85 illustrates Deliège’s thoughts on the differences between horizontality, or concepts that are on

the same conceptual level, versus verticality, or concepts that embed each other:

the concept of horizontality could apply immediately to music listening. But for verticality, some adjustment is required. You cannot simply transfer to music the hierarchical principles that come from language and refer to precise concepts and semantic contents. But by analogy, one could say the following:

(1) The reference to a basic level could cover the abstraction of the different cues within a single piece. Each cue generates its own horizontal relations. It has its own specific function and creates its own auditory image, independently from all the others while sharing with them a common reference: the style of the piece.

(2) The superordinate level can be assigned to the reference of

each cue to a group or section, within the overall mental representation of the work.

(3) The subordinate level refers to relations between the patterns

that share analogies within the auditory image, and this leads back to the concept of horizontality (p. 72).

She then likened Rosch’s prototype to her notion of an imprint, which she defined as the best

representation of the category, or a “prototypical summary that facilitates the recognition of

musical patterns” (p. 73). Deliège did not consider extensions of the hierarchy in the opposite

direction; that which subsumes the superordinate level could include the overall concept of the

piece represented in the listener’s terms, communicable to outsiders via a set of abstract terms.

She argued against the presence of ostensible referents and semantic content in music, since it is

the role of the listener to form these abstractions. This will be discussed in more detail in the

next section.

Other studies focus on the role of distinct musical features in the formation of schemata.

Dowling (1978) found that listeners process melodies primarily by their contour, or patterns of

86 ups and downs, such that scrambling of contour and range information made melodies

indeterminable. In a later publication, Dowling and Harwood (1986) suggested that “both the

label and the contour can serve to retrieve a particular melodic schema from among the many

stored in long-term memory” (p. 129). Regarding schemata in jazz, Williams (1988) applied

Meyer’s theory of “archetypal schemata” (1973), or exceptionally representative cases, to bebop

themes. According to Williams,

Archetypal schemata, then, are the normative classes that serve, collectively, as a conceptual frame of reference for the perception and comprehension of melodic events. In the act of listening, one constantly but unconsciously compares what is heard to one’s conception of what melodies generally do. It is the mental results of such comparison that provide the basis for critical evaluations of originality and esthetic value (p. 55). Referencing Meyer’s famed “gap-fill” schema and Narmour’s (1974) elaborations, Williams

presented common octave-leap and axial patterns in jazz themes,24 implying that they play a

significant role in bebop improvisations and compositions. These comments from Dowling and

Williams support an integrated view of musical schemata; recognition of familiar melodies and

archetypes depends not only on contour information, but also on chunks of categorized

information that may depend on interactions between musical features.

Referential and Associative Representations of Music

Referential meanings of music are largely explored by theorists and musicologists, with

the exception of a few cognitive studies relating music to emotional qualities (Tovey, 1935;

McClary, 1991). The main thesis from a theorist’s point of view is that composers refer to extra-

24 Colloquially, jazz musicians refer to these as “heads.”

87 musical ideas in the work itself.25 The Sinatra-Basie recording of Fly Me to the Moon was

mentioned at the outset of this dissertation to support this view. In the world of classical music,

Richard Strauss was a composer who utilized typical references, by relating musical patterns,

timbres, and instrumentation to specific images and emotions. Of his early operas and unfinished

works, Schmid (2003) noted that Strauss presented a variety of emotional ideas and concepts,

albeit more abstract than those presented earlier in works by Richard Wagner. Similar claims

have been made for the presentation of unified, structural narratives in Brahms’ symphonies

(Knapp, 2001). Due to its culturally significant recording history, jazz has also been the subject

of referential analysis. The ethnomusicologist Ingrid Monson (1994, 1996) submitted a view of

jazz “as a mode of social action that musicians selectively employ in the process of

communicating” (1994, p. 285). In her analyses of recordings by Coltrane, Roland Kirk, and Jaki

Byard, Monson (1994) associated elements of music with a typology of concepts relating to

social action. Of Coltrane’s My Favorite Things, she argued:

25 Duly noted is the vehement opposition to this claim in mid-century aesthetics of music, including the work of Eduard Hanslick (1957), who stated,

What part of the feelings, then, can music represent, if not the subject involved in them? Only their dynamic properties. It may reproduce the motion accompanying psychical action according to its momentum: speed, slowness, strength, weakness, increasing and decreasing intensity. But motion is only one of the concomitants of feeling, not the feeling itself. It is a popular fallacy to suppose that the descriptive power of music is sufficiently qualified by saying that, although incapable of representing the subject of a feeling, it may represent the feeling itself—not the object of love, but the feeling of love. In reality, however, music can do neither. It cannot reproduce the feeling of love but only the element of motion….This is the element which music has in common with our emotions and which, with creative power, it contrives to exhibit in an endless variety of forms and contrasts (pp. 24-25).

Susanne Langer (1954) wrote of similar dynamic properties with her concept of “morphology of feeling.”

88 Coltrane…demonstrates the power of his musical intelligence and imagination…to transform a European-American musical theater song into a vehicle for expressing the improvisational aesthetic of jazz (p. 293).

Monson also interpreted this recording as a commercial attempt to appeal to a more diverse

audience. Essentially, she associated abstract concepts, on one level, with lower-level surface

features, such as modification of meter, presentation of static harmony,26 and reharmonization of

standard repertoire, on another. Similar descriptive methods have been used to approach

associations of irony, humor, and playfulness in Thelonious Monk’s music (Solis, 2009). In sum,

these theories are hierarchically situated, as they presume that there are chunks of musical

features embedded in multiple layers of referential meaning.

As an alternative to associating ideas and concepts to music, Deryck Cooke (1959)

attributed the pairing of emotional qualia and music to three distinct processes: “direct imitation,

approximate imitation, and suggestion or symbolization” (p. 3). He presented evidence to

support the emotionality of musical intervals and motifs, including those that communicate

pleasure, happiness (major third), sadness, and pain (minor third).27 With regard to isolated pitch

relations, listeners have shown similarities in their adjective descriptions of two-note intervals

(Edmonds & Smith, 1923). These authors suggested that this finding depended on “auditory

categories” developed through experience. They observed that listeners referred to taste and

touch sensations, such as smooth, dilute, gritty, and harsh, to describe musical intervals, or

auditory categories. Huron (2006) conducted a similar experiment, in which similarities in

“qualia” of musical chords were observed. Like those set forth by Edmonds and Smith, Huron

devised four categories – expectedness (surprising, sudden), tendency (leaning, urging), valence 26 In this case, a “vamp.” 27 This may be more of a Eurocentric view of emotion and music, as there are many examples of happy songs in minor keys in the traditional Eastern European canon.

89 (happy, somber), and other (whole, fuzzy) – to explain adjective-descriptor responses to

chromatic-mediant chords. Huron’s classification implied that judgments were based on the

statistical compilation of musical properties that tended to be associated with typical emotional

experiences. In several experiments, Isabelle Peretz and colleagues (1998a; 1999; 2001) have

also paired emotional terminology with musical stimuli, and proposed that listeners consistently

required only one quarter of a second of an excerpt to categorize music as “happy” or “sad”

(1998a). In further case studies of neurologically impaired patients, Peretz and collaborators

(1998a; 1998b; 2001) showed that neural correlates of these emotional processing units depend

on distributed, as opposed to localized, cortical interactions.

Despite these theoretical and empirical advances, only a few scholars have attempted to

incorporate referents into a comprehensive model of musical meaning (Coker, 1972; Zbikowski,

2002; Burkholder, 2007). The music theorist Peter Burkholder (2007) constructed an analytical

framework for interpreting associative meaning in music, with the following objectives:

…the listener’s sense of what the music means is created through a process of five steps: 1. Recognizing familiar elements. 2. Recalling other music or schema that make use of those elements. 3. Perceiving the associations that follow from the primary associations. 4. Noticing what is new and how familiar elements are changed. 5. Interpreting what all this means (p. 79, emphasis his).

He humbly acknowledged these stages as highly personal processes that result in varied

meanings between listeners, but contrastingly, he contributed analyses for musical innuendos

with “associations…beyond dispute” (p. 81). His examples were described by their means of

association and included “arbitrary encoding,” “performance,” “quotation,” “stylistic allusion,”

“topic and timbre,” “allusion to a specific piece,” “interaction with generic and formal

90 conventions,” and “reference to musical syntax” (pp. 81-97). In one example, Burkholder

applied his model to the presentation of bugle calls in Aaron Copland’s Fanfare for the Common

Man and implied that the timbre of the trumpet coupled with the well-known topic of fanfare

creates the potential for associating the work with concepts of humanity, dignity, and nobility.

Caveats were included to exemplify the role of knowledge of these referents, and with Copland’s

work in general: “meaning depends on what the listener knows” (p. 101, emphasis his), and

further, “Music acquires associations, and thus meanings, through use” (p. 102, emphasis his).

Although this model is underdeveloped due to its simplicity and reliance on personal experience,

it attempted some systematic views on the associative nature of music.

The music theorist Lawrence Zbikowski approached the modeling of music’s relational

structure somewhat differently. Although his work is generally more concerned with musical

features, in Conceptualizing Music (2002), he explained his philosophy in terms of cognitive

notions of typicality and categorization (Rosch & Mervis, 1978; Barsalou, 1992). His proposed

theory specified that the act of categorization creates a musical concept, and further, conceptual

models are composed of hierarchically nested concepts, such as the association between “pitch-

events and objects” (p. 102). The listener goes through a process of “conceptual blending” to

solidify these associations, which Zbikowski defined as “…a dynamic process of meaning

construction that involves small, interconnected conceptual packets called mental spaces, which

temporarily recruit structure from conceptual domains in response to local conditions” (p. 94).

After these processes unfold, the listener extends his or her conceptual domain and generates

theories, based upon associations within the domain, to solve problems. Further into the text, he

applied these constructs to Mozart’s compositional strategies, apparent in Musikalisches

Würfelspiel, the Musical Dice Game, and musical patterns in String Quartet K. 465. Zbikowski

91 described one category in the first movement, which contained relations between a “rhythmic

pattern…,diatonic contour…, and an implied harmonic change” (p. 155). Additional musical

patterns contribute to the meaning of the piece and to an abstract concept of Mozart’s music in

general – “…something to be introduced, varied, and ultimately reprised” (p. 168) – which he

then compared to the compositional strategies of the style and time period. Although he did not

incorporate it formally, Zbikowski hinted at this higher level of description. Multiple layers of

meaning are implied in both models, mirroring the network-like structure and organization of the

music-centered models of processing. Both Burkholder and Zbikowski propose that listeners

should have a developed sense of these associations, built up from learned and shared

experiences in sociocultural circles, to ensure reliable accessibility and retrievability during the

listening process.

Social Groups, Culture, and Music

Introduction

Music, like other domains, is characterized by an interactive relationship between its

numerous experiential, social, and cultural variables. Meyer, commenting on the role of these

distinctions on the experience of art (1989), wrote:

There is no such thing as understanding a work of art in its own terms. Indeed, the very notion of work of art is cultural. The choices made by some compositional community can be understood and explained only if relationships can be discerned among the goals set by culture, the nature of human cognitive processes, and the alternatives available given some set of stylistic constraints (p. 351).

Meyer was thus concerned with the strategies composers use to develop common practices, and

further, how enculturated listeners build up knowledge structures to interact with musical stimuli.

92 Since knowledge of a style presumes knowledge of “what might come next” in the structure of

a musical work, his theories imply that this expertise may be defined beyond culture, to

experience and learnedness (p. 24). Anthropologists have explored music as a culture-bound

phenomenon and related it to behaviorisms and structural compositions of communities (Nettl,

1956; Merriam, 1964; Seeger, 1987). Some of these earlier studies were specifically targeting

“primitive” practices of music in hopes of expanding the distinction between musicians and

nonmusicians (Blacking, 1974). More recently, social psychologists have argued that groups and

institutions significantly influence responses to music (Hargreaves & North, 1997; MacDonald et

al., 2002; DeNora, 2003). Moreover, recent research in music cognition has incorporated the

study of cultural differences in perception and cognition (Castellano et al., 1984; Walker, 1987,

1997, 2004; Kippen, 1987; Moisala, 1995; Meyer et al., 1998; Balkwill & Thompson, 1999; Bar-

Yosef, 2007; Curtis & Bharucha, 2009). The following summarizes some current trends on the

relationship between sociocultural variables, musical experience, and cognition. Social groups

will be considered as entities that impact preferences, stereotypes, and functionality, while

cultural affiliations will be related to music cognition and perception.

Social Influences on Musical Experience

Recent research in the area of the social psychology of music has seen an upsurge of

focus on the topics of group influence, identity, misperceptions, and stereotypes. This interest

stems from a desire to understand the ways in which different groups use music as social

boundaries. As social groups display characteristic patterns of musical taste, delineations

between groups become clearer. The sociologist Simon Frith (1989) focused on shared values

and experience: “To be a rock fan is not just to like something but also to know something, to

93 share a secret with one’s fellow fans…” (p. 4-5). Contrastingly, Ian Cross (2001)

contemplated social qualities as an inherent feature of music:

The polysemic potential that characterises proto-musical activity is likely to underpin the social functionality of music and to contribute to, but not determine, music’s meaning. The functionalities and functions of music or proto-musical behaviors for the individual, whether in their own cognitive development or in their socialisation, must be set in the context of the functionalities and functions of music as a cultural phenomenon. Music, like language, cannot be wholly private; it is a property of communities, not individuals (p. 9).

Cross argued against the notion that music is a wholly individual experience and advocated for a

sociocultural means of musical analysis. In light of these and other comments, different scholars

interpret music as a way of “bringing people together,” and creating a sense of shared identity

and group solidarity (Bakagiannis & Tarrant, 2006; MacDonald et al, 2002).

The majority of research on music’s use in social groups concentrates on musical

preferences, and the sampled populations tend to be adolescents and young adults (Inglefield,

1968; Frith, 1981; North & Hargreaves, 1999, 2003; Bakagiannis & Tarrant, 2006). Phillip

Russell (1997) proposed that the musical preferences of young people “act as a framework for a

set of socially shared meanings and common states of awareness through which individuals

identify with others in their peer group” (p.152). Tarrant and colleagues have conducted a

number of studies on adolescent groups and music preferences, and one of their earlier studies

(2001) illustrated their participants’ motivations for listening to music. A factor analysis

performed on their questionnaire data suggested that the majority of adolescents’ responses could

be explained by 3 factors: self-actualizing, fulfilling emotional needs, and fulfilling social needs.

A later study by Bakagiannis and Tarrant (2006) sought to illustrate how these factors were

revealed in manipulated group settings. In this experiment, adolescent participants were led to

94 believe they were placed in different groups based on the way they “think;” but in fact, group

placement was random. They were then told that members of their group possessed either similar

or different music preferences. Participants in the former condition identified more with the in-

group and less with the out-group, and when asked to rate both groups on trait adjectives (i.e.

nice, intelligent, selfish, snobbish), they used more positive terms to describe the in-group. These

results highlighted the interaction between musical preferences and intergroup biases.

Other scholars’ work relates closely to these findings. Frith (1981) has commented on the

function of music as a “badge” for adolescents, providing “a means of identifying and

articulating emotion” (p. 217). North and Hargreaves (1999) explored this notion through a

questionnaire, in which they asked adolescents of different ages to indicate their involvement

with, and attitudes on, music. Their results indicated that adolescents listened to music between 3

to 4 hours a day and used it to express their attitudes and values in life. When asked to associate

fans of particular genres (“chart pop,” “jazz,” “classical”) with attitudinal statements, such as

“physical attractiveness is important to them” and “see technology as a good thing,” North and

Hargreaves’ participants exhibited a number of stereotypes and biases. For example, they

associated “classical music” with older age, “chart pop” with females, and “indie pop” with

political activism. Although there were other significant factors in this study, both age groups

(10-11 and 18-19 years) revealed similar patterns of associations, illustrating longitudinal effects

of these biases. Their responses to value-laden statements were also influenced by their music

preferences. On average, adolescents associated classical music preference and older age with

statements such as “This person is more likely than others to be successful in later life,” and

“This person might find it more difficult than others to get a date with a member of the opposite

sex.” These results, as a whole, speak to the relationship between the participants’ perceived

95 music preferences and these preferences’ social consequences (North & Hargreaves, 1999).

The results also suggest that adolescents subscribed to numerous stereotypes and misperceptions

regarding musical preference.

The presence of others has also been examined as an influence on musical behavior and

perceived status. Howard Inglefield (1968) paved the way for research on group presence and

music preference in his dissertation. His additional study investigated conformity behavior “as a

factor in the formation and fluctuation of adolescent musical preferences” (1972, p.57).

Inglefield asked about pretest music preference in a population of 9th grade adolescents and then

assessed his participants’ personalities with inventories that specified inner-otherdirectedness,

need for social approval, and independence. His participants were then asked to rate pieces of

music on preference, both alone and in the presence of social leaders28 in their school. His

participants’ conformity scores, or the response change between pretest and posttest music

preferences, reflected an influence of social leaders on overall preferences. His results also

indicated significant differences in the amount of conformity behavior between musical genres:

“…participants conformed most when responding to jazz music, next to folk music, thirdly to

rock music, and least to classical music” (p. 65). Inglefield interpreted his findings as lending

support for the strength and stability of musical preference among his participants. For instance,

he claimed that adolescents’ preferences for classical music were more stable than those for jazz,

because “most classical music responses were well-established negative responses and not likely

to change under peer group pressure” (p. 65). In another study, Finnäs (1989) set up a similar

experimental paradigm, requiring adolescents to submit their music preferences either privately

(on their own) or publicly (by holding up a piece of paper in front of their classmates). He asked

28 These leadership labels were based on students’ responses to the personality inventories.

96 students to rate musical excerpts, representative of rock, traditional, classical, and folk, on

their degree of preference alone and in relation to other excerpts. They were then asked to

estimate their classmates’ general music preferences. His results revealed a significant effect of

public influence on personal submissions and estimations of preference: adolescents tended to

give lower preference ratings to folk and classical music in the presence of their peer group than

in private. His findings also showed a tendency on the part of his participants to misperceive

preferences of their in-groups; specifically, his respondents underestimated their peers’

preference for particular folk and classical music excerpts. Overall, the results of this study imply

that adolescents are significantly influenced by their perceptions of both in- and out-group, and

by extension, that social context broadly affects estimations of others’ preferences.

Research underscoring the importance of SIT (Social Identity Theory) contends that

group identification shapes perception of both in-group and out-group music preferences. In a

study conducted in an all-male school, Tarrant and colleagues (2001) asked adolescents to rate

students both inside (in-group) and outside (out-group) of their school on personality

characteristics and likeability. Respondents favored the in-group more than the out-group by

associating positively stereotyped music (e.g. pop) with the in-group and negatively stereotyped

music with the out-group (e.g. classical, jazz). In addition, the authors found that the lower a

student scored on a self-esteem inventory, the more he differentiated between the groups. These

results imply that lower self-esteem promotes stronger delineations of group boundaries.

Although generalizing these findings to a population of adults is cautionary, this study also

supports the notion of similar in-group music preferences as well as attitudes towards the out-

group. Gender also been shown to influence the delineation of group boundaries and thus

97 differences in musical experience. Toney and Weaver (1994) conducted a study that explored

the role of gender on the socialization of affect:

…it is argued that gender-specific rules of social conduct - which result from the fact that most young men and women in our society are socialized according to “traditional” cultural gender roles-include gender-specific proscriptions concerning the expression and exhibition of affect (p.568).

However, adherence to these roles depends on “gender schematicity,” or how well one’s self-

perceptions match those of salient gender-role norms. In their experiment, Toney and Weaver

found that participants’ ratings of music videos on scales measuring enjoyment and disturbance

did not depend on gender schematicity. Females showed an overall negative relationship

between enjoyment and disturbance, while males showed the opposite. It may have been that

these gender roles were so ingrained that respondents were not aware of the influence on their

responses. The participants also misperceived out-group musical preferences: males

overestimated female preference for soft rock, and females overestimated male enjoyment of

hard rock. Regarding misperceptions, however, gender only accounted for 1/5 of the variance,

implying that additional variables and complex interactions between them create these effects.

Other studies have looked at the impact of stereotypes and misperceptions on music

evaluation, performance, and musical-instrument associations. In a study assessing the effect of

judgment biases on evaluation of music performances, college students were instructed to

evaluate several video performances of Western piano music (Davidson & Edgar, 2003). The

authors manipulated the materials so that in a “dubbed condition,” the visual mismatched the

aural information. Results illustrated significant in-group effects, related to demographic

variables. For instance, in the dubbed condition, Caucasian judges rated Caucasian performers

higher than African-American performers. This study’s results also implied that judges,

98 regardless of gender, tend to rate female pianists higher than male pianists. In a paper

exploring the effects of gender bias on music evaluations, North and collaborators (2003) asked

adolescents to evaluate pieces as more likely composed by a female or a male. Their participants

also rated the pieces using characteristic adjectives, including forceful, individualistic,

innovative, soothing, warm, and expressive. Males were perceived as more likely to compose in

certain genres, notably jazz, more than new age or classical. Furthermore, males gave lower

ratings of artistic merit to music composed by females, and females gave lower ratings of artistic

merit to music composed by males. The results of this study stand in opposition Davidson and

Edgar’s, where there was a higher overall evaluation of female pianists. The interaction of

demographic variables, such as age and gender, with group context provides a complex set of

concerns, not addressed in either of these studies. Bruce and Kemp (1993) explored the nature of

gender biases in music by focusing on association for musical instruments. After attending two

concerts on different days, children aged 5-7 were invited to approach and explore one

instrument. This study’s data included the number of children approaching each instrument, the

gender of both the child and the musician, and the instrument. The study’s results showed a

larger number of children approaching the instruments performed by members of the same

gender. Children were also asked to draw a picture of a musician playing one of the instruments

they preferred. Males depicted fewer female musicians in their pictures, and likewise, females

depicted fewer male musicians. Although the researchers did not interview children on their

attitudes towards musical instruments, this study suggests that stereotypes (e.g. female flute

player), may disappear in atypical situations (e.g. presence of male flute player).

From this overview of the literature on social groups and music preferences, stereotypes,

and misperceptions, it is clear that further investigation of the complex interactions between

99 participant attributes is warranted. Affiliations with social groups affect music preferences, but

do they affect cognitive activities? There are only a few studies that query adults on these gender

and social group biases; is this observation due to the lack of social groups in adulthood, or do

these effects diminish with age? Finally, if social affiliations are defined by demographic

attributes in adulthood, which variables are the most strong for delineating groups? Are there

other significant distinctions, for instance culture and profession, that contribute to group

delineations? The study of culture-specific behaviors may help to answer some of these

questions, especially with respect to self-identity and affiliation.

Culture, Music, and Cognition

Generally, scholars who examine the role of cultural variables on musical experiences

fall into one of two broad categories:

1) Those who believe that responses to music are universal. 2) Those who believe that responses to music are culturally shaped.

Although the focus of this section will be on the second of these views, many studies

acknowledge the existence of some universal qualities of music. In a recent commentary on the

role of culture in music, Baily (1996) noted that ethnomusicologists, as a unified group, “have

been more interested in the idea that human beings are intrinsically musical, and have evolved

specifically to be music-makers” (p. 115) – the argument proceeding this generality being that

cultures approach the perception, interpretation, and performance of music in different manners.

The goal for ethnomusicologists, then, has become one of describing these practices within their

cultural contexts (Hood, 1960; Merriam, 1964). Contrastingly, the goals of scholars involved in

the cognitive sciences have focused on musical systems of analysis, perception, cognition, and

100 meaning-making. This viewpoint stems from the theory that musical behaviors arise a result

of cognitive processes (Walker, 1997). The following review will briefly summarize a few

studies that have contributed to the growth and development of culture and cognition, especially

in the form of modeling culturally-specific structures in music analysis and cognitive

representation of musical features.

Many attempts at merging two domains into a “cognitive ethnomusicology” have

examined cross-cultural patterns in musical systems. Early studies focused on modeling distinct

features, such as vocal singing styles in folk songs (Lomax, 1959, 1968). Lomax (1968)

characterized folk song styles for over 200 cultures in a system called cantometrics, in order to

demonstrate “main paths of human migration and…known historical distributions of culture” (p.

3). The cantometrics project was designed to focus on certain dimensions in vocal music,

including patterns of stress, repetition in text, length of melodic segments, intonation, pitch,

ornamentation, tempo, and volume (p. 14). Lomax also observed contextual factors such as the

social organization of the vocal and instrumental groups (e.g. spatial arrangement, dominance

patterns), and audience behavior. His system incorporated ratings from independent coders, and

resulted in a number of categories of cultural systems of song. The results of this lengthy project

illustrated that musical patterns mirrored other dimensions of society, including production of

food, delineation of status, and sexual activity. In the following passage, Lomax relates sexual

practice and independence to vocal production in two arbitrarily labeled (A and F) cultures:

In the A situation the girl is on her own in some degree; but in F cultures, where there are simply no rules that apply to sex, the girl is totally on her own, and thus less secure. The vocal tension in this situation approaches that of the restrictive set (p. 196).

101 These and other assertions were supported by statistical differences in the ratings obtained

from Lomax and his collaborators; thus, the data collected and the assessment of the data were

from Western listeners rather than being described by members of the cultures. Another study

(Kippen, 1987) attempted to model a system of musical dimensions in North Indian tabla

drumming by interviewing an expert musician. Rather than focusing on differences between

cultures, Kippen used ethnomusicological methods of ethnography, interview, and in situ

observation to model the grammatical dimensions of one musical system. In this case, data were

used to construct an expert model, called the Bol Processor, that performed variations of patterns

based on grammatical rules. Essentially, the system acts as a human listener and performer of

Indian drum patterns, accounting for moment-to-moment experiences specified by the culture.

Other research has provided cultural details of semantic associations (Baily, 1988), durational

contrasts (Huron & Ollen, 2003), and analogies for pitch and time (Bar-Yosef, 2007), in music,

indicating a significant interaction between cultural context and musical systems.

A number of empirical studies have illustrated different degrees of cultural impact on

cognitive responses to music. Robert Walker has been a leading proponent of these cross-cultural

experiments, especially concerning reactions to basic properties of physical sound. In his earlier

work, Walker (1978, 1985, 1987) hypothesized that “subjects acculturated in different auditory

environments and language traditions might be expected to have formed different auditory

gestalts at the higher levels of neural processing” (1987, p. 493). To test this theory, his study

(1987) analyzed agreement patterns for visual-sound metaphors between experience- and

culture-related groups. Respondents from six groups – musically trained, urban (musically

inexperienced), Inuit, Haida Indians, Shuswap Indians, and Tsimsian Indians – were asked to

102 match short sounds29 to visual metaphors by circling pictures on a response sheet. His results

showed overall differences in visual metaphors between sounds. These differences were

ultimately dependent on the feature to which it was manipulated (e.g. size matched amplitude;

vertical changes matched frequency; horizontal changes matched duration; and pattern matched

waveform). In addition, group identity influenced the responses, such that those who were

musically trained responded more conventionally than musically naïve groups, and the Shuswap

Indians responded with fewer typical matches than the other groups, especially for frequency and

duration. Even though musical experience had a larger effect on the results, Walker suggested

that cultural factors, such as remoteness of location, impacted participants’ knowledge of

Western-influenced metaphors for sound. In a later publication, Walker (1990) further claimed

that “…our perceptions are mediated in powerful fashion by our acquired beliefs and cultural

knowledge, which supply the requirements our perceptual apparatus seems innately designed

for” (p. 173). By examining pitch relations between intervals in Western and Pacific Northwest

Indian cultures, he postulated a direct influence of cultural definitions of pitch, implicitly

apparent, on resultant perception. A different study illustrated similar cultural deviations for

vocal production of sound, specifically evident in patterns of spectral energy (Walker, 1978).

Walker postulated that repetitive musical behaviors, passed down as cultural knowledge, create

these variants in sound and resultant perception:

…as our experience grows we develop systematic ways of coding information…Incoming information that can be recognized as language or music known by the perceiver is processed through these pre-existing codes (Walker, 1978, p. 24).

29 Each sound was manipulated within one of four features: frequency, amplitude, duration, and waveform.

103 The features of these specialized codes show a striking resemblance to cognitive mechanisms

of schematic and categorical processing, previously mentioned in this review. Although he

shows strong effects of experience, and slight effects of culture, Walker’s studies do not consider

self-reported identities and affiliations of his respondents; there is no indication as to whether

participants would describe themselves as “musically-experienced” or affiliate with the “Pacific

Northwest Indian culture.” Instead, there is an implicit assumption of affiliation.

Additional experiments provide conflicting evidence on the extent of culture-specific

aspects of in real-time processing and judgments of musical stimuli. In an application of

Krumhansl’s (1990) probe tone paradigm to North Indian music, Castellano, Bharucha, and

Krumhansl (1984) asked Western and Indian listeners to judge the contextual appropriateness of

tones in North-Indian themes. Their results suggested specialized tonal hierarchies for Indian

music, dependent on interactions between scale membership and tone duration. No between-

group differences were discovered for these probe-tone judgments; however, further analyses

revealed that Indian listeners’ responses adhered more to fundamental aspects of the “parent

scale,” or thaat. The authors suggested that certain elements, such as the hierarchic nature of

tones in a harmonic system, are perceptible regardless of cultural experience; thus, listeners may

have referred to their preexistent, culturally-influenced knowledge structures to make judgments

about these stimuli. However, others have argued that listeners are unable to process music from

outside their culture, especially considering potential variations in musical syntax between

cultures. In a recent experiment, Curtis and Bharucha (2009) used a memory task to test

schematic knowledge, or information about musical syntax and semantics. Their paradigm

required participants, unfamiliar with Indian music, to judge whether tones were included in

preceding Western- or Indian-derived melodies. Their measurements of reaction time and

104 accuracy showed that listeners thought they heard Western-derived more than Indian-derived

scale tones (a case of a false alarm) and took longer to reject Indian-derived scale tones. These

results were considered in light of previous research on factors of musical experience: “…when

listening to music from an unfamiliar modal system, we may impose our own cultural

expectancies on that musical system” (p. 373). Experiments on emotional judgments of music

also show a range of results. Basic emotions, including happiness, sadness, and anger, as well as

affective sounds (e.g. gasps) have been shown to be perceptible, regardless of the listener’s

musical or cultural background (Meyer et al., 1998; Balkwill & Thompson, 1999). However,

further analyses in this latter experiment revealed cultural differences in judgments of phrase

structure as related to emotional perception. These discrepancies imply that relationships

between culture and musical experience are characterized by a complex, detailed set of

interactions.

Professional Musicians

Since the present study involves participants who are professional musicians, a brief

survey of the research on musicians as groups is helpful. Beyond differences in their musical

processing and their patterns of music preference, music plays a large role in musicians’ social

lives, contributing to distinct subcultures of sociocultural activity. This section of the paper will

provide a summary of previous research on the activities and relationships of professional

musicians. In particular, recent evidence on the identity of jazz musicians, as contrasted with

previous outlandish depictions, will be presented as providing a framework for the present study.

Descriptions of musicians’ relationships and practices are almost exclusively

ethnographic, and musicians in social groups are often referred to as cultures. The sociologist

105 Ruth Finnegan’s seminal work in Milton Keynes (2007) described the daily and local

routines of amateur musicians as separate from those of the conventional practices of outsiders.

She revealed a social structure of independent music worlds, such as jazz, rock, and pop, in

Milton Keynes, with sufficient numbers of connections between them. Portions of her work also

depicted the differences in skill acquisition, practice routines, and creative process which are

seen between these worlds. She concluded that music-related activities served as means of

identity formation for actors in each musical world; her contentions thus matched one of the

defining features of groups discussed earlier. In a similar study, the anthropologist Sara Cohen

(1991) explored activities of professional rock musicians in Liverpool, England. She

concentrated on those activities dealing with the music industry and business, including the topic

of attaining individuality in a commercialized market. Even though her observations were

limited to the tradition of Liverpool rock music, she provided a valuable glimpse into the give-

and-take processes of rehearsing music, constructing identity, and surviving in the industry.

Jazz musicians are often depicted in a variety of ways, although some early studies have

focused on their isolation in a difficult industry (Merriam & Mack, 1960; Becker, 1963). An

article by the ethnomusicologists Merriam and Mack (1960) related jazz communities to groups,

namely “…people who share an occupational ideology and participate in a set of excepted

behaviors” (p. 211). According to the authors, jazz musicians in the time period under study

enacted the norms of their group, including language use, musical tastes, clothing, and

interpretation of music. Merriam and Mack saw isolation as a central theme in the jazz

musician’s life, resulting in anti-social behavior, dislike of nonmusicians, and display of

accepted group norms. They supported their claims with numerous examples, ranging from

language use (e.g. “ya dig, cats?”), dress, and jam session participation. Since this study was

106 painted in the light of jazz culture during the late 1950s, it is now severely outdated.

Nonetheless, these authors provided a systematic view of the implicit activities and assumptions

within jazz communities. Several years after this article, sociologist Howard Becker (1963)

addressed similar aims with a book on deviant cultures, in which he included a section, published

earlier (1951), on the working dance band musician. His sociological roots were palpable

throughout the chapter in his view of the relationships between musicians and their audience. An

outsider, specifically a nonmusician or sellout, was described as a “square,” or,

…the kind of person who is the opposite of all the musician is… and a way of thinking, feeling, and behaving (with its expression in material objects) which is the opposite of that valued by musicians (1963, p. 85).

This distinction promoted a view emphasizing the separation of the musician from the rest of

society, dependent on ostensible social attitudes and behaviorisms. Becker opined that these

practices served as a means of isolation, removing the musician from popular society. Musicians

were observed distancing themselves by proximity in venues, avoiding eye contact, and making

use of symbolic expressions (e.g. “square”). According to Becker, these characteristics

influenced the formation of clique membership; these cliques in turn “allocate the jobs available

at a given time” (p. 104). At the time of Becker’s text, and as is still true today, musical-network

affiliations increase job security, because musicians pass along job opportunities to those in their

close circles. Becker described the network as an interlocking web of connections, but also as a

hierarchy musicians can transcend, in order to gain prestige in the entertainment industry.

Although somewhat outdated, Becker’s sociological study provided an in-depth look at the

personal reflections of working musicians, which helped to validate his statements on the culture

of deviant groups.

107 Within the past fifteen years, jazz scholarship has seen two major works on the

processes of creating, improvising, and interacting with music. Paul Berliner’s Thinking in Jazz

(1994) covers the topics of skill development, creative acts of improvising and composing, and

social and musical interactions between musicians. Berliner emphasizes the importance of rich

cultural environments, including performance opportunities in church, school, and at home, often

using interview statements from professional jazz musicians:

Many serious young performers ultimately supplemented their training at school with coaching by relatives at home or in the neighborhood. In Vea Williams’s household, her earliest “voice lessons” consisted of singing with her mothers and sisters as they all washed dishes after meals and did other household chores. When Max Roach grew up in New York city, “there was always somebody’s uncle next door or across the street who had a band, and when they took a break, the kids were allowed to fool with their instruments” (p. 27). As this passage implies, the development of musical skills in jazz often occurs through both

observation and participation. This mirrors the aforementioned practices of social groups in their

development of traditional histories, which involve reexamination of rules and conventionalized

practices by the group (Thrasher, 1927). Like Merriam and Mack, Berliner further defined the

jazz community, but in a more informal manner: “At its core are professional musicians and

aspirants for whom jazz is the central focus of their careers. Overlapping with the core are

accomplished improvisers who divide their professional energies and talents between jazz and

other musics” (p. 36). His definition incorporated fundamental experiences in community

development, including informal study sessions and apprenticeships, jam sessions, and “paying

dues.”30 Berliner does not explicitly consider the effects of these practices on cognitive

30 “Paying dues” is described by Berliner and others as a set of activities aimed at professional success, such as attending jam sessions, playing “sensitive renditions” of jazz standards, and performing as background musicians (Vargas, 2008). They are unified by their contributions to adversity, or the hardships of being a professional musician.

108 frameworks for jazz listening and performance. However, Ingrid Monson’s study (1996),

appearing two years later, framed analyses of musical interaction and communication within

interview statements from professional jazz musicians. Her proposal concentrated on the link

between music and interpersonal factors, as exemplified in this selection from the text:

In an improvisational situation, it is important to remember that there are always musical personalities interacting, not merely instruments or pitches or rhythms. It is not uncommon for players to express this musical process of interaction in interpersonal rather than musical terms, which makes sense in a form in which performance and the creation of music ideas are not separated (p. 26). Interpersonal talk about music between musicians could, then, create musical metaphors that

characterize musicians’ identities. Monson suggested that this could arise within the interplay

between “intercultural associations” and musically performed patterns, which presumably

connect a sonic environment to the meaningful structures and patterns of which musicians speak.

Thus, although not explicitly cognitive in scope, her text indicated a move to the connection

between thought process (expressed as a musical identity) and action (performance).

At the turn of the new millennium, several articles related to Berliner’s and Monson’s

ethnocentric studies were produced from a group of researchers in Great Britain. MacDonald and

Wilson (2005; 2006) investigated jazz musicians’ identity formation, drawing upon the rich

research literature in social identity theory and inter-group relations summarized earlier. They

used focus group settings as a way of creating natural, ecologically valid environments in which

their participants could discuss musical activities. An earlier text by MacDonald and colleagues

(2002) assumed that “we all operate musical identities,” and “how we see ourselves and how we

relate to the world around us is…influenced by music” (p. 343). Their focus group studies

emphasized the importance of engaging in identity-forming activities, such as appreciation of

109 and resourceful engagement with the jazz tradition. Their further definitions of musical

identity in the jazz community were specified by a set of subjective criteria. Particular examples

were musicians’ awareness of the failure to understand the jazz language, and of musical

moments when “everything comes together,” securing a place for the experience in a musician’s

memory. Overall, their results supported their assertion that speech and conversation about music

significantly informs the conceptual identities of musicians. In addition, they provided evidence

for the relation between constructed identities and the creative process of improvising and

performing jazz. Not only did these psychological studies elaborate upon the interviews and

analyses presented by Berliner and Monson, but they also paved the way for projects linking

professional activities to cognitive processes in musicians.

Chapter Summary

The preceding review of literature illustrates the multifaceted nature of this dissertation.

Semantic systems of associative representations may be viewed in a number of ways, including

the structure, function, and organization of items in semantic memory, as well as through

modeling mechanisms of cognitive processing. Previous studies have demonstrated the impact of

sociocultural variables on behavior, preferences, and cognitive representations, as related to

domain-specific knowledge. Still, theoretical and empirical studies of associative semantic

memory have yet to be addressed in music as they have in other domains. Although the degree to

which collaborative affiliations affect cognition of meaningful stimuli can be related to previous

findings on research in social and cultural groups studies, the extension of these previous

methods to music has not yet been attempted. Thus, the present study represents a new line of

110 inquiry for understanding the relationship between associative structures and affiliations by

providing an integrated view of cognition and collaborative activity.

111 CHAPTER 3

RESEARCH METHODS AND DESIGN

Introduction: Restatement of Purpose and Chapter Overview

This chapter details the design and methodological procedures of the present study.

Chapter 2 provided a synthesized picture of the research issues. Here, I will briefly readdress my

questions to frame my methodology:

1. What governs the content and structure of semantic knowledge of music in a specialized style system, namely mainstream jazz?

2. What is the relationship between musicians’ characteristics such as experience,

education, and community affiliation, and the semantic knowledge used to interpret mainstream jazz?

These two questions will be addressed with focus group interviews as well as more traditional

methodological paradigms, such as comprehension studies, in cognitive psychology. Following

an overview of these methods, I describe the ecological approach framing this study’s motivation

and design, and comment on its potential complications. The bulk of this chapter details the

design and results for the preliminary focus group interviews and describes the eminent

performer study, which included three components: social network analysis, association of

names to musical excerpts, and matching of terms to musical excerpts. Previous experiments on

categorical perception and music helped to form my speculative hypotheses, which will be

presented at the end of the chapter.

112 Methodological Overview

The goal of the present study is to describe the structure and function of the knowledge

and abstract representations, which are critical for, and govern expertise for, a familiar style

system. As specified in chapter 2, studying various forms of mental representations provides a

useful way to both organize knowledge and elucidate processes of meaning making in a given

domain. However, before the structure and function of knowledge can be modeled, the content

and relative strength of this memory must be considered. Thus, for this study, a combination of

qualitative and quantitative data collection and analysis procedures will be used to investigate

this question. Although I will not systematically evaluate the two methods with respect to a

mixed methods approach, I will use both approaches (as seen in chapter 4) attempting to find

both ecological and descriptive validity, while at the same time knowingly relaxing experimental

control. Since few published investigations in jazz have attempted to explain musical meaning as

a network of associations in memory, I will use free recall, verbalization, and matching tasks to

explore these knowledge systems. This variety of tasks will allow me to compare multiple

responses to the same stimuli, while still maintaining an aspect of relevance for the

participants31.

An ecologically valid method considers the nature of tasks presented to the respondents

and attempts to relate the results to everyday activity (Neisser, 1982). Researchers who value

ecological validity have argued that the majority of laboratory experiments ignore the influence

of contextual information and fail to acknowledge the importance of conventionally framed

inquiries (Gibson, 1979; Shepard, 1984). Results from ecologically valid experiments have a

greater probability of application to “real world” phenomena (Brewer, 2000). By including two 31 “Relevance” meaning that the participants will be able to identify with the stimuli because they may already be familiar with it.

113 focus group interviews, I intend to understand real world activities of professional musicians,

such as the verbalization of meaning in conversation, in order to account for their points of view

in the study’s design.

Focus Group Interviews

Traditionally, focus group research has allowed the consumer industry to understand how

potential buyers feel about a certain product or material (Merton & Kendall, 1946; Merton et al.,

1990). Using guided group conversation, focus group studies have typically yielded group

consensus and reflections upon variations within the consensus (Krueger & Casey, 2000).

Krueger and Casey (2000) defined focus groups in terms of five broad characteristics: focus

groups consist of “(1) people, who (2) possess certain characteristics, (3) provide qualitative data

(4) in a focused discussion (5) to help understand the topic of interest” (p. 6). With reference to

qualitative research in the social sciences, Morgan (1997) stated that the most meaningful data

from focus group research arises out of the interaction between the members in the group,

because it highlights the variation between individual opinions and experiences. However, since

the interview sessions are conducted by moderators with research agendas and include

participants with preexistent group influence, the setting of a focus group can also create

noticeable drawbacks. Despite this and other critiques regarding the inconclusive nature of many

studies’ results (see, for example, Stycos, 1981) focus group research has proved to be a useful

source for understanding any understudied phenomenon, whether it is food, music, or work

atmosphere.

In the present study, two focus group interview sessions were conducted with

professional improvising musicians to determine the content, structure, and function of

114 purposeful listening. The term was used by the composer David Dunn to denote the active

process of assigning meaning to a piece of music. In the directions to his piece, Purposeful

Listening in Complex States of Time, Dunn (1999) stated, “…not only does music primarily

consist of the perception of sound in time but…it is the perceiver that is engaged in both

organizing that perception and assigning it meaning” (p. 1). Dunn further explicated his

compositional goal as opening “a different universe of musical perception where…an emphasis

is placed upon the processes of perception and not materials” (p. 2). Studies of jazz musicians

have indicated that purposeful listening plays a significant role in their development both in the

practice room and in performance (Murphy, 1990; Berliner, 1994; Monson, 1996; Lewis, 2008).

Elucidating the process of active listening, or the sense of attending to certain features implied by

or realistically present in the music, was the main goal of the focus group interviews.

Specifically, my interview questions and tasks were designed to address when and where

musicians listen, how they listen, and what they listen for.

Participants

A database of 400 names of professional improvising musicians in the greater Chicago

area was created, through personal communication, online listings, and websites. The email

addresses for 200 of the musicians were collected from personal friends and websites. From this

database, an email message was sent to 40 professional improvising musicians in the Chicago

area who had expressed an interest in the study, and the first 7 who responded were included as

participants in the focus group. Each of the musicians was asked to invite a musical collaborator

to one of two focus group sessions, based on their availability, since a total of six to eight

participants in each group is considered ideal to promote fluid discussion and turn-taking in the

115 focus group setting (Stewart & Shamdasani, 1990; Krueger & Casey, 2000). Two out of the

seven participants were unable to complete this request; thus, focus group 1 included 7 musicians

(7 males; aged 25 to 45 years, M = 34 years; playing experience 15 to 35 years; M = 21.36

years), and focus group 2 included 5 musicians (3 males, aged 26 to 53, M = 36.8 years; playing

experience 13 to 36 years, M = 23.8 years). As depicted in table 3.1, the participants had a

variety of educational backgrounds, ranging from self-taught to years of private instruction, and

9 participants had university degrees in music.

Table 3.1: Focus Group: Participant Demographics32

All participants had at least two years experience in performing professionally in the Chicago

area and played from 1 to 7 performances (M = 2.7) per week. The participants provided

descriptions of the style of music they performed most regularly; these included “straight-ahead

jazz,” “rock,” “instrumental creative music,” “music,” “original music,” “modern classical,”

32 Participants had either Undergraduate (U) or Graduate (G) educations.

Gender Age Instrument Experience (Yrs) Training Practice (Hrs) Education Gig/Wk Style

M 29 Gtr 18 private 2 U 1 to 2 Jazz, RockM 25 Sax 15 private/school 1.5 U 0 to 2 CreativeM 28 Dms 19 group/self 1 U 3 Improvised MusicM 28 Bs 18 private 1 G 5 to 7 Jazz, Jobbing,

Original

M 45 Clo 35 private/group 2 U 3 to 4 Improvised MusicM 45 Tb 35 group/private 2 U 2 to 3 Improvised MusicF 48 Sax 36 private/school 4.5 U 3 Gospel, Jazz,

OriginalF 53 Vb 34 private 1.5 U 1 Original, worldM 26 Gtr 13 private 3.5 U 1 JazzM 28 Bs 15 self/private 1 U 4 Jazz, Rock,

ClassicalM 41 Perc 25 self 0 U 0 Improvised MusicM 26 Dms 16 private 1 U 4 Improvised Music,

Rock

116 “improvised music,” and “jobbing music.” Each participant was compensated $30 for

participating in the study.

Materials and Procedure

Each focus group session took place in the moderator’s home, and each lasted 2 hours.

Each participant was instructed to bring a recording that he or she “knew like the back of your

hand” to the focus group session. Since several participants asked for clarification on this

requirement, an additional email specified that the participant should have listened to the

recording numerous times, thus knowing the music very well, but not necessarily possessing a

written transcription of the songs on the album.

Upon arrival, the participants filled out an extensive musical background survey

(Appendix A). After the participants introduced themselves to the group, the moderator

explained the purpose of the group interview and then asked a series of questions about listening.

The sessions were divided into four topic areas, the last of which included a set of listening

activities: early influences on listening to music (topic 1), structure of listening (topic 2), and

musical features of focus while listening (topics 3, 4). A series of questions was asked during the

first three sections, while the fourth included a set of listening tasks and subsequent discussion.

Specifically, the participants responded to the following:

Topic 1: Describe the first time you experienced music that grabbed your attention, motivating you to listen with a purposeful direction.

Topic 2: How often and under what circumstances do you listen to music? Topic 3: What features do you focus on while listening? Topic 4: Listen to each excerpt, completing the following tasks on your worksheet:

117

1. Write a description of each excerpt, focusing on the features that you think characterize the music. In addition, state your personal preference for the excerpt.

2. Please group the numbered excerpts within the circle below

according to their musical resemblance. This classification must be based on your own set of criteria, but should reflect similarities and differences.

As the moderator, I posed each question in an informal manner, and encouraged participants to

respond to each other. I also directed questions to particular people in the group if they were

reticent to participate in the discussion; however, both focus groups included participants who

were more talkative than others. Discussion topics 1, 2, and 3 were allotted 25 minutes, while

discussion topic 4 and the listening tasks were allotted a total of 45 minutes. For topic 4, a two-

minute excerpt of each participant’s recording was played for the group, while they completed

the description and categorization tasks. The two control recordings that were used for both

focus group sessions, as well as the participants’ self-selected recordings are listed in table 3.2.

Table 3.2: Focus Group Recordings

Focus Group Artist Year Album Title1 & 2 Thelonious Monk 1958 Genius of Modern Music1 & 2 Peter Brotzmann 1968 Machine Gun1 Charles Mingus 1963 Mingus Plays Piano1 Matthew Golombisky 2005 Unreleased1 Biosphere 1997 Substrata1 Velvet Underground 1968 White Light, White Heat1 Latin Play Boys 1994 Self Title1 Luc Ferrari 1967-70 Presque Rien1 Lightnin' Hopkins 1966 Live2 Wes Montgomery 1965 Smokin' at the Half Note2 Cedar Walton Trio 1996 St. Thomas2 Miles Davis 1954 Bag's Groove2 Bill Frissell 1995 Live2 Thelonious Monk 1961 Live in Italy

118 The two control recordings were chosen on the basis of their distinct stylistic mannerisms

within the genres of improvised music and jazz in that the Monk recording can be distinctly

catalogued as jazz, while the Brotzmann recording may be catalogued as improvised music

(Erlewine et al., 1998). All discussion topics and tasks were completed over the two hours.

Audio recordings of the conversations were transcribed and analyzed according to three

qualitative coding techniques: text chunking, emergent theme analysis, and conversation analysis

(Denzin & Lincoln, 2003; Agar & Hobbs, 1985; Schiffrin et al., 2003). Text chunking involves

separation of blocks of text in terms of a common topic, which is a framework for extracting

themes from the text. The second technique, emergent theme analysis, distinguishes between

broad themes, or those reported in multiple interviews, and specific themes, or detailed versions

unique to particular participants. Finally, researchers who do discourse analysis have

traditionally used conversation analysis to look at the significance of pauses, dynamics, and

contour changes in speech patterns, thus uncovering potential implicit meaning from the speech

signal. Table 3.3 provides a summary of the symbols used to denote such speech patterns.

Table 3.3: Discourse Analysis Symbols (Schiffrin et al., 2003)

Symbol Indication[ ] Overlap of 2 people talking- Interruption

(.), (..), (...) Pauses. Falling/Final intonation contour? Rising intonation contour, Continuing intonation?, Rise weaker than question:: Stretching of sound just preceding

___ Dynamic emphasis>< Compressed talk<> Drawn out talk

(hh) Hearable aspiration(( )) Description of events( ) Unknown passage or word

119 Results

Under the three discussion topics, several themes and sub-themes arose from examining

the recordings and the transcriptions. During the discussion on early influences, the participants

in both focus groups spoke about the importance of family members and close friends in

developing listening habits and tastes. The participants characterized early experiences as vivid

emotional and visual sensations, including those connected with live performance and bodily

sensations:

And it was in 9th grade(.) He’d just gotten out of college and took this job (.) And (.) I remember listening to it it was this really really hot day and I was mowing the lawn (.) Isthis (.) completely surreal I was just sweating listening to this youknow and if you know there Tim Berne is panned like hard left and Zorn’s like hard right (...) but it’s weird like its those-those experiences I can remember (.) like everything. I remember what everything looked like and where everything was my mom’s (scarf) I just (.) I remember that one specifically.

This participant, as well as others, accentuated the importance of a recording by bringing to mind

the contextual events that contributed to its representation in memory. The participants also

referenced television and radio theme songs as being significant in the active separation of visual

(the television image) and aural (the music) mediums. As these musicians discussed their early

listening experiences, they framed them within a structured narrative, including periods of

realization in which free will and choice determined their active pursuit of particular artists and

songs. Typically, this active pursuit involved some way of preserving the music, such as

recording songs off the radio or buying tapes, compact discs, and/or records. Although the

participants spoke of the significance of these events to their development as professional

musicians, their comments implied that at the time they were not explicitly aware of these

developmental ramifications.

120 Due to time constraints, the participants in focus group 2 were only able to discuss the

listening routine questions for five minutes. The difference in response in this group, compared

to group 1, is illustrated in table 3.4. In response to the questions about listening routines, the

participants’ contributions were categorized as finding time to listen during either routine

activities or specific moments of the day. About half of the participants (n = 7) said they

typically listen to music during routine activities, such as driving, cleaning, or emailing. The rest

of the participants (n = 5) prioritized the act of listening without added distraction. Some

discussion focused on listening to music alone versus with others. In the latter case, participants

typically shared recordings or songs with musicians or friends for the purpose of introducing

them to something new or significant. Seeing live performances of music and discussing music

with friends were also classified as shared listening activities. On the other hand, many

participants agreed that solitary listening experiences were structured and sacred parts of their

days. In general, repeated listening was revealed as a significant theme in these discussions,

especially as evidenced by the statement “over and over and over again.” This activity of

repeated listening was seen as a way to build knowledge for a particular artist’s repertoire or to

seek inspiration for practicing, composing, and performing.

When asked about their listening foci, the participants mostly discussed topics that fell

into seven themes: determine preference, hear new dimensions, imagine functionality,33 build

knowledge, parse out distinct dimensions,34 promote mysteriousness, and provide emotional

release. The participants in group 1 commented on the issue of mysteriousness of the listening

process. For example:

33 Imagining the functionality of a performance involves the thought of “how I would feel playing this on my instrument.” 34 Such as certain pitches, melodies, harmonies, rhythms, or meter.

121 I don’t really wanna know what I like about music…Cause I feel like if I try to like identify it…And say like that I’m looking for this?, (.) then I get scared that…That I’m gonna like make these (.) judgments on this music and stuff that I normally just naturally would be drawn to (.) are somehow…tainted with these thoughts of like I’m looking f:or (.) a good sonic experience.

In this case, the listener generally wanted to focus on the music in abstract terms that could not

be expressed by a codified system.35 In opposition to this view, the participants in group 2 tended

to focus more on distinct dimensions of the music, such as soloists, particular sections of the

music, or interactions between members of the ensemble. These differences between groups can

be explained by the robust differences in musical taste illustrated by the recordings from each

group and between self-reported performance styles (table 3.2).

In their written responses, the participants referred to distinct musical features, such as

instrumentation, genre and style markings, reference to other musicians, functionality of

performance, identification of emotion, feature descriptions, and preference (table 3.5). When the

participants associated the excerpt with other musicians, they tended to do so with statements

such as “sounds like Monk,” or “kind of reminds me of the Ahmad Jamal trio,” without

describing the features that brought such musicians to mind. For the categorization task,

participants employed several different strategies to organize the excerpts and usually began the

process with an anchor or reference point. Illustrations of the twelve circle diagrams can be

found in Appendix B. The participants referenced four dimensions that they used to structure

their diagrams: genre or style, approach, lineage, and interconnectedness. Excerpts embodying

the same genre (or in some cases subgenre) and time period tended to be grouped together

35 It is worth noting that this participant seemed to be presenting an alternative, somewhat reactionary viewpoint that was broadly agreed upon in this focus group. This is not a typical response from musicians during an interview, as many are willing to comment on musical features they find interesting on a recording.

122 instead of excerpts with the same tempo, tonality, or metric framework. However, many

participants spoke about how they used different strategies simultaneously, which complicated

the diagram and in some cases required an extra dimension, “outside” of the paper (depicted by

arrows and lines in Appendix B; Focus Group 1, Participants 1, 2, and 5; Focus Group 2,

Participant 3). There also seemed to be group differences for task strategy; group 1 participants

referred to the importance of anchors, genre, lineage, and sound quality, while group 2

participants referred to lineage, connection to tradition, and style in guiding their diagrams.36

When these musicians were asked to reflect upon the listening tasks, the collective

discussion centered on the complexities of describing the music, based on elaborated knowledge

structures for the artists in the recordings. Several of the participants spoke about lineage, style,

and collaborations, illustrating their depth of knowledge for the performers on the recordings.

Notably, one participant in the first focus group spoke directly about how familiarity with a

performer’s music affects the way the music is heard, and thus, how the tasks were performed:

“…the only way I think itcouldbe different for me is if I became more familiar with (..)…with

like (.) the overall catalogue of one of the artists that I didn’t know.” This observation also seems

to be related to some of the associations in the written descriptions (e.g. “kind of sounds like

Samuel Barber”). This different level of perception can be influenced by a listener’s knowledge 36 Again, this result may be an artifact of the group difference in genre and preferred performance style. Some research has linked genre preferences to gender, age, personality, social group, and political orientation (Frith, 1981; Weinstein, 1983; Peterson & Christenson, 1987). However, the focus group difference here may be due to a newer form of identity, whereby musicians on the “fringe” of the jazz genre seek out alternative styles of music to inform new styles of jazz. A recent interview article explored these issues in the “avant” jazz scene in Brooklyn by depicting the connections between this new form of “DIY” (Do-It-Yourself) avant jazz, indie rock, and punk music. Dorr (2008) wrote, “What all these New York musicians have in common is that ultimately, they care about jazz. They know its history and they believe in its ability to captivate and astonish. But they’ve also all been disillusioned with jazz at some point, and their work today is a product of complicated relationships, whether they’re attacking outmoded conventions, charting ignored or unknown territories of technique and style, or just pushing familiar forms to their best and brightest potential” (Accessed March 1, 2009). I would like to thank Geof Bradfield for his insightful comments on this matter.

123 of a performer’s history and influences, as a participant in focus group 2 indicated about

Thelonious Monk:

…you know like Monk is gonna use some Bebop because (.) he grew up listening to that (.) and that’s just part of his—his style but then in order for him to be an artist of his own right and not be someone who’s playing just Bebop he had to (.) go a step further (.) and find his voice— So therefore like I couldn’t say Monk is—is just—is Bebop.37

Along the same lines, another participant in the second focus group mentioned the importance of

identifying with the music: “I was like oh yeah—I could tell that was Unit 7, although I didn’t

know it was Wes you know but I knew the—the ch-changes and stuff so I was following (.)

everything else much easier.” Her knowledge of the composition’s harmony not only eased the

process of listening, but it also enhanced her experience of the recording. Such reflective

comments from the participants provided some of the most useful information for developing the

remainder of the study.

Discussion and Relevance to the Main Study

Although the listening exercise proved to be the most relevant to the main study,

additional themes, brought up by the other questions, served to highlight the role of listening in a

musician’s life. The participants constructed their listening experiences with personal narrative,

which included well-developed chronologies to support their status as professional musicians.

The social psychologist, Dan McAdams (1993) elaborated on this process:

37 It is worth noting that this information is not necessarily the case. In fact, Thelonious Monk was heavily influenced by stride pianists such as Art Tatum and James P. Johnson, who were not playing in the style of bebop (Gourse, 1997). The participant seems to either be confused about Monk’s influence, or he was not capable of communicating his knowledge effectively.

124 Social scientists often point to the family unit as the major vehicle for cultural transmission in childhood…Through their actions and words, parents expose children to a wide assortment of images and symbols…Functioning as…“internalized objects,” these emotionally charged images may become parts of the self, continuing to exert an unconscious influence on behavior and experience through one’s adult years (p. 60-1).

For the focus group participants, “internalized objects” included musical opportunities, made

available by family members, like music from radio, television, movies, and in some instances,

live performances. In addition, participants agreed on the significance of these objects in their

early listening behaviors, as is common among professional musicians’ verbalized stories and

biographies as well as in performed improvisations (Finnegan, 2007; Iyer, 2004; Jackson, 1998;

Lewis, 1996). Regarding both verbalized narratives and performed improvisations, specific

performers have been referenced as influential to the development of a musician’s identity.

According to Bloom (1973) and Murphy (1990), the stamp of influence is most noticeable in the

work of art, in which the artist transforms his understanding of his or her influences. Participants

in the focus groups furnished this concept by referencing early influences and relating future

experiences to these artists. Such attachment to a particular artist or catalogue of music can be

likened to the stage of early childhood in a personal narrative, when the child becomes attached

to a caretaker and relates their experiences to her own life, creating an agentic character

(McAdams, 1993). The development of such characters allows the adult to search for a

structured sense of meaning, or to “personify the general agentic and communal tendencies in

human lives,” representing “how each of us chooses or desires to live as an adult in our own time

and place” (p. 161). Musicians in this study hinted at their agentic characters by merging

significant listening experiences into a solidified collection, bound by their musical identities.

Given the data from the focus group interviews, I assert that musical identities are actively

125 formed by a three-stage process: seeking out influential musicians, listening to their

catalogue of records, and sharing those experiences with peers and musician collaborators.

The listening task results suggest that respondents used higher-level characteristics of

music to explain what they heard. For example, musicians used terms like harmony and melody

to describe the excerpts, instead of referencing specific pitches, chord progressions, or patterns in

the music. This finding is not particularly surprising, given the results from previous experiments

on the variety of adjectives required to describe the emotional qualia of music (Hevner, 1935a;

Huron 2006). Huron (2006) categorized adjective responses to music into four classes:

expectedness (e.g. surprising, different), tendency (e.g. leading, restful), valence (e.g. bright,

sad), and other (e.g. simple, melodious). Studies by Hevner (1935a, 1935b, 1936, 1937),

Gabrielsson and Juslin (1996), and Sloboda (1991) suggested that emotional reactions were

explained by musical properties such as pitch height, tempo, timing deviations, and harmony,

textural, and dynamic changes; however, specific musical terms were generally absent from

participants’ responses. Results from the present study’s focus groups indicate that musicians

referred to different features, including instrumentation, genre and style markings, reference to

other musicians, functionality of performance, feature descriptions, and preference. This suggests

that the focus of participants’ listening incorporated more than feelings or mood states. These

musicians typically organized listening experiences intellectually – this process seems to be a

significant part of their professional development (Berliner, 1994). Further more, the act of

associating excerpts with other musicians’ names is a trend not only in this study, but also in

previous anthropological investigations (Berliner, 1994; Jackson, 1998; Davis, 2005). In order to

explore this phenomenon further, both the association and term-descriptor paradigms were

reused in the forthcoming methodology.

126 Main Study: Concepts for Eminent Jazz Performers

Since semantic knowledge for performers is a relatively understudied phenomenon, a

variety of data collection methods based on the focus group sessions were used in the present

study. Each of these methods – social network analysis, free association and descriptor tasks –

will be discussed below. The combination of these methods was used to provide converging

evidence for the content, structure, and function of semantic knowledge for eminent jazz

performers and to speculate on the influence of experience and community affiliation on this

knowledge.

The Network Approach

Techniques associated with social network analysis (SNA) are used in this study as an

indication of cultural and community affiliation. Social network analysis assumes that affiliations

are defined by sets of interrelations, or links, between people. Wasserman and Faust (1994)

accentuate four features that highlight this approach:

• Actors and their actions are viewed as interdependent rather than independent, autonomous units.

• Relational ties (linkages) between actors are channels for transfer or “flow” of resources (either material or nonmaterial).

• Network models focusing on individuals view the network structural environment as providing opportunities for or constraints on individual action.

• Network models conceptualize structure (social, economic, political, and so forth) as lasting patterns of relations among actors (p. 4).

Typically referred to as the “network perspective,” these principles are used to study social

patterns in a range of disciplines, including psychology, business marketing, anthropology, and

127 education. SNA aims to model the structure of social relationships and to explain transfer of

information with specialized mathematics, terminology, and graphs (Hanneman & Riddle, 2005).

Although the mathematics behind social networks is a particularly interesting topic that provides

useful theory for software analysis programs, the present study is not concerned with these

complex details.

Techniques of social network analysis rely upon specialized terminology, based on

matrix operations and graph theory. People in networks are referred to as actors, who are related

to each other by links. Links between actors in a network are represented by their presence or

absence in a square matrix, and the directionality and strength of links are denoted by numbers in

the matrix (Wasserman & Faust, 1994; Hanneman & Riddle, 2005). Wasserman and Faust

(1994) specified particular relationships between people in a network, including evaluation,

transfer of materials, association, affiliation, behavioral interaction, movement between places,

physical connection, formal relations, and biological relationships (p. 18). Given these complex

relationships, multiple links between actors are defined as the relations that are unique to a

particular number of actors, such as a dyad or a triad. Links among a larger number or “system”

of actors are called subgroups or groups, and social scientists typically focus their projects on

these structures. Terms are used to refer to similar phenomena in the network graphs drawn by

computer software. Somewhat contrasted from matrix operations, graph theory distinguishes

actors as nodes, points, or vertices, and links as ties, edges, or arcs. Graphs are analyzed in terms

of nodal degree, or “the number of lines incident with each node in a graph,” which influences

the graph’s overall density, or “proportion of lines actually present” (Iacobucci, 1994, p. 101).

Features other than degree can be represented in network graphs, such as participant attributes

(e.g. gender, age) and geodesic distance, or the smallest number of ties between two nodes.

128 Although additional concepts are used to describe network graphs, the following study will

only consider the aforementioned terms.

Social network analysts collect data on the relations between actors with several different

methods. Generally, researchers specify a population of interest and represent it by surveying a

sample of people from the larger population (Laumann et al., 1989). Then, a well-defined closed

sample of participants is asked to provide information on their ties to others, given a set of

instructions (Wasserman & Faust, 1994). The present study uses ego-centric network methods to

allow for flexibility around musicians’ schedules and willingness to participate. These

procedures ask individual actors to comment on their localized relations and typically result in a

network with an unknown number of actors (Burt, 1984; 1985). According to Hanneman &

Riddle (2005), this approach models “the differences in the actors’ places in social structure,”

and “make[s] some predictions about how these locations constrain their behavior” (p. 10). In

addition, ego networks can be used to speculate on social positions and roles within localized

communities that grow and change over time. This type of data collection was best suited for the

current project, since professional music communities are constantly affected by the arrival and

departure of musicians as well as part-time touring opportunities.

Finally, the forthcoming methodology makes use of rating-scale questionnaires to gather

information on ego-centric musician networks. Given the large number of members in the

Chicago jazz and improvised music communities,38 participants were asked to list ties in the

format of fixed-choice free recall, instead of free-choice roster.39 Although this method results in

38 This sample included approximately 461 actors; however, this is by no means a valid estimation of improvising musicians in the entire city of Chicago. 39 Fixed-choice free recall asks participants to name a specific number of contacts, but does not ask them to choose from a list. Free-choice roster asks participants to choose contacts from a preexisting list of names, but does not specify a limit to the number of names they can choose.

129 a larger number of names than free-choice roster, it was used to give respondents the freedom

to name any musician in the Chicago community, rather than attempting to compile a

comprehensive fixed-choice list of names from which to choose. Specifically, the participants

were required to name twenty musicians in Chicago with whom they collaborate. Wasserman

and Faust (1994) have asserted that this free-choice method may be less reliable because of its

reliance on memory; however, considering the large number of people in the present network,

the roster method would lengthen the survey time considerably.

Conceptualization Tasks

The majority of previous research on mental concepts and categories incorporate stimulus

priming as a method of collecting responses (Rosch & Mervis, 1975; Medin & Smith, 1978).

However, these paradigms use standard or normalized definitions for categories, drawn from

dictionary definitions or common sense information. Since no standard set of concepts or

categories for eminent jazz performers is present in the literature, such information may be

beneficial to the psychological study of jazz and improvised music. Given their extensive

experience listening to and transcribing recorded music, professional musicians are a good

resource for accurate and candid descriptions of performers and their music (Ratliff, 2009). Thus,

the following methodology explores musicians’ knowledge in both qualitative and quantitative

ways.

This study employs a free association task as one method of uncovering knowledge about

music. Traditionally, free association tasks have been used to determine the content of memory

for prompted stimuli in a qualitative manner. In one of the earliest known studies using this

method, Francis Galton (1879) reflected on the process of interpreting objects while he walked

130 down Pall Mall Street in London. By using a simple method of data collection, that is,

informally observing the way in which the mind uses external prompts to create “free”

associations, Galton came up with list of 75 word associations for objects on the street. After

this, he furthered the process by associating more words with earlier sets of words, over four

trials. These words were then classified into the following types: sense imagery, histrionic,

names of persons, and verbal phrases or quotations. His analysis of the resulting associations

revealed that all were related to the stimulus, even those produced later in time, despite Galton’s

hypothesis that the words would relate more to fixed associations in memory. He also found that

some of the words were associated with identical ideas during different trials at varying points in

time. When the task was repeated with the same object, a standard set of ideas was devised.

Galton explained this finding with the following conclusion: “This shows much less variety in

the mental stock of ideas than I had expected, and makes us feel that the roadways of our minds

are worn into very deep ruts” (p. 151). Galton’s informal observations suggest that free

associations for concepts and categories, although large in number, have an upper limit. Later

studies have provided a more formal set of requirements for participants and like Galton’s, have

implied that mental associations are formed by considering both fixed associations in memory

and stimulus features (Deese, 1965; Jenkins & Russell, 1952)

Experiments that use the free-association method propose standard word banks to imply

that memory contains similarly compiled information (Nelson et al., 2000, 2004; Fernandez et

al., 2004; Steyvers et al., 2005). One study asked participants to “produce the first word that to

comes to mind that is related in a specific way to a presented cue” (Nelson & McEvoy, 2000, p.

887). Although cues can be presented in any medium or form, most studies present syllables,

words, or pictures (Nelson et al., 2000; Snodgrass & Vanderwart, 1980). In these studies,

131 associations are analyzed for their frequency and probability, providing an “index of

strength” for the most typical responses for a word. When participants are asked to list as many

words as possible, their first responses usually have the highest index of strength, especially

when compared with second or third responses (Nelson & McEvoy, 2000). However, Galton’s

(1879) results suggest that words produced later were equally as relevant and reliable as those

listed first. This may depend on the type of information presented, since Galton’s experiment

was prompted by external objects and Nelson’s and McEvoy’s was prompted by words. The

results from free association tasks have shown remarkable agreement and consistency between

participants and prove to be a reliable indication of memory content (Fernandez et al., 2004;

Nelson et al., 2004). This technique is also significant to building semantic networks, which play

a role in reaction time experiments (Rosch & Mervis, 1975).

Previous studies also suggest that mental associations depend on social and cultural

variables (Fernandez et al., 2004; Nelson et al., 2004). Nelson and colleagues (2004) asserted

that free association studies provide information that “taps into lexical knowledge acquired

through world experience,” which is created by “associative structures involving the

representations of words and the links that bind them together” (p. 402). Provided there is a

relationship between the content of knowledge and the process of interpretation, this method

allows the researcher to analyze how a specific group of people normatively interprets a

stimulus. In their study on word associations, Nelson and Zhang (2000) found that previously

experience accounted for approximately 50% of the variance in word recall. In the free

association literature, there has also been an active move to create databases of word associations

for various cultural groups (Fernandez et al., 2004) and to compare different populations on

related cognitive tasks (Stacy et al., 1997).

132 To test the hypothesis that musicians tend to focus on higher-level processes of

interpretation, the following study also asks participants to reflect upon their associations by

referring to the music and their knowledge of it. Typically, verbalization tasks like this require

participants to describe reasoning strategies and inner dialogue during a cognitive task (Ericsson

& Simon, 1993). Studies that require either written or spoken description, or verbalization, show

a facilitation effect for learning and retrieval of stimuli (Spearman, 1937; Richards & Waters,

1948; Brown & Lloyd-Jones, 2006). Furthermore, verbalization increases the amount of attention

and detail of focus on stimulus characteristics, such as its particular features and the

interpretation of those features. This process characterizes the level-of-processing as tapping into

a higher-level of interpretation, which has been shown to engage in the processing mechanisms

involved in long-term memory retrieval and feature comparison (Brown & Lloyd-Jones, 2006).

For the purposes of this study, the verbalization task was modified slightly by asking respondents

to type instead of speak aloud their responses.

Pilot Study: Participants

To ensure clarity of instructions, a pilot study was included before the development of the

main (eminent performer) study. Typically, pilot studies are used to improve the methodology,

given comments from participants who fit the projected sample population (Mertens, 1998). The

following guidelines, provided by Mertens (1998), were used as a reference for the present pilot

study:

ask the pilot participants to tell them what they think the questions mean and to suggest ways of rewriting them if they are unclear or too complex…include a section at the end of every questionnaire where participants can record any additional questions they think should have been asked (p. 117).

133 After considering these comments, Mertens advises experimenters to revise the study

accordingly. The present pilot study asked 3 professional jazz musicians (table 3.6) to reflect

upon the length and comprehensibility of the tasks.

Table 3.6: Pilot Study Participant Demographics

Participants were asked to specify a date, time, and location that suited their schedules. Locations

included the experimenter’s residence (n = 1), the participant’s residence (n = 1), and a local café

(n = 1).

Pilot Study: Materials and Procedure

The experiment was designed in MediaLab, a research software developed for standard

presentation of stimuli (Jarvis, 2008). The interface presents instructions to participants and

provides options for variety of responses, including rating scales, fill-in-the-blanks, and multiple-

choice.

The experiment was divided into four sections,40 presented in the same order between

participants, and lasted between 100 and 120 minutes. Section one instructed participants to type

twenty names of Chicago musicians with whom they collaborate regularly. I defined

collaborations as frequent and significant relationships with musicians in creative performance

40 This dissertation will not present methods or results from the third (card sorting) task.

Gender Age Instrument Experience (Yrs) EducationM 29 Tpt 18 GraduateM 23 Pno 12 UndergraduateM 38 Sax 25 Graduate

134 situations. For each collaborator who was listed, participants were prompted to rate how well

they knew him or her, on an endpoint-defined Likert scale from “not very well” (1) to “very

well” (5). Each time they entered a new name, they were reminded of whom they had already

named, until they had entered the required 20 names. Then, participants were asked to rate how

much they identify themselves as a consistent part of a music community in Chicago, on a scale

from “not at all” (1) to “a great deal” (5). This provided a measure of self-reported community

affiliation. In addition, participants typed free-response descriptions for the music communities

with which they affiliated themselves. A third question queried participants on the extent to

which others’ opinions influence their thoughts on music, on a scale from “not at all” (1) to “a

great deal” (5). This provided a measure of self-reported social and community influence. This

collaborator portion of the experiment typically lasted 15 minutes.

The second part of the experiment presented 20 excerpts, each representing an eminent

jazz performer, and asked musicians to complete a free association task for each excerpt. The 20

eminent jazz recordings (table 3.7) were chosen by referring to the Jazz Innovators list in the

essay section of the All Music Guide to Jazz (Erlewine et al., 1998).

135 Table 3.7: Pilot Study Excerpts

An effort was made to choose well-known innovators in a variety of styles, including classic

jazz, swing, bebop, hard bop, avant-garde, fusion, and European free jazz. Since there is no

known set of standardized stimuli for jazz excerpts, the recordings represented albums that had

been given five. The descriptions and ratings in the All Music Guide provided information for

renowned tracks from the recordings, which were included in the experiment. An excerpt of 30-

40 seconds was extracted from each of these tracks, and each excerpt included a section of the

piece that featured the musician. On the tracks with solo sections, portions from the middle to the

end of the improvisation were extracted, in order to include heightened moments of musical

development. In addition, each excerpt included a period of transition in the piece, so a distinct

structure could be deduced. All the excerpts were equalized for amplitude, and sound files were

edited to fade in and fade out.

Excerpt Performer Album Year Track TimeA Armstrong, Louis Hot Fives, Vol. 1 1925 Heebie Geebies 0:26-0:52B Art Ensemble of Chicago Live at Mandel Hall 1972 Duffvipels 3:00-3:27C Blakey, Art Moanin' 1958 Moanin' 0:00-0:30D Brotzmann, Peter Machine Gun 1968 Machine Gun 3:18-3:48E Coleman, Ornette New York is Now! 1968 Broadway Blues 0:20-0:50F Coleman, Ornette The Shape of Jazz to Come 1959 Lonely Woman 2:04-2:34G Coltrane, John Ascension 1965 Ascension 0:00-0:33H Coltrane, John Giant Steps (Alternate Take) 1959 Giant Steps 01:45-2:15I Coltrane, John Live at the Village Vanguard 1961 Impressions 1:47-2:20J Davis, Miles Bitches Brew 1969 Bitches Brew 0:00-0:34K Davis, Miles Miles Smiles 1966 Freedom Jazz Dance 1:55-2:32L Davis, Miles Kind of Blue 1959 So What 2:45-3:13M Ellington, Duke Duke and His World Famous Orchestra 1946 Take the 'A' Train 0:00-0:32N Hancock, Herbie Headhunters 1973 Watermelon Man 2:04-2:36O Holiday, Billie Complete Decca Recordings 1950 God Bless the Child 0:43-1:12P Mingus, Charles Mingus Ah Um 1960 Fables of Faubus 7:07-7:45Q Parker, Charlie Birdsong 1945 Now's the Time 0:30-1:02R Pastorius, Jaco Jaco Pastorius 1976 Donna Lee 0:40-1:12S Roach, Max We Insist!, Freedom Now Suite 1960 Freedom Day 3:50-4:24T Tristano, Lennie Capitol Jazz Classics, Vol. 14: Crosscurrents 1949 Wow 1:52-2:23

136 The 20 excerpts were labeled by the letters A through T and presented in random

order to the participants. The participants listened to each excerpt and were prompted with the

following directive:

List five musicians who immediately come to mind when you listen to this excerpt. These musicians do not have to be people you know, they can be anyone you think about when the music is playing. Then, in the same response box, describe why you think you associated the excerpt with each of the musicians. The instructions also told participants not to include the name of the musician soloing in the

excerpt during the free association task – an additional response box was provided for their

guesses of the excerpt’s performer at the end of the task. After typing these names, the correct

name for the excerpt was revealed, and participants were asked to rate how well the excerpt

represented the musician on a scale from “not very well” (1) to “very well” (5). Additionally,

participants rated the extent to which each performer influences them on a scale from “not at all”

(1) to “very much” (5). This task was completed in approximately 60-90 minutes.

The final task required participants to think of three musical features that contributed to

their understanding of each performer. The name of each performer was presented on a blank

screen for 10 seconds, and after the prompt, participants were instructed to use succinct words or

phrases to “describe (type) your understanding of the performer’s music.” This task required

approximately 30 minutes.

Immediately following the experiment, I conducted an informal interview to ask

participants to reflect upon the difficulty of the tasks, the appropriateness of the excerpts, and the

length of the study. The participants were provided with information on the purpose and goals of

the study and were given contact information for any additional questions.

137 Eminent Performer Study: Participants

A database of 400 musician names was created, given personal interaction, online

listings, and websites. Email addresses for 275 of the musicians were collected from personal

friends and websites. Over a two-week period, two email messages were sent to the 275

professional improvising musicians in the Chicago area, and 51 musicians (45 males; aged 22 to

61, M = 32.8) volunteered for participation in the study. Years of playing experience varied from

8 to 45 (M = 19.6) on the primary instrument, and instruments included saxophone (n = 12),

drums (n = 8), bass (n = 7), guitar (n = 7), piano (n =5), trumpet (n = 4), voice (n = 2), trombone

(n = 2), bass clarinet (n = 2), cello (n =1), and vibraphone (n = 1). Participants fell under three

levels of education, including High School (n = 8), Undergraduate (n = 29), Graduate (n = 14).

All the participants had at least one year of performing professionally in the Chicago area and

played from 1 to 5 performances (M = 2.8) per week in the local area. Self-descriptions of

performed musical styles typically fell under four categories: jazz (J) (n = 18), jazz and other

(JO) (n = 22), improvised music (IM) (n = 2), and jazz and improvised music (JIM) (n = 9).41

Participants were compensated $20 for their time and participation.

Eminent Performer Study: Materials and Procedure

Over the three-month data collection period, the participants were asked to specify a date,

time, and location that suited their schedules to complete the study. The four locations included

the experimenter’s residence (n = 28), the participant’s residence (n = 13), the lab at

Northwestern University (n = 6), and local cafés (n = 4). An effort was made to choose a quiet

41 IM and JIM were later collapsed into one category (see chapter 4).

138 location in the cafés, so that participants would be able to concentrate on the tasks without

being disturbed.

The design of the study included the same three topics as the pilot study, with a few

modifications to shorten the length of an experimental session to between 80 and 110 minutes.

The participants in the pilot study had no difficulties with the social network portion of the

experiment, so no modifications were made to this task. However, the number of excerpts was

reduced to 15, due to the comments of the pilot study participants. The 5 musicians who were

eliminated from the excerpt list were either those who had been included more than once, such as

John Coltrane, Miles Davis, and Ornette Coleman, or those who were judged to be less

representative of the jazz canon, such as Peter Brotzmann and Lennie Tristano. The same

qualifications and editing procedures used in the pilot study were used for the 15 excerpts in the

eminent performer study. The musicians, recordings, tracks, and time information used are listed

in table 3.8.

Table 3.8: Eminent Performer Study Excerpts

Excerpt Performer Album Year Track TimeA Armstrong, Louis Hot Fives, Vol. 1 1925 Heebie Geebies 0:26-0:52B Coleman, Ornette The Shape of Jazz to Come 1959 Lonely Woman 2:04-2:34C Coltrane, John Giant Steps (Alternate Take) 1959 Giant Steps 1:45-2:15D Davis, Miles Kind of Blue 1959 So What 2:45-3:13E Ellington, Duke Duke and His World Famous Orchestra 1946 Take the 'A' Train 0:00-0:32F Hancock, Herbie Maiden Voyage 1965 Dolphin Dance 6:50-7:21G Hawkins, Coleman Body and Soul 1939 Body and Soul 1:53-2:22H Holiday, Billie Complete Decca Recordings 1950 God Bless the Child 0:43-1:12I Mingus, Charles Mingus Ah Um 1960 Fables of Faubus 7:07-7:45J Monk, Thelonious Monk Alone 1968 Round Midnight 1:13-1:49K Montgomery, Wes The Incredible Jazz Guitar 1969 Four on Six 1:52-2:32L Parker, Charlie Birdsong 1945 Now's the Time 0:30-1:02M Pastorius, Jaco Jaco Pastorius 1976 Continuum 2:24-2:58N Roach, Max We Insist!, Freedom Now Suite 1960 Freedom Day 3:50-4:24O Rollins, Sonny The Bridge 1962 Without a Song 2:20-2:53

139 These excerpts were lettered from A to O and were presented in a random order to the

participants. These instructions were modified by asking participants to list three instead of five

musicians who immediately came to mind. This significantly shortened the length of the task to

approximately 50 minutes. The rest of the rating scales and directions were the same as in the

pilot study.

For the final task, the responses from the pilot study were collated and recoded into 24

representative musical descriptors (Table 3.9). These 24 terms encompassed all of the free

responses provided in the focus group study and the pilot study. In the final study, the

participants typed three musical features (given the list of 24 musical features) that contributed to

their understanding of each eminent performer. This portion of the experiment was shortened to

approximately 10 minutes.

Immediately following the experiment, an informal interview was conducted in which the

participants could reflect upon the difficulty level of the tasks as well as provide any comments

they might have about the study in general. The participants were then provided with information

on the purpose of the study and were given contact information in case they had any additional

questions.

Hypotheses

Jazz history texts and liner notes from recordings were valuable resources for developing

and interpreting the tasks, and in the subsequent analyses of the data. A priori hypotheses are

generally considered irrelevant for social network studies, due to sampling procedures and

reliability on unique relationships (Hanneman & Riddle, 2005). Despite this caution, a modest

level of overlap in collaboration names is expected. Differences in named collaborations might

140 be expected to account for a wide range of geodesic distance and centrality measures

between participants, thus providing a reliable measure of strength of community affiliation.

Additionally, a social network graph should reveal a number of subgroups or cliques, in which

members of subgroups have collaborative connections. An analysis of the network’s community

affiliation and graph patterns should provide category membership for each participant.

Since free association word tasks typically provide greater than 81% agreement for

primary associates (Nelson et al., 2000), a moderate to high level of association agreement42 for

the excerpts, between participants, is to be expected. Of course, this will depend on the excerpt

and musicians performing on the excerpt. On the other hand, since the association task involves

the use of musicians’ names, memory lapses by participants may produce some variation in

response. Since ratings of representativeness in this study are a measure of typicality, higher

ratings should be positively correlated with higher association agreement. This pattern of results

is common in free association word tasks and categorization paradigms (Nelson et al., 2000;

Rosch & Mervis, 1975). In addition, higher ratings of a musician’s influence may correlate with

either a very high or low frequency of response, since increased opportunities for building

knowledge in a domain (e.g. listening to more music in a musician’s catalogue) can result in

either more or less solidified concepts in memory (Smits et al., 2002; Medin et al., 2006).

Associations for this listening task may be driven by a combination of feature- and knowledge-

driven factors. If participants correctly guess a performer, they may engage a set of knowledge

structures specific to that performer. The free association task should reveal significant

differences between performers, since each of the 15 musician-represented excerpts is assumed

to have a unique identity, based on the musicians’ performance history and musical 42 Association agreement is defined as the number of times a particular association (name) was present in the total number of responses.

141 collaborations. Therefore, a variety of criteria for making the associations should be

observed, including information about the performers’ collaborations, influences, and personal

relationships. For example, Miles Davis is often considered to be a collaboration-driven leader

whose career saw many personnel, band, and style changes, so his Kind of Blue excerpt might be

associated with a large number of names (Davis & Troupe, 1989; Szwed, 2002). On the other

hand, Charlie Parker performed for only about 19 years, cutting short his possibilities for

collaborations; yet, he was extremely influential as a saxophonist (Russell, 1996). Thus, Parker’s

excerpt might be associated with fewer names, and these associations might be specified by

criteria about influence rather than collaboration.

The results from the descriptor-matching task are expected to exemplify the differences

between the 15 performer prompts, based on knowledge and feature-driven information.

Descriptor-matching differences between participants are predicted, in light of the differences in

the participants’ jazz experience and community affiliations. I expect the results to show higher

agreement patterns for participants with more jazz experience and community affiliation, and the

opposite for participants with less jazz experience and community affiliation. This is based on

previous research on folk biological terms for objects in nature, which revealed higher agreement

patterns for respondents who were more experienced with the stimuli, or who used the objects

(e.g. fish) for the same underlying purpose (e.g. for sport or for food) (Medin et al., 2006).

Eleanor Rosch (1978) asserted that cognitive mechanisms of categorization are used to

“provide maximum information with the least cognitive effort,” and that “the perceived world

comes as structured information rather than as arbitrary or unpredictable attributes,” especially

for highly familiar stimuli (p. 28). Therefore, since the present study presents well-known

excerpts, the task might be expected to elicit prototypical responses. Previous musical

142 categorization experiments have shown that participants refer to musical features such as

genre or style, tempo, and performing medium when they respond to music (LeBlanc, 1981;

Welker, 1982; Brittin, 1991; Koniari et al., 2001; Deliège, 2006). The variation among responses

in these and other studies might be explained by multiple and embedded levels of categorical

reasoning (Barsalou, 1993; Rosch & Mervis, 1975). Applying these principles of categorization

to Bruckner’s Sixth Symphony, Zbikowski (1995) noted the difference between type 1, or

exemplar-based, categories and type 2, or communication-driven, categories. He states that

“listener[s]…arrive at this [typical, type 1] category without recourse to…informal

formalizations—musical categorization instead goes on quickly and without seeming effort” (p.

25). Zbikowski briefly mentioned that these processes are dependent upon not only auditory

information, but also upon culture- and knowledge-based information, which can affect the way

an excerpt or piece is heard. Thus, knowledge- and community-based variations in the two tasks

are to be expected. Specifically, one might anticipate that participants with more jazz

performance experience and community affiliations might refer to complex categorical

information such as musicians’ collaborations and influence, while those with less jazz

experience and affiliation might use more basic-level category information based on genre,

timbre, tempo, and instrumentation.

Chapter Summary

The methodology presented in this chapter included a preliminary focus group session, in

which several themes on musician narratives (based on phases of development) and listening foci

were revealed. The results from these sessions were used to design the subsequent eminent

performer study, which incorporated both measures of community affiliation and a set of

143 experimental tasks for describing 15 eminent jazz performers. The analysis of the results

from this experiment are expected to show agreement patterns between participants for feature-

and knowledge-based conceptualization strategies, which may in turn illustrate differences

between participants possessing varying degrees of knowledge and community affiliation.

144 CHAPTER 4

DATA ANALYSIS AND RESULTS

Introduction: Review of Goals and Chapter Overview

This chapter presents the analysis procedures and the results for the eminent performer

study. The aims of the study are reframed below to highlight the topic of the chapter:

Collaborator Task: 1. What types of network structures (e.g. clusters) are to be found from the connections provided by the collaborator task? 2. How many subgroups of participants can be determined, and to which subgroup does each participant “belong”? 3. How do the network measures relate to each other and to participant

attributes? Association Task: 1. Do the responses to the association task (names, instruments, criteria43) differ

between excerpts? 2. What is the level of agreement for the name, instrument, and criteria

associations for each excerpt? 3. Do the typicality and influence ratings affect the participants’ accuracy in identifying

the soloists in the excerpts? 4. Do the agreement scores, typicality and influence ratings, and accuracy

depend on the participants’ characteristics44 (e.g. network, education, experience)? Matching Task: 1. Do the participants match different descriptors (given the list of 24) to each performer

prompt? 2. What is the level of agreement for each descriptor? 3. Do the agreement scores depend on participant attributes? 4. Do the agreement scores depend on rated influence and identification accuracy?

These questions were addressed using both qualitative and quantitative methods. Qualitative

techniques were primarily used to code and interpret data, while quantitative techniques were

used to collate and compare responses between participants. This chapter is organized into three

43 These categories will be defined later in this chapter. 44 Characteristics and attributes will be used interchangeably throughout this and the following chapter.

145 sections, each including a brief overview of purpose, a report of data analysis procedures

including descriptive and inductive statistics, and a summary of the main findings pertinent to

each question. The closing section of the chapter provides a comprehensive summary of the

effect of participant attributes on the association and descriptor tasks.

Collaborator Task

Overview

The collaborator task was designed to provide systematic measures of network structures

and the influence of participant attributes on those relationships. Each of the participants (n = 51)

provided the names for 20 of their local collaborators and rated each collaborator on how often

they discuss music together (discussion) and how well they know them (familiarity). In addition,

the participants rated the extent of their social inclusion (community affiliation judgment) as well

as the degree of their peers’ influence on their musical opinions (social influence). The following

analyses of my results will show how these measures uncovered patterns of connection among

smaller communities of musicians and also outline characteristic markers for community

boundaries, such as age and preferred performance style.

Analysis Procedures

Although the collaborator names were collected as 51 separate ego networks, each with

20 nodes, the data were treated as they would be in conventional social network studies (see

chapter 3, page 126). This strategy was used because of the high inter-connectivity between ego

networks, an overlap of approximately 9 alters45. Conventional social network data appear as a

45 Alters are the social network term for people.

146 square matrix (Figure 4.1), with relations treated as a binary (1 = connection, 0 = no

connection) variable; thus, collaborator data were recoded into matrix form to illustrate relations

based on a total of 461 names.

Figure 4.1: Example of a Matrix in Social Network Analysis (Hanneman & Riddle, 2005, p. 2)

This matrix shows that Carol reported that she likes Bob, but Bob did not report that he likes

Carol. In the present study, since there were only 51 participants, connection data for the other

410 was not considered. In other words, if a participant reported a connection to another

musician, the tie between them was considered a symmetric, as opposed to the asymmetry

illustrated in the example above. In addition, certain properties could not be computed since the

study only provided data for 51 actors in the one-mode network.46 In addition, participants were

not asked to specify ties between each of their collaborators, as is often the case in ego network

studies (Hanneman & Riddle, 2005). This methodological choice significantly reduced the length

and cognitive effort of the task; however, it resulted in hundreds of “non-ties” or no relations

46 A one-mode network studies “a single set of actors,” and data are collected on each actor in the network (Wasserman & Faust, 1994). The present study assumed a single set of actors (n = 461), but only collected data on 51 of those actors, instead of collecting data on all 461 of the actors in the network.

147 between actors that might, in reality, be connected. To get around these complications, each

relation was entered as nondirectional, assuming a set of symmetric, mutual ties between actors,

regardless of their participation in the study. Although these methodological choices resulted in

less accurate information, it was the best option to make the task short and easy for participants

to understand.

The 461-by-461 matrix was imported into the software programs UCINET (Borgatti et

al., 2002) and Netdraw (Borgatti, 2002) to calculate and draw the network properties. General

network characteristics, such as geodesic counts and correlations, were computed in UCINET to

determine the likelihood of information exchange between musicians. The geodesic count

function estimates the path length47 between two actors; thus, a smaller geodesic count suggests

frequency of information exchange. These values were added together and averaged to produce a

geodesic count for the network as a whole. Standard deviations of the geodesic counts were also

considered as indicators of relative agreement between actors in the network.48 Another measure

of network characteristics, the correlation function, calculates a Pearson product moment

correlation49 for patterns of relations between pairs of actors. With respect to the network, a

positive correlation suggests a strong agreement in patterns of relations between actors (or a

large amount of overlap in ties), whereas a negative correlation implies a weak agreement in

patterns of relations between actors (or little to no overlap in ties) (Hanneman & Riddle, 2005).

47 Wasserman and Faust (1999) define a path as that “which all nodes and all lines are distinct,” and a path length as the summation of relations that make up the path (p. 107). 48 Hanneman and Riddle (2005) state that the standard deviation in geodesic distances shows “how far each actor is from each other as a source of information for the other; and how far each actor is from each other actor who may be trying to influence them” (p. 110). 49 Developed by Karl Pearson (1896), this method of analysis was explained as a mathematical indication of similarity between two variables. The “r value” represents a product of summed deviations from the average. A positive correlation (r = 0 to 1.00) specifies a direct association in systematic changes in both variables, whereas a negative correlation (r = -1.00 to 0) indicates an opposing association. In the case of no correlation (r = 0), changes in the two variables are not significantly related.

148 Links between musicians were analyzed to ascertain two network-dependent

characteristics for each participant. The first characteristic, cluster, was determined by using two

methods of subgroup analysis: the Hierarchical Clustering (HC) function in UCINET and the

Girvan-Newman (GN) clustering algorithm in Netdraw. The similarity index provided by a

clustering analysis is determined by looking at shared paths, or summed distances, between

nodes; thus, the algorithm focuses on the connections within a given cluster (Girvan & Newman,

2002). HC analysis illustrates “agglomerative hierarchical clustering of nodes on the basis of

similarity of their profiles of ties to other cases” (Hanneman & Riddle, 2005, p. 205). The

clustering profile starts by including each node in a separate cluster, next compiles the nodes

with the highest index of similarity into the next cluster, and continues until all nodes are

contained within one cluster. On the other hand, the GN algorithm takes into account shared

paths, as well as betweenness measures, or the relations between clusters. Girvan and Newman

(2002) tested the algorithm on both artificial and real-world communities and found it to be more

accurate than the hierarchical clustering model. In the present study, the clustering algorithm

produced diagrams from the network graph, which were visually rendered by the graphic

representation program, Netdraw (Borgatti, 2000). The HC and GN algorithms will be compared

to better understand the categorical aspects of community affiliation for each participant in the

study.

Density, the second network characteristic, was determined by using the ego-network

density function in UCINET. Wasserman and Faust (1994) define density as “the average

proportion of lines incident with nodes in the graph” (p. 102). Typically, measures of density

consider the observed ties in relation to expected ties in a given cluster, so this ratio reliably

indicates the extent to which each actor is a part of a cohesive cluster. In the present study,

149 participants provided a limited amount of information on the relationships between

themselves and 20 other musicians. Due to this constraint, additional potential relations between

actors in the present network cannot be considered; thus, the density measure is limited due to

this study’s sampling method. Further interpretation of the density results requires this to be

taken into consideration.

The second part of the analysis procedures dealt with the discussion and friendship

ratings. Traditional methods using Likert scales treat such data as either nominal or ordinal

variables (Likert, 1932). However, several researchers have suggested that such scales are a good

indication of response strength, supporting use of these scales as a continuous, interval measure

(Lubke & Muthen, 2004; Kline, 2005). Since both Likert scales used here were anchored by two

bipolar descriptors, the ratings were treated as continuous, and thus used to calculate

relationships between the discussion and friendship ratings. Using the Statistical Package for the

Social Sciences50 (SPSS), Pearson product-moment correlation coefficients were calculated to

determine the interdependency of discussion and friendship ratings. Similar correlation analyses

were carried out for the association and matching tasks.

Self-ratings of affiliation and social influence were approached in a manner similar to the

collaborator ratings. Means, standard deviations, and correlation coefficients for these Likert

scale ratings were calculated. Two tests, the t-test51 and analysis of variance52 (ANOVA) were

50 All statistical procedures were analyzed with SPSS software. 51 William Gosset (pen name “Student”) formulated the Student’s t-test to calculate differences in two means for an observed variable (Moore & McCabe, 1999). Fisher (1925) was the first to formally recommend the Student’s t-test for comparison of means that were drawn from the same population. Independent t-tests were used to test the statistical differences in means between two groups. 52 Two variations of the ANOVA, the one-way and two-way, calculate main and interaction effects, respectively, of variables. Each variation provides several values, the F-statistic, sum of squares, mean square error, and the p-value. A higher F value indicates a bigger difference in variation between groups, and a lower p-value strengthens both the probability of the test’s correctness and rejection of the null

150 used to assess the difference in means between cluster groups. Independent t-tests53 were

used to test the statistical differences in means between two groups, whereas ANOVAs were

used to test differences between three or more groups.

Finally, participant attributes were compared to network properties in order to better

define the features that distinguish one group from another. Table 4.1 shows the ranges and

categories for the nine attributes54 included in the analyses for the collaborator, association, and

matching tasks.

Table 4.1: Participant Attributes

hypothesis. If the p-value is less than .01, a post-hoc test, or multiple comparisons analysis can be used to view detailed differences between means. The least-significant differences (LSD) provided a post hoc test of significance for each pair of means in the sample. Moore and McCabe (1999) warn against the LSD test in the case of larger samples, since it results in a higher error rate; however, the test suits the needs and characteristics of the present analysis. 53 This version of the t-test compares means and standard deviations for two independent groups, or “samples,” and estimates the robustness of the differences (Moore & McCabe, 1999). It tests the “null hypothesis,” or the extent to which the two groups have similar means. A positive t-value indicates that the first mean of the pairs is larger than the second, and vice versa for a negative t-value. To assess the robustness of the test, Moore and McCabe (1999) recommend the p-value, a “probability…that the test statistic would take a value as extreme or more extreme than that actually observed” (p. 458). Typically, smaller values (0.01 to 0.05) indicate a higher statistical validity of the test of significance; thus, with a p value equal to or less than 0.05, the null hypothesis has a more valid and reliable chance of being rejected. 54 The nine attributes included: age, instrument, years of experience, education, preferred performance style, HC group, GN cluster, network density, and community affiliation judgment. Although results from the collaborator task were only compared to the first 5 attributes, the other tasks considered all nine of the attributes.

Age (Yrs) Instrument Exp (Yrs) Education Performance Style HC Group GN Cluster Density Comm Aff

22 to 61 bass clarinet 8 to 45 high school (HS) jazz (J) 1 0 0 to 38.1 2 to 5bass undergraduate (U) jazz and other (JO) 2 1cello graduate (G) jazz and improvised music (JIM) 3 2drums improvised music (IM) 4 3guitar 5pianosaxophonetrombonetrumpetvibraphonevoice

151 Non-categorical attributes were recoded into 2 groups so that the variables would be more

easily used in group comparisons. For the same reason, attributes with more than 4 groups were

condensed into 2-4 groups (table 4.2).

Table 4.2: Attribute Recoding55

An effort was made to distribute participants equally across groups; however, for education,

hierarchical clustering (HC) group, Girvan-Newman (GN) cluster, and community affiliation,

groups were relatively unequal. Although some statisticians warn that mean comparison tests

with unequal groups is less reliable, the tests can still provide a relatively reliable indication,

although less robust, of variance statistics (Moore & McCabe, 1999). To assess interrelations

between network properties and participant attributes, the data were subjected to cross-tabulation

comparisons.56 Since the data are treated as nominal and categorical, the nonparametric Chi-

55 Instruments were grouped into two categories: melodic and rhythm section. These groupings refer to the role each instrument plays in a typical instrumental performance. Bass-clarinet, cello, saxophone, trombone, trumpet, and voice were coded as melodic instruments, while bass, drums, guitar, piano, and vibraphone were coded as rhythm section instruments. 56 The cross-tabulations function analyzes categorical data by providing a summary of categorical distribution for all outcomes in a contingency matrix. Typically, the cross-tabulation procedure assesses the causal relation between an independent (row) and a dependent (column) variable (Hellevik, 1988). Values in the table show the percentage of cases common to both variables.

Age (Yrs) Inst Exp (Yrs) Ed Perf. Style HC Group

GN Cluster Density Comm

Aff!30 (26) melodic (23) !18 (25) HS (8) J (21) 1 (22) 1 (24) !10 (26) >3 (31)<30 (25) rhythm (28) <18 (26) U (29) JO (18) 2 (15) 2 (16) <10 (25) "3 (20)

G (14) JIM (12) 3 (10) 3 (10)4 (3)

152 square test for independence57 was used. In addition, both t-tests and ANOVAs were used to

measure agreement score differences between groups for each attribute variable.

Results

The network included 1896 ties between 461 actors (figure 4.2), but only the connections

for the 51 participants are shown in the results (tables 4.3-4.8). The results indicated an average

geodesic distance of 4.03 for the 461 actors in the overall network and of 2.30 for the 51

participants in the study. Larger geodesic distances indicate longer shortest-distance paths, while

geodesic counts indicate the number of geodesic distances between two participants. For

example, a geodesic count of 64 connected JK and RS; thus, they were 64 paths to connect them,

given the data collected. Table 4.3 depicts the range (1 to 64) of geodesic counts58 for the 51

participants in the form of a matrix, while table 4.4 shows the range (0 to 6) of geodesic distance.

The average degree between participants was 60.71 (SD = 17.43), much larger than that for the

overall network, an average distance of 11.38 (SD = 19.11). The overall degree-degree

correlation coefficient59 between participants was r = 0.04, with a range of -0.05 to .70 (table

4.5). These values confirmed that none of the participants had identical collaborator lists.

Nonetheless, the range of correlations was still useful for viewing what similarities do exist

between patterns of ties. For example, the actor KJ had similar ties to actors JK (r = .70), JB (r =

57 The Chi-square test, also developed by Karl Pearson (1900), was designed to discern the difference in distribution of categories for two variables (Moore & McCabe, 1999). Thus, the test requires category overlap between variables. A larger difference in distributions between the two variables results in a larger value of the Chi-square statistic (X2). Similar to the t-test and ANOVA, reliability of the Chi-square test is estimated with a p-value. 58 The geodesic count procedure provides “the number of shortest paths connecting all pairs of vertices (Borgatti et al., 2002). 59 Social network analysts define degree as a measure of the number of ties to other actors in the network (Wasserman & Faust, 1994; Hanneman & Riddle, 2005).

153 .71), JS2 (r = .76), MR (r = .62), FLM (r = .60), TD (r = .47) and AH (r = .47). The actor

MG had similar ties to actors JG1 (r = .57), QK (r = .54), and JD1 (r = .51).

The HC algorithm produced 216 iterations of clustering agglomerations, of which only

five are shown in table 4.6. The numbers in each column of the table are therefore arbitrary, as

they simply represent a new grouping. In other words, there is no relationship between cluster 1

in stage 1 and cluster 1 in stage 150. The 5 iterations shown in the table represent significant

breakpoint stages, where a select number of participants are converged into persistent clusters.

At stage 1, none of the participants were grouped into the same cluster, resulting in 51 different

clusters. However, at stage 150, there were only a total of 30 clusters, since several participants

belonged to clusters 4, 16, and 17. In this stage, it was also apparent that actors JS1 and RK were

the only participants in cluster 18, and actors GB and JH were alone in cluster 17. These cluster

patterns illustrate the stability in node relations between these pairs of respondents. Stage 200

revealed an even smaller number of clusters (n = 12), three apparent pendants60 (AU, CB, CG),

and two larger clusters (1 and 5). Stage 211 specified only 5 clusters (1 (n = 22), 2 (n = 15), 3(n

= 10), 4(n = 3), and 5(n = 1)), the last of which is a pendant participant (CG). The final clustering

stage grouped all participants into one component. GN cluster results were somewhat different

from the hierarchical clustering results, as depicted in table 4.7. The GN algorithm provided 9

stages of partitioning, but only partitions 10, 8, 5, 3, and 261 are shown in the table. Only one

participant was labeled as a pendent (CB), thus, he was left out of each cluster. Finally, the

following differences between the two clustering algorithms were observed: AK (HC 3 to GN1),

BT and JG2 (HC 1 to GN 2), CB (HC 4 to GN 0), CG (HC 5 to GN 1), DC and AB (HC 4 to GN

2), and JS3, SM, and TF (HC 2 to GN 1). These disparities illustrate that these two methods do 60 Pendants are nodes who are only connected to the network by 1 link (Hanneman & Riddle, 2005). 61 Partition 1 is typically excluded from the Girvan-Newman clustering algorithm.

154 indeed produce different results that should be noted in the analysis stage. Figure 4.3

illustrates the three main GN clusters.

The overall density of the 461-node network was 0.03 (SD = 0.29), indicating that only

3% of possible links were present in the data from the 51 participants. This is not surprising,

given the lack of data collected for the remainder of nodes in the network. To provide a better

indication of relationships among the actors in the sample, the matrix was therefore revised to

include only the 51 participants. After this revision, the density for the matrix was 0.37 (SD =

1.03). Table 4.8 shows the density values for each participant in the sample, the average being

12.91 (SD = 10.73). As expected, the standard deviation and range of density values were large,

due to the relatively few participants in the sample. Nevertheless, the density values summarized

the number of connections.

The mean discussion ratings (M = 2.77, SD = 1.27) were lower than the mean friendship

ratings (M = 3.49, SD = 1.13), t(1019) = -21.53, p < .001. The correlation between the discussion

and friendship ratings was moderately strong, r = 0.61(1018), p = .01, suggesting that these

musicians tended to discuss music with collaborators with whom they were friends.

The participants’ self-ratings of their musical community affiliations were moderately

higher (M = 3.80, SD = 1.06) than their ratings for social influence (M = 2.90, SD = .85), t(50) =

5.67, p < .001. One-way ANOVAs for community affiliation judgments yielded main effects for

both HC and GN clusters, F(3, 46) = 2.88, p = .04 and F(2, 47) = 3.32, p = .04, respectively.

155 Table 4.9: Community Affiliation Groups by HC Groups ANOVA

Table 4.10: Community Affiliation Groups by GN Clusters ANOVA

Post hoc tests indicated higher affiliation ratings for HC group 3 (M = 5.00, SD = 0) and GN

cluster 3 (M = 4.56, SD = .73), compared to HC groups 1 (M = 3.55, SD = 1.06), 2 (M = 3.60, SD

= .99) and 4 (M = 4.30, SD = 1.06), as well as GN groups 1 (M = 3.56, SD = 1.00) and 2 (M =

3.69, SD = 1.14). In addition, there was a positive correlation between community affiliation

judgments and density values, r = .33(49), p = .02. Neither HC nor GN clusters affected social

influence ratings (p > .80), and a correlation analysis found no relationship between social

influence ratings and density values (p > .40).

Finally, the cross-tabulations revealed several significant relationships between network

properties and the 5 participant attributes. First, a larger number of younger participants were in

HC group 1, while a larger number of older participants were in HC group 2, X2 (3, N = 50) =

8.55, p = .04.

Sum of Squares df Mean Square F Sig.

Between Groups 8.85 3 2.95 2.88 0.04Within Groups 47.15 46 1.03Total 56.00 49

ANOVA

Sum of Squares df Mean Square F Sig.

Between Groups 6.76 2 3.38 3.32 0.04Within Groups 47.82 47 1.02Total 54.58 49

ANOVA

156 Table 4.11: Age Groups by Network Properties Cross-tabulations

Likewise, there were more participants under age 30 in GN cluster 1 and over age 30 in GN

cluster 2, X2 (2, N = 50) = 12.03, p = .002. The relationship between age group and density group

approached significance, X2 (1, N = 51) = 3.33, p = .06, with younger participants in the high-

density group and older participants in the low-density group. An equal number of participants in

the two experience groups were found for the HC groups; however, distribution of experience

groups across the GN clusters was not equal, X2 (2, N = 50) = 6.07, p = .05.

Table 4.12: Experience Groups by GN Cluster Cross-tabulation

Those participants with more experience were in GN cluster 2, and those with less experience

were in cluster 1; the spread for GN cluster 3 was about equal. A chi-square analysis showed no

relationship between density group and experience (p = .12). Cross-tabulations indicated equal

distributions of education and instrument groups for all network properties (p > .40). The

distribution of preferred performance styles was unequal for HC groups and GN clusters, X2 (6,

N = 50) = 23.35, p = .001 and X2 (4, N = 50) = 25.67, p < .001, respectively.

Age 1 2 3 4 1 2 3 H L! 30 24.0% 44.0% 24.0% 8.0% 26.9% 50.0% 23.1% 38.5% 61.5%< 30 64.0% 16.0% 16.0% 4.0% 75.0% 12.5% 12.5% 64.0% 36.0%

Density GrpHC Group GN Cluster

Exp 1 2 3H 36.0% 48.0% 16.0%L 64.0% 16.0% 20.0%

GN Cluster

157 Table 4.13: Network Properties by Preferred Performance Style Groups Cross-tabulations

There were more participants who primarily performed jazz (J) and jazz and other (JO) in HC

groups 1 and 2, as well as in GN groups 1 and 2, whereas those who performed jazz and

improvised music (JIM) tended to be in both HC group 3 and GN cluster 3. Finally, participants

with higher density values were associated with performance styles J and JIM, while those with

lower density values were associated with J and JO, X2 (2, N = 51) = 11.92, p = .003.

Summary of Results

Despite the study’s sampling constraints, an analysis of collaborator lists revealed 3 to 4

distinct clusters of musicians which were highly related. Participants in HC groups 1-3 and GN

clusters 1-3 matched, with the exception of the 10 differences mentioned above. Upon closer

examination, a relationship is also apparent between these clusters and the geodesic count and

correlation values, such that participants in the same clusters were closely connected and highly

interrelated. Furthermore, participants in HC group 3 and GN cluster 3 appear to be the most

closely connected, and thus had higher density values. Participants’ self-ratings of affiliation and

influence related significantly to these measures as well. The clusters were thus reliably

characterized by age, preferred performance style, and density of connections.

Perf Style 1 2 3 4 1 2 3 H LJ 45.0% 40.0% 10.0% 5.0% 57.1% 38.1% 4.8% 47.6% 52.4%JO 55.6% 33.3% .0% 11.1% 58.8% 41.2% .0% 27.8% 72.2%JIM 25.0% 8.3% 66.7% .0% 25.0% 8.3% 66.7% 91.7% 8.3%

HC Group GN Cluster Density Group

158 Association Task

Overview

The association task provided both qualitative (categorical) and quantitative (continuous)

data for the cognitive processing of 15 typical excerpts. Participants heard each excerpt, listed

three musicians’ names which the excerpt brought to mind, and provided their criteria for citing

each name. After identifying the soloist, the respondents next rated the excerpt on how well it

represented the performer (typicality) and the extent to which the performer has influenced their

own music (influence). The following results will reveal differences in the participants’

responses to the task, which were markedly affected by both the excerpts and participant

attributes.

Analysis Procedures

The association task resulted in three categorical data variables. Name association

referred to the musician associated with the excerpt, instrument association referred to that

which the named musician plays most frequently, and association criteria corresponded to

participants’ self-reflections on the strategies used during the task. Where needed, the name

associations were corrected for spelling errors, and the results from all participants were

summed. The instrument variable (table 4.14) was coded by referring to biography profiles in

jazz history texts (Gioia, 1997; Martin & Waters, 2002) and online jazz resources (All Music

Guide, Access Date March 2009; All About Jazz, Access Date March 2009).

159 Table 4.14: Instrument Codes

Respondents’ self-reported association criteria were labeled by two independent coders to ensure

accuracy and reliability. Specially-developed coding guidelines (see table 4.15 for a summary)

were used for consistency in the coding phase for the two coders. Table 4.15 illustrates the

categories that were used across the two coding phases, including the two categories that were

excluded in phase two.62

62 Geography was excluded from phase two because it was only used as an explanation for the Louis Armstrong excerpt, and role was eliminated because it was only used to choose names for the Miles Davis excerpt.

Instrument Code

Big Band bbBanjo bjBass bsClarinet clCello cloComposer cmpDrums dmsGroup grpGuitar gtrNon given ngNonmusician nmPiano pnoSaxophone saxTrombone tbTrumpet tptVibraphone vbVoice vc

160 Table 4.15: Criteria Coding Strategies

In the second phase of coding, geography was incorporated into the style category, and role was

incorporated into other.63 Inter-coder reliability was calculated using Cohen’s kappa (Dewey,

1983; Lombard et al., 2002). The Cohen’s kappa measure of agreement revealed a value of .75 (p

< .01), indicating a reliable level of coding agreement (Lombard et al., 2002).

Many free association studies recode associations into frequency scores and probability

percentages to prepare the data for statistical analysis (Palermo & Jenkins, 1964; Jenkins &

Palermo, 1965; Nelson & McEvoy, 2000). Here, frequency scores were calculated for each

association variable (name, instrument, criteria) by counting the number of category

appearances, and percentages were determined by dividing the frequency of occurrence by the

number of responses (n = 153). The frequency score data were analyzed with one-way ANOVAs

to assess between-excerpt differences. To look at the distribution of instrument categories within

the three variables, the data were also analyzed with cross-tabulations. Chi-square values were

calculated to determine the reliability of these procedures. 63 Participants generally referred to “New Orleans” (geography) when describing the style of the Armstrong excerpt. Since role was only used to describe the Davis excerpt, it was recoded as other.

Criteria Code Defining Features PhaseApproach apr Abstract qualities of music 1 & 2Collaboration clb Musical, personal, contemporary 1 & 2Geography geo Geographical area 1Influence Given infg Person influenced excerpt musician 1 & 2Influence Received infr Person influenced by excerpt musician 1 & 2Instrument inst Legacy of instrument 1 & 2Musical mus Concrete qualities of music 1 & 2None ng None provided 1 & 2Other oth Admirer, personal lifestyle 1 & 2Personal Memory prs Autobiographical experiences 1 & 2Role ro Role in ensemble 1Style sty Conventionalized style label 1 & 2Time Period tm Decade, year mentioned 1 & 2

161 The second phase of analysis reformatted name associations, instruments, and criteria

into ratio-type, continuous data. Some studies have shown quantification of categorical variables

to be useful for additional statistical analyses (Nelson et al., 2000). In particular, agreement

scores were calculated by adding together the three frequency scores for each variable. If a

participant listed names, instruments, or criteria common to the rest of the sample, he received a

larger agreement score. One-way ANOVAs were used to assess the differences in participants’

agreement scores between excerpts. These procedures aimed to answer the following questions:

(a) how typical are the name associations, instruments, and criteria for each excerpt; and (b) do

these differ between excerpts? By viewing the data in this way I hope to discern patterns of

agreement among participants.

The purpose of the third phase of analysis was to ascertain the relationship between

association task responses and participant attributes. Nine attributes were considered in relation

to the association task data (Table 4.2).64 To evaluate the effect of participant attributes on

categorical data for instruments and criteria, the data were analyzed with the Chi-square

comparison in cross-tabulations. Differences in continuous data, including frequency and

agreement scores, were analyzed with between-group t-tests and ANOVAs.

The final phase of analysis considered the typicality and influence ratings, as well as the

accuracy of soloist identification for each excerpt. Since the typicality and influence ratings were

collected using endpoint-defined Likert scales, the variables were treated as continuous data.

Identification accuracy was coded as a binary variable; participants correctly or incorrectly

guessed the identity of the excerpt’s soloist. Means and standard deviations were calculated for

the entire sample, and one-way ANOVAs were used to provide an indicator of differences

64 These were the same attributes that were presented in the collaborator task.

162 between the 15 excerpts. In addition, two-way ANOVAs assessed the interaction of typicality

and influence ratings on their effect on accuracy. Finally, Pearson correlations estimated the

relationship between the typicality and influence ratings as well as agreement scores. The

influence of accuracy and excerpt on agreement scores was determined with a two-way

ANOVA.

Results: Categories, Frequency and Agreement Scores

The association task was completed by all (n = 51) of the participants in the study. A total

of 592 (M = 67.2; range 56 to 79) musicians were named during the task, yielding a duplicate

rate of 74.20 percent. Only 2.40%, or 55 out of the 2,295 associations, were left blank. Appendix

A includes the frequency counts and percentages for all listed musician names. Table 4.16

following, presents the names which were listed by at least 5 participants.65 In addition, figures

4.4-4.18 depict the association networks for each musician’s excerpt.

65 Ng means that there was no name provided for that particular excerpt.

163 Table 4.16: Name Associations with Frequency Scores ! 5.

Louis Armstrong Ornette Coleman John Coltrane Miles DavisWynton Marsalis (13) Don Cherry (16) Tommy Flanagan (12) Bill Evans (19)King Oliver (13) Charlie Haden (16) Sonny Rollins (11) John Coltrane (15)Roy Eldridge (7) Charlie Parker (9) Elvin Jones (10) Paul Chambers (11)Sidney Bechet (6) Dewey Redman (7) Michael Brecker (7) Jimmy Cobb (9)NG (6) Ornette Coleman (5) McCoy Tyner (7) Cannonball Adderley (8)Bix Beiderbecke (6) Wayne Shorter (6) Wallace Roney (6)Baby Dodds (5) Freddie Hubbard (6)Louis Armstrong (5) Wayne Shorter (5)

Art Farmer (5)Duke Ellington Herbie Hancock Coleman Hawkins Billie Holiday

Count Basie (16) Tony Williams (13) Lester Young (23) Ella Fitzgerald (24)Billy Strayhorn (14) Ron Carter (11) Ben Webster (12) Lester Young (20)Johnny Hodges (10) Ron Perrillo (11) Sonny Rollins (10) Sarah Vaughn (14)Cootie Williams (7) Chick Corea (10) Charlie Parker (7) Carmen McRae (5)Thelonious Monk (6) Miles Davis (7) Dexter Gordon (7) Louis Armstrong (5)Duke Ellington (5) Bill Evans (6) Johnny Hodges (6) Madeline Peyroux (5)

Wynton Kelly (6) NG (6)Brad Mehldau (5) Count Basie (5)Bud Powell (5) Stan Getz (5)Keith Jarrett (5)NG (5)Wayne Shorter (5)

Charles Mingus Thelonious Monk Wes Montgomery Charlie ParkerPaul Chambers (11) Art Tatum (9) Grant Green (18) Dizzy Gillespie (17)Ray Brown (11) John Coltrane (9) Bobby Broom (11) Sonny Stitt (15)Oscar Pettiford (7) Charlie Rouse (8) Jim Hall (9) Charlie Parker (8)Ron Carter (7) Duke Ellington (6) Charlie Christian (8) Max Roach (8)Dannie Richmond (6) Ron Perrillo (6) Jeff Parker (7) Bud Powell (6)Sam Jones (6) Chick Corea (5) George Benson (6) Miles Davis (6)Charles Mingus (5) Miles Davis (5) Wes Montgomery (6) Ornette Coleman (5)Eric Dolphy (5) Kenny Burrell (5)Jimmy Blanton (5) Pat Martino (5)

Jaco Pastorius Max Roach Sonny RollinsJoe Zawinul (14) Art Blakey (13) Jim Hall (11)Wayne Shorter (14) Max Roach (13) John Coltrane (11)Herbie Hancock (8) Elvin Jones (11) Bobby Broom (8)Pat Metheny (8) Philly Joe Jones (11) Coleman Hawkins (7)Chick Corea (5) Tony Williams (10) Hank Mobley (5)John Patitucci (5) Buddy Rich (6)Miles Davis (5) George Fludas (5)

164 Since the categories (names) varied significantly between excerpts, frequency score cross-

tabulations were deemed inappropriate for statistical comparison.

The average agreement score for name associations was 17.02 (SD = 10.43), as shown in

table 4.17.

Table 4.17: Name Association Agreement Scores

ANOVA results showed a main effect of excerpt on the name agreement score (table 4.18).

Table 4.18: Excerpts by Name Association Agreement Score ANOVA

Sum of Squares df Mean Square F Sig.

Between Groups 13497.60 14 964.11 10.39 0.00Within Groups 69605.02 750 92.81Total 83102.62 764

ANOVA

Excerpt M Agreement Score SDBillie Holiday 27.71 14.26Miles Davis 21.90 13.54Coleman Hawkins 21.90 11.44Max Roach 18.14 10.27Wes Montgomery 17.90 9.02Charlie Parker 17.78 10.43Herbie Hancock 17.27 8.35Ornette Coleman 16.84 11.24Duke Ellington 16.53 9.44Jaco Pastorius 15.63 9.70Louis Armstrong 13.94 6.71John Coltrane 13.71 6.47Charles Mingus 13.14 6.74Sonny Rollins 12.10 6.71Thelonious Monk 10.84 4.77Mean 17.02 10.43

165 In general, agreement scores for the Monk excerpt were lower than for most of the other

excerpts, while those for the Holiday, Davis, and Hawkins excerpts were significantly higher

than others.

Musicians associated with the excerpts played a total of 18 different instruments (table

4.19).

Table 4.19: Instrument Association Frequency Scores66

An average of 9.87 (SD = 1.55) instruments characterized the name associations. By far, the

largest number musicians named by participants played the saxophone (647), while the fewest

played the banjo (1) and violin (1). A between-groups (instruments) ANOVA yielded a main

effect of instrument on frequency score (table 4.20), showing an unequal distribution of

instrument responses for the task. 66 LA (Louis Armstrong), OC (Ornette Coleman), JC (John Coltrane), MD (Miles Davis), DE (Duke Ellington), HH (Herbie Hancock), CH (Coleman Hawkins), BH (Billie Holiday), Charles Mingus (CM), Thelonious Monk (TM), Wes Montgomery (WM), Charlie Parker (CP), Jaco Pastorius (JP), Max Roach (MR), Sonny Rollins (SR).

Inst LA OC JC MD DE HH CH BH CM TM WM CP JP MR SR TotalSaxophone 12 79 97 32 25 10 113 28 10 32 8 81 19 5 96 647Piano 15 4 24 26 33 85 7 9 3 94 6 17 33 1 1 358Bass 2 22 8 11 8 18 6 1 118 6 0 10 68 1 11 290Trumpet 91 25 5 56 14 11 2 13 4 5 3 28 6 7 6 276Drums 6 11 13 16 5 18 2 1 11 3 7 10 5 133 9 250Guitar 1 4 1 3 2 3 1 2 1 5 121 2 14 0 24 184Voice 5 3 0 1 8 1 4 93 0 1 0 2 1 0 1 120None Given 6 4 4 3 3 5 6 3 2 1 4 2 4 4 4 55Big Band 0 0 0 0 31 0 9 3 1 0 0 0 0 0 0 44Composer 1 0 0 4 14 0 0 0 0 4 0 0 2 0 1 26Trombone 4 1 1 0 7 0 0 0 2 0 1 0 0 1 0 17Clarinet 7 0 0 0 3 0 2 0 0 0 0 0 0 0 0 12Vibraphone 1 0 0 1 0 2 0 0 0 2 1 0 0 0 0 7Group 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 3Cello 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 2Nonmusician 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2Banjo 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1Violin 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1

166 Table 4.20: Instrument Associations by Frequency Scores ANOVA

Post hoc tests showed significantly larger frequency scores for saxophone, piano, bass, and

trumpet (p < .05) and likewise, lower scores for banjo, cello, violin, nonmusician, and group (p <

.05). The Chi-square test for independence showed that the distribution of instruments differed

significantly between excerpt, X2 (238, N = 2295) = 5744.52, p < .01. Specifically, the instrument

of the soloist in the excerpt matched the instrument with the highest frequency score for each

excerpt. For example, most of the musicians named for the Armstrong and Davis excerpts played

the trumpet, the instrument played by both of these performers on their respective excerpts. This

effect was most evident for the Roach excerpt, with an overwhelming count of 133 drummers

listed.

The average agreement score for instrument association was 192.80 (SD = 78.27), as

shown in table 4.21.

Sum of Squares df Mean Square F Sig.Between Groups 35422.03 17 2083.65 5.70 0.00Within Groups 92071.47 252 365.36Total 127493.50 269

ANOVA

167 Table 4.21: Instrument Association Agreement Scores

One-way ANOVA results revealed a main effect of excerpt on agreement scores (table 4.22).

Table 4.22: Instrument Association Agreement Scores by Excerpts ANOVA

The instrument agreement scores for the Roach, Montgomery, Mingus, and Hawkins excerpts

were higher than the other agreement scores (p < .01). Conversely, patterns of agreement were

lower for the Ellington and Davis excerpts (p < .01).

All 11 of the criteria categories were used for 13 out of the 15 excerpts (M = 10.87, SD =

.35); approximately 6.88% (n = 158) of criteria responses were left blank. This finding,

supported by post-study interview comments, suggested either that participants did not follow

Sum of Squares df Mean Square F Sig.Between Groups 4034424.92 14 288173.21 43.19 0.00Within Groups 5004712.08 750 6672.95Total 9039136.99 764

ANOVA

Excerpt M Agreement Score SDMax Roach 348.69 99.56Wes Montgomery 290.57 100.44Charles Mingus 278.06 117.98Coleman Hawkins 254.92 93.86Sonny Rollins 204.10 90.11John Coltrane 201.20 91.32Thelonious Monk 195.63 81.15Billie Holiday 190.33 69.49Louis Armstrong 172.96 78.16Herbie Hancock 159.47 85.96Charlie Parker 153.86 72.26Ornette Coleman 147.63 71.31Jaco Pastorius 127.12 58.83Miles Davis 102.92 41.81Duke Ellington 64.53 21.78Mean 192.80 78.27

168 the instructions, or perhaps thought that their reasoning strategies were self-explanatory.

Table 4.23 shows these scores.

Table 4.23: Criteria Frequency Scores

The majority of the responses were based on musical (470), approach (434), and collaboration

(407) criteria. A one-way between-groups ANOVA indicated a main effect of criterion on

frequency score (F(10, 154) = 40.71, p < .001), as shown in table 4.24.

Table 4.24: Association Criteria Frequency Score by Excerpts ANOVA

Post hoc tests showed significantly higher frequencies for musical, approach, and collaboration

criteria when compared to other criteria, but not when compared to each other (p < .001). The

least frequently used criteria were personal memory, influence given, and other (p < .01). Chi-

Sum of Squares df Mean Square F Sig.

Between Groups 18140.84 10 1814.08 40.71 0.00Within Groups 6862.80 154 44.56Total 25003.64 164

ANOVA

Criteria LA OC JC MD DE HH CH BH CM TM WM CP JP MR SR TotalMusical 26 27 33 20 18 34 38 33 51 29 40 30 18 31 42 470Approach 21 38 18 29 17 31 21 31 32 35 32 23 39 37 30 434Collaboration 18 38 34 56 37 36 16 22 15 24 11 31 29 12 28 407Influence Received 15 12 37 15 17 11 26 17 8 31 15 20 11 12 14 261Style 39 15 8 7 13 11 20 6 7 12 17 22 21 14 5 217Instrument 4 9 5 6 21 6 6 17 16 5 17 9 14 20 9 164None Given 12 9 9 12 11 14 11 7 8 4 11 9 11 17 13 158Time Period 10 1 2 1 8 4 10 6 2 1 1 5 4 4 3 62Personal Memory 1 2 2 2 7 1 1 8 6 6 2 1 2 3 4 48Influence Given 5 1 3 2 1 5 1 2 5 4 5 2 2 3 2 43Other 2 1 2 3 3 0 3 4 3 2 2 1 2 0 3 31

169 square comparisons revealed strong differences in category distribution between excerpts, X2

(140, N = 2295) = 403.00, p < .001, suggesting a highly significant effect of excerpt on

reasoning strategies.

The average criteria agreement score was 74.41 (SD = 27.37), as shown in table 4.25.

Table 4.25: Criteria Agreement Scores

The ANOVAs on these criteria yielded a highly significant main effect of excerpt on agreement

scores for criteria (table 4.26).

Table 4.26: Association Criteria Agreement Scores by Excerpts ANOVA

Sum of Squares df Mean Square F Sig.

Between Groups 55149.80 14 3939.27 4.71 0.00Within Groups 627813.49 750 837.09Total 682963.29 764

ANOVA

Excerpt M Agreement Score SDMiles Davis 95.08 56.55Charles Mingus 85.43 47.37Ornette Coleman 81.47 24.60John Coltrane 80.96 26.71Sonny Rollins 77.59 29.55Herbie Hancock 77.04 29.60Thelonious Monk 75.39 16.14Wes Montgomery 72.61 22.71Jaco Pastorius 70.45 21.80Max Roach 69.35 25.89Coleman Hawkins 68.33 22.74Louis Armstrong 68.18 27.51Charlie Parker 67.98 13.70Billie Holiday 65.04 23.61Duke Ellington 61.27 22.13Mean 74.41 27.37

170 The criteria responses for the Davis, Mingus, Coleman, and Coltrane excerpts were more

similar; thus, responses to these excerpts resulted in higher agreement scores than responses to

other excerpts (p < .05). The patterns of criteria agreement were lowest for Ellington, Holiday,

Parker, and Hawkins excerpts, but with fewer differences between the means (p < .05).

Participant Attribute Effects

Many of the participant attributes influenced category distribution and agreement scores

for the three association variables. Age group differences were observed for both instrument

association (X2 (3, N = 2295) = 22.35, p < .001) and criteria category distribution (X2 (10, N =

2295) = 79.85, p < .001). Table 4.27 shows the differences in category distribution for

instrument associations, while table 4.28 shows age group as related to association criteria.

Table 4.27: Age Groups by Instrument Associations Cross-tabulation

Table 4.28: Age Groups by Association Criteria Cross-tabulation

When the tests were run for each excerpt, the age group differences for instruments associated

with the Ellington excerpt were especially pronounced. With respect to the criteria variable, age

Age Melodic None Given Other Rhythm Section! 30 years 44.6% 3.3% 3.6% 48.5%< 30 years 46.5% 1.2% 6.6% 45.7%

Instrument Association

Age apr clb infg infr inst mus ng oth prs sty tm! 30 years 18.7% 18.8% 2.7% 12.1% 6.6% 15.0% 9.8% 1.5% 2.1% 9.8% 2.9%< 30 years 19.1% 16.6% 1.0% 10.6% 7.7% 26.2% 3.8% 1.2% 2.1% 9.1% 2.5%

Association Criteria

171 groups differed more for the Mingus, Pastorius, and Rollins excerpts. However, age did not

affect agreement scores (p > .30).

Contrasts were observed between instrument groups on the association task. Cross-

tabulations showed a difference in both instrument and criteria category distribution between

participant instrument groups, X2 (3, N = 2295) = 26.47, p < .001 and X2 (10, 2295) = 57.46, p <

.001 (tables 4.29 and 4.30).

Table 4.29: Instrument Groups by Instrument Association Cross-tabulation

Table 4.30: Instrument Groups by Association Criteria Cross-tabulation

Category distribution comparisons for each excerpt showed particular differences for the

Coleman, Davis, Hancock, Monk, and Rollins excerpts. For most of these excerpts, respondents

who play bass, drums, guitar, or keyboards seemed to list more rhythm section players than did

melodic instrument respondents. For the criteria variable, instrument-group differences in criteria

were heightened for the Mingus, Montgomery, and Pastorius excerpts. Although mean

comparisons found no agreement score differences between instrument groups, post hoc tests

showed higher name agreement between rhythm section instrumentalists for the Hancock excerpt

Participant Instrument Melodic None Given Other Rhythm SectionMelodic 51.0% 1.4% 4.3% 43.2%Rhythm Section 41.0% 3.0% 5.6% 50.3%

Instrument Association

Participant Instrument apr clb infg infr inst mus ng oth prs sty tmMelodic 19.5% 20.1% 2.0% 12.4% 9.5% 17.5% 4.0% 1.4% 2.4% 9.2% 2.1%Rhythm Section 18.4% 15.8% 1.7% 10.6% 5.2% 22.9% 9.3% 1.3% 1.8% 9.7% 3.2%

Association Criteria

172 (t(49) = -1.99, p = .04), melody instrumentalists for the Holiday excerpt (t(49) = 2.77, p =

.01), and melody instrumentalists for the Parker excerpt (t(49) = 2.00, p = .04). Lower instrument

agreement scores were observed between melodic-instrument respondents only for the Monk

excerpt, t(49) = -2.38, p = .02. No instrument-group differences were found for criteria

agreement scores.

Several between-group differences were found for the experience attribute. Category

distribution varied significantly between groups for instrument association and criteria variables,

X2 (3, N = 2295) = 33.46, p < .001 and X2 (10, 2295) = 150.16, p < .001. Tables 4.31 and 4.32

show the distribution of response between experience groups for the instrument associations and

association criteria.

Table 4.31: Experience Groups by Instrument Association Cross-tabulation

Table 4.32: Experience Groups by Association Criteria Cross-tabulation

Instrument differences were especially pronounced for the Coltrane and Hawkins excerpts (p <

.05). Criteria category distributions varied significantly between experience groups for the

following excerpts: Coleman, Coltrane, Davis, Ellington, Hancock, Pastorius, Roach, and

Experience apr clb infg infr inst mus ng oth prs sty tm! 18 years 15.6% 18.3% 2.0% 12.7% 8.4% 14.8% 12.0% 1.2% 2.8% 8.9% 3.3%< 18 years 22.1% 17.2% 1.7% 10.1% 5.9% 26.0% 2.0% 1.5% 1.5% 10.0% 2.1%

Association Criteria

Experience Melodic None Given Other Rhythm Section! 18 years 46.0% 3.9% 3.6% 46.5%< 18 years 45.1% .8% 6.4% 47.7%

Instrument Association

173 Rollins. Specifically, criteria tended to be musical for the lower experience group, but

influence received and collaboration for the higher experience group. Participants with more

experience also tended to leave criteria responses blank. No between-experience-group

differences were discovered for name or instrument agreement scores; however, contrasts existed

for the criteria variable, t(763) = -4.11, p < .001. Specifically, participants with more experience

agreed less (M = 69.93, SD = 31.62) than did those with less experience (M = 78.72, SD =

27.50), specifically for the Coleman, Hancock, Holiday, and Rollins excerpts (p < .05).

Few differences between groups were observed in terms of educational background. Chi-

square tests indicated disparities in category distributions for instrument association and criteria

variables, X2 (6, N = 2295) = 27.20, p < .001 and X2 (20, 2295) = 96.62, p < .001. Cross-

tabulations for each excerpt revealed larger education-group differences for the Davis excerpt.

Tables 4.33 and 4.34 illustrate the distribution of response between education groups.

Table 4.33: Education Groups by Instrument Association Cross-tabulation

Table 4.34: Education Groups by Association Criteria Cross-tabulation

Education Melodic None Given Other Rhythm SectionHigh School 45.0% 2.8% 3.1% 49.2%Undergraduate 44.4% 3.2% 4.8% 47.7%Graduate 48.3% .2% 6.8% 44.8%

Instrument Association

Education apr clb infg infr inst mus ng oth prs sty tmHigh School 29.7% 11.1% 1.9% 11.1% 7.2% 18.9% 10.0% .8% 1.9% 4.7% 2.5%Undergraduate 16.6% 18.1% 2.1% 10.6% 7.2% 20.8% 8.7% 1.2% 2.1% 9.7% 3.0%Graduate 17.5% 20.8% 1.4% 13.2% 7.0% 20.8% 1.4% 1.9% 2.1% 11.7% 2.2%

Association Criteria

174 Participants with a high school education listed more musicians who played melodic

instruments, while those with higher educations listed more musicians who played rhythm

section instruments. Mean comparisons illustrated no differences between education groups for

name, instrument, and criteria agreement scores (p > .15). However, differences in instrument

agreement scores approached significance, F(2, 762) = 2.44, p = .06. Specifically, Least Squares

Difference (LSD) comparison showed higher agreement scores for respondents with a high

school versus undergraduate and graduate educations.

Participants’ preferred performance style influenced instrument association (X2 (6, N =

2295) = 73.32, p < .001), especially for the Ellington, Hawkins, and Rollins excerpts (tables 4.35

and 4.36).

Table 4.35: Performance Style Groups by Instrument Association Cross-tabulation

Table 4.36: Performance Style Groups by Association Criteria Cross-tabulation

J (jazz) and JO (jazz and other) groups associated more melodic instrumentalists, whereas the

JIM group associated composers, big bands, and nonmusicians (other). The criteria were also

distributed differently between performance style groups (X2 (6, N = 2295) = 191.47, p < .001).

Performance Style apr clb infg infr inst mus ng oth prs sty tmJazz 20.3% 21.2% 2.3% 11.5% 6.0% 18.8% 1.2% 1.8% 2.6% 10.6% 3.6%Jazz and Other 18.3% 16.8% .5% 12.0% 4.0% 22.1% 13.6% 1.1% 1.6% 7.8% 2.3%Jazz and Improvised Music 17.4% 13.1% 3.1% 10.2% 13.9% 20.9% 6.9% .9% 1.9% 10.0% 1.7%

Association Criteria

Performance Style Melodic None Given Other Rhythm SectionJazz 46.7% .1% 4.8% 48.5%Jazz and Other 48.3% 5.2% 3.5% 43.1%Jazz and Improvised Music 39.4% 1.9% 8.0% 50.7%

Instrument Association

175 Overall, performance style affected agreement scores for name associations (F(2, 762) =

7.16, p = .001) and criteria (F(2, 762) = 4.42, p = .01), but not for instruments. Post hoc tests

showed that the J group had higher name agreement scores than JO and JIM (jazz and

improvised music) groups, notably for the Davis and Hancock excerpts. Similarly, the J group’s

criteria agreement scores were noticeably higher than those for the JIM group for the Coltrane,

Davis, and Pastorius excerpts.

Finally, network properties and community affiliation judgments played a significant role

in association task responses. Instrument distribution varied across HC groups (X2 (9, N = 2250)

= 77.03, p < .001), particularly for the Hawkins excerpt. With respect to the criteria variable, HC

group membership affected category distribution, with less effect on the Coltrane, Coleman and

Parker excerpts (X2 (30, N = 2250) = 205.61, p < .001). Specifically, a larger percentage of HC

group 3 and 4 participants associated musicians who played melodic instruments than did groups

1 and 2. ANOVAs indicated a main effect of HC group on name association (F(3, 746) = 3.27, p

= .02) and criteria agreement scores, F(3, 746) = 5.10, p = .002). Tables 4.37 and 4.38 show

these distributions.

Table 4.37: HC Groups by Instrument Association Cross-tabulation

HC Group Melodic None Given Other Rhythm Section1 48.0% .7% 5.3% 46.1%2 42.5% 6.4% 3.7% 47.4%3 43.3% .7% 7.3% 48.7%4 47.4% .0% 4.4% 48.1%

Instrument Association

176 Table 4.38: HC Groups by Association Criteria Cross-tabulation

GN cluster contrasts were observed for instrument association (X2 (6, N = 2250) = 72.67, p <

.001), especially for the Armstrong, Davis, Hawkins and Roach excerpts. Likewise, criteria

categories were distributed differently between GN clusters (X2 (20, N = 2250) = 79.83, p <

.001). Name and criteria agreement score differences were found between GN clusters, F(2, 747)

= 6.32, p = .002; F(2, 747) = 4.41, p = .01. Specifically, participants belonging to GN clusters 1

and 2 had higher agreement scores than those in GN cluster 3. Tables 4.39 and 4.40 specify the

differences in response distribution between GN Clusters.

Table 4.39: GN Clusters by Instrument Association Cross-tabulation

Table 4.40: GN Clusters by Association Criteria Cross-tabulation

HC Group apr clb infg infr inst mus ng oth prs sty tm1 19.5% 18.3% 1.2% 7.9% 8.2% 22.5% 7.3% 1.1% 1.3% 9.6% 3.1%2 22.4% 13.5% 1.2% 12.6% 3.7% 24.7% 7.7% 1.6% 2.1% 7.7% 2.8%3 12.9% 22.4% 3.8% 13.1% 11.6% 14.4% 6.4% 1.1% 3.1% 8.9% 2.2%4 8.1% 24.4% 4.4% 28.9% 2.2% .7% 3.7% 3.0% 5.2% 17.8% 1.5%

Association Criteria

GN Cluster Melodic None Given Other Rhythm Section1 47.4% .6% 5.7% 46.3%2 44.2% 6.0% 2.9% 46.9%3 42.2% .7% 7.2% 49.9%

Instrument Association

GN Cluster apr clb infg infr inst mus ng oth prs sty tm1 19.6% 18.8% 1.3% 8.1% 7.5% 23.5% 6.2% 1.2% 1.9% 9.0% 2.8%2 20.8% 15.0% 1.4% 13.1% 4.3% 20.3% 8.2% 1.7% 2.1% 10.4% 2.8%3 13.8% 21.2% 4.2% 12.3% 12.1% 14.8% 7.2% 1.2% 3.0% 7.7% 2.5%

Association Criteria

177 Chi-square tests showed that instruments and criteria were equally distributed across density

groups (p > .05). Correspondingly, density did not affect agreement scores for any of the tasks (p

> .05). Community affiliation had no effect on instrument category distribution for any excerpt;

however, criteria and community affiliation were related (X2 (10, N = 2295) = 67.91, p < .001),

notably for the Coleman, Davis, Mingus, and Monk excerpts. Table 4.41ß depicts the distribution

of response for the two community affiliation groups.

Table 4.41: Community Affiliation Groups by Association Criteria Cross-tabulation

Mean comparisons showed affiliation group differences for name agreement scores, t(763) =

2.61, p = .01, but not for instrument or criteria. Post hoc evaluations showed that participants

who rated themselves higher on community affiliation also had higher name agreement scores.

Ratings and Accuracy

Mean typicality and influence ratings for the 15 excerpts were 4.54 (SD = 0.74) and 3.79

(SD = 1.03), respectively (table 4.42).

Community Affiliation apr clb infg infr inst mus ng oth prs sty tm>3 17.8% 18.0% 2.4% 12.0% 5.7% 24.5% 5.6% 1.6% 1.9% 8.2% 2.2%!3 20.6% 17.3% 1.1% 10.3% 9.4% 14.2% 8.9% .9% 2.4% 11.3% 3.4%

Association Criteria

178 Table 4.42: Typicality and Influence Ratings

As expected, influence ratings varied more than typicality ratings. The correlation analysis

indicated a significant relationship between the two variables, r(763) = .34, p < .001. One-way

ANOVAs yielded main effects of excerpt on typicality, F(14, 750) = 6.76, p < .001, and

influence, F(14, 750) = 14.31, p < .001. Post hoc tests showed lower typicality ratings for the

Roach and Mingus excerpts (p < .05). On the whole, participants were influenced the most by

Davis, Coltrane, and Monk (p < .001). A two-way ANOVA indicated an interaction effect for

excerpt and influence on typicality ratings, F(49, 697) = 1.80, p = .001.

Overall, participants were quite accurate in their identification of musicians across all the

excerpts (table 4.43).

Excerpt M Typicality SD Excerpt M Influence SDBillie Holiday 4.94 0.24 Miles Davis 4.76 0.47Miles Davis 4.86 0.40 John Coltrane 4.51 0.73John Coltrane 4.76 0.51 Thelonious Monk 4.37 0.80Jaco Pastorius 4.75 0.63 Charlie Parker 4.16 1.03Duke Ellington 4.71 0.70 Sonny Rollins 4.14 1.02Wes Montgomery 4.65 0.59 Herbie Hancock 4.10 1.04Louis Armstrong 4.61 0.80 Duke Ellington 3.98 0.97Thelonious Monk 4.61 0.85 Ornette Coleman 3.78 1.27Ornette Coleman 4.53 0.90 Coleman Hawkins 3.69 1.14Coleman Hawkins 4.53 0.81 Billie Holiday 3.61 1.08Charlie Parker 4.47 0.95 Louis Armstrong 3.35 1.13Sonny Rollins 4.47 0.86 Coleman Hawkins 3.24 1.23Herbie Hancock 4.22 0.99 Max Roach 3.24 1.14Coleman Hawkins 4.04 1.00 Wes Montgomery 3.04 1.25Max Roach 3.96 0.89 Jaco Pastorius 2.94 1.14Total 4.54 0.74 Total 3.79 1.03

179 Table 4.43: Performer Identification Accuracy

The accuracy results showed main effects of excerpt (F(14, 750) = 12.60, p < .001), typicality

(F(4, 760) = 106.27, p < .001), and influence (F(4, 760) = 25.06, p < .001) on accuracy. Two-

way ANOVAs resulted in interaction effects of excerpt and typicality (F(38, 708) = 2.72, p <

.001), excerpt and influence, (F(49, 697) = 1.93, p < .001), and typicality and influence (F(12,

744) = 3.96, p < .001). On average, the accuracy of performer identification was lowest for the

Roach, Hancock, and Hawkins excerpts, and highest for the Davis, Coltrane, and Pastorius

excerpts (p < .01). Excerpts rated higher on typicality (Holiday, Davis, Coltrane, Ellington) and

influence (Davis, Coltrane, Monk) scales were identified more accurately than those rated lower

on the scales. With regard to the interactions, participants performed worse on the excerpts that

were rated lower on the typicality scale. It was more difficult to discern the direction of

interaction between accuracy and influence ratings, since participants were extremely accurate

(JP = 98.04%; MD = 100%) at identifying excerpts with both low (JP = 2.94) and high (MD =

4.76) influence ratings. This appears to be a ceiling effect. To explain the trends in accuracy, the

Excerpt M Accuracy SDMiles Davis 100.00% 0.00John Coltrane 98.04% 0.14Jaco Pastorius 98.04% 0.14Duke Ellington 96.08% 0.20Billie Holiday 96.08% 0.20Louis Armstrong 94.12% 0.24Thelonious Monk 92.16% 0.27Ornette Coleman 86.27% 0.35Wes Montgomery 86.27% 0.35Charlie Parker 86.27% 0.35Sonny Rollins 80.39% 0.40Coleman Hawkins 72.55% 0.45Charles Mingus 70.59% 0.46Herbie Hancock 64.71% 0.48Max Roach 39.22% 0.49Total 84.05% 0.30

180 excerpts were categorized into two groups based on the performer’s role, either as the main

melodic voice or a part of the rhythm section (table 4.44).

Table 4.44: Performer Instrument Categories

A matched-pairs t-test found a significant difference between accuracy means for melodic (M =

.90, SD = .29) and rhythm section (M = .78, SD = .41) instruments, t(356) = 4.90, p < .001,

suggesting that accuracy was best explained by performer instrument.

These results revealed a number of correspondences between ratings, accuracy, and

association task agreement scores. First of all, the correlation analysis showed little to no

relationship between both ratings and the three association task agreement scores. However, one-

way ANOVAs demonstrated a main effect of typicality ratings on agreement scores for name

association, F(4, 760) = 6.86, p < .001, and instrument, F(4, 760) = 8.97, p < .001, but not for

criteria, F(4, 760) = 1.88, p = .11. Post hoc comparisons showed higher name and instrument

agreement scores for higher typicality ratings (p < .05). Likewise, there were main effects of

Excerpt Instrument CategoryLouis Armstrong MelodicOrnette Coleman MelodicJohn Coltrane MelodicMiles Davis MelodicDuke Ellington RhythmHerbie Hancock RhythmColeman Hawkins MelodicBillie Holiday MelodicCharles Mingus RhythmThelonious Monk RhythmWes Montgomery RhythmCharlie Parker MelodicJaco Pastorius RhythmMax Roach RhythmSonny Rollins Melodic

181 influence rating on agreement scores for instruments, F(4, 760) = 8.09, p < .001, and criteria,

F(4, 760) = 4.65, p = .001, but not for names, F(4, 760) = 1.43, p = .22. Two-way ANOVAs

showed no interaction effects of ratings on agreement scores (p > .32). Finally, independent t-

tests revealed disparities for name agreement scores between accurate (M = 17.79, SD = 10.60)

and inaccurate (M = 12.96, SD = 8.44) respondents, t(763) = -4.76, p < .001, but showed the

opposite for instrument agreement scores, t(763) = 7.31, p < .001 (Accurate M = 180.68, SD =

103.39; Inaccurate M = 256.68; SD = 114.43). There were no differences in criteria agreement

scores between participants who were accurate (M = 75.03, SD = 29.99) and inaccurate (M =

71.15, SD = 29.34), t(763) = -1.316, p = .19.

Typicality ratings depended on network characteristics more than on demographic

attributes. Overall, no differences in age, experience, instrument, and education groups were

observed; however, excerpt comparisons showed higher typicality ratings for the Monk excerpt

in the older age group (p = .04), and for the Ellington excerpt in the higher education groups (p =

.03). These results illustrated performance style group differences (F(2, 762) = 6.23, p = .002),

such that respondents who submitted jazz and jazz and other as their primary style found

excerpts to be more typical than did respondents who perform jazz and improvised music. HC

group and GN cluster membership had main effects on typicality ratings, F(3, 746) = 4.65, p =

.003 and F(2, 747) = 4.88, p = .008, respectively. Post hoc tests found that this effect was

especially pronounced for the Monk, Montgomery, and Parker excerpts (p < .02). Community

affiliation judgments had no effect on typicality ratings, t(763) = 1.82, p = .07.

As with the typicality ratings, participant attributes mildly affected influence ratings. Age

and experience had no effect on influence ratings (p > .40). Education group had a main effect on

the ratings, F(2, 762) = 4.68, p = .01, such that lower education groups had higher ratings (p <

182 .01). Overall, there was no effect of instrument group on influence ratings, but excerpt

comparisons showed that melodic instrumentalists rated Hawkins as more influential (t(49) =

2.29, p = .02); the same effect was found with rhythm section players for Montgomery, (t(49) = -

2.07, p = .04). Preferred performance style strongly affected influence ratings, F(2, 762) = 11.42,

p < .001, especially for Armstrong, Hancock, Montgomery, Parker, and Pastorius. Particularly,

the jazz and jazz only groups considered these performers as more influential to them than did the

jazz and improvised music group. Both network clusters affected influence ratings, F(3, 746) =

9.60, p < .001 and F(2, 747) = 3.71, p = .03. Post hoc tests indicated lower influence ratings in

HC groups 3 and 4, and GN cluster 3. This effect was particularly strong for Pastorius and

Hancock. There were no density or community affiliation group differences in influence ratings

(p > .20).

Accuracy scores differed between age, experience, and performance style groups, but not

between instrument, and education groups, or any network property groups. Participants over 30

were more accurate than those under 30, t(763) = 3.02, p = .003, especially for the Monk and

Parker excerpts (p < .04). Those with more experience performed better on the identification task

than those with less, t(763) = 2.14, p = .03. Even though there were no overall instrument group

contrasts, excerpt comparisons showed that melodic instrumentalists were better than rhythm

section players at identifying the Hawkins excerpt, t(49) = 2.14, p = .04. Performance style

groups jazz and jazz only were generally more accurate than the jazz and improvised music

group, F (2, 762) = 2.55, p = .04, especially for Ellington and Parker, but not for Monk and

Mingus (p < .05).

183 Summary of Results

Results from the association task indicate that participants’ responses depended on a

combination of stimulus characteristics and participant attributes. First of all, names,

instruments, and criteria differed significantly between excerpts. Name associations tended to

include musicians directly67 or indirectly68 related to the stimulus. Examples of direct

associations were Bill Evans (Davis), Jim Hall (Rollins), and Tommy Flanagan (Coltrane), as

contributions by each musician were heard on the excerpt recordings. Indirect associations

included Ella Fitzgerald (Holiday), Lester Young (Hawkins), Count Basie (Ellington), and Art

Blakey (Roach). Each of these artists can be considered contemporaries of the performers, and

moreover, their contributions were not present on the excerpts. Likewise, instrument associations

tended to relate directly to those performers heard on the excerpts. Examples of this phenomenon

were the overrepresentation of drummers for Roach, bassists for Mingus, vocalists for Holiday,

and big band artists for Ellington. Six of the excerpts69 included solos from saxophone players,

thus this was the instrument that was revealed most in the results. Association criteria depended

on the performers in the excerpt. For instance, the majority of responses for the Armstrong

excerpt were based on style, while the majority of responses for the Hawkins, Montgomery, and

Rollins excerpts were based on concrete musical features. These disparities suggest that

participants conceptualized performers quite differently.

Second, the patterns of agreement for each association variable differed between

excerpts. Overall, agreement scores for names were the lowest, while those for instruments were

the highest. More participants named the same musicians for the Holiday, Davis, and Hawkins

67 Such that the respondent listed a musician on the recording. 68 Such that the respondent listed a musician related to the performer or recording. 69 Coleman, Coltrane, Ellington, Hawkins, Parker, and Rollins.

184 excerpts, while less did such for the Monk excerpt. The highest instrument agreement scores

were observed for the Roach excerpt and lowest for the Ellington excerpt. Interestingly, these

two excerpts represented extreme opposites in instrument density; participants only heard drums

in the Roach excerpt, while they heard an entire big band for the Ellington excerpt. Likewise,

criteria agreement scores were lowest for the Ellington excerpt. Criteria agreement scores were

highest for the Davis excerpt. As with the categorical data, larger criteria agreement scores

suggest that participants had similar ways of thinking about the excerpts.

Third, the nine participant characteristics appeared to affect responses to certain excerpts

in the association task. Age affected criteria more than instrument categories, but had no effect

on agreement scores. Overall, older participants wrote more about collaboration in their

association explanations, while younger participants focused more on musical characteristics.

Participant instrument group affected the instrument more than the criteria variable. Specifically,

the participant instrument group matched the instrument association group. Name agreement

scores were only affected for particular excerpts, but they were larger when participant and

performer instrument groups matched. Experience affected criteria more than instrument

responses. In particular, participants with less musical experience tended to explain associations

with musical reasons, while experienced participants wrote more about instrument groups and

also left many responses blank. Overall, participants with less experience responded with more

typical criteria than those with more experience. Education had less of an effect on the

association task; however, participants with more formal education tended to include style as a

criteria more than those with less. In addition, respondents with high school educations referred

to musical approach more than those with higher education. Preferred performance style affected

name, instrument, and criteria responses. Jazz and jazz only groups tended to list more melodic

185 instrumentalists, while the jazz and improvised music group listed more rhythm section

instrumentalists. Criteria were based on collaboration information for the jazz and jazz other

groups, and on instrument for the jazz and improvised music group. Furthermore, performance

style had a profound influence on name and criteria agreement scores, such that the jazz and jazz

other groups responded more conventionally than did the jazz and improvised music group.

In general, the network attributes had a significant impact on responses to the association

task. HC group and GN cluster membership influenced the categorical distribution for

instruments, but more so for criteria. HC Group 2 focused more on musical approach, while

groups 3 and 4 on collaboration and style, and group 1 on time period. Likewise, GN cluster 1

explained their responses in musical terms more than the other two groups, while GN cluster 3

with information about the performers’ collaborations. GN cluster 2 provided more style criteria

than the other two groups. Name and criteria agreement scores were influenced by network

groupings, such that HC groups 1 and 2 and GN clusters 1 and 2 provided more typical responses

than did HC groups 3 and 4 and GN cluster 3. Density and community affiliation had little to no

effect on task responses; however, slight differences in instrument distribution were found

between density groups and in criteria distribution between affiliation groups. Higher community

affiliation judgments produced higher name agreement scores, but no other effect was observed.

Accuracy, typicality, and influence ratings significantly interacted and affected the task.

Ratings and accuracy varied between excerpts; Holiday and Roach had the highest and lowest

typicality ratings, Davis and Pastorius had the highest and lowest influence ratings, and Davis

and Roach had the highest and lowest accuracy scores. Influence and typicality were slightly

related, and both ratings had a combined effect on accuracy, such that more influential

performers and typical excerpts were more accuracy identified. In addition, melody-instrument

186 performers were identified more accurately than rhythm section performers. Name and

instrument agreement scores were higher for more typical excerpts, while instrument and criteria

scores were higher for more influential performers. Accurate participants listed more similar

names and fewer similar instruments than inaccurate participants.

Lastly, certain attributes slightly affected task ratings and accuracy. Performance style

preference, HC group, and GN cluster affected typicality ratings, while education, preferred

performance style, HC group, and GN cluster had an effect on influence ratings. Overall, task

accuracy was slightly influenced by age, experience, and performance style groups.

Descriptor-Matching Task

Overview

The final task required participants to match 3 musical descriptors, from a list of 24, to

each performer prompt. As was the case with the association task, the following discussion will

consider both categorical and continuous data for performer prompts. Moreover, it will show

disparities between education and performance style preferences, as well as interactions with

ratings and accuracy from the association task.

Analysis Procedures

As previously mentioned, the list of musical descriptors was developed by collating and

coding free response descriptions from the pilot study.70 Below (table 4.45) is the list of the 24

descriptors participants were instructed to match to the 15 performer name prompts, as well as

the codes used in data analysis. 70 Conventionalized terminology from jazz history and theory texts was also used to develop the descriptor categories (Jaffe, 1983; Levine, 1995; Gioia, 1998).

187

Table 4.45: Musical Descriptors and Codes

The data for the 153 responses (3 descriptors for each prompt) were analyzed in ways similar to

those used for the association task: nominal categories, frequency scores, and agreement scores.

To examine the relationship between descriptor categories and performer prompts, a Chi-square

test was calculated in the cross-tabulation function. Frequency scores were computed to show

distributions of chosen descriptors within and between performer prompts. In addition, the

frequency scores for each descriptor were summed to provide each participant with a cumulative

agreement score. The differences in agreement scores between prompts were assessed with one-

way ANOVAs and LSD post hoc tests.

Descriptor CodeArticulation artcBlues Influence bluCommunication and Interaction cmintComposition and Orchestration cmporConsonance consContour contDissonance dissEmotion and Expression emoexExtramusical Association extrmGroove groHarmony and Tonality hmtnImprovisational Creativity impcrLyricism lyrMelodicism melPhrasing phrasRepetitiveness repRhythm rhyRisk-taking riskStructure strucTime timeTimbre tmbTexture txtVoice-leading vcldVirtuosity virt

188 Determining what, if any, associations existed between participant attributes and

matched descriptors required the use of several statistical procedures. As with the association

task, continuous attributes were divided into groups in order to carry out t-test and ANOVA

comparisons (table 4.2). First, a Chi-square analysis was performed to look at between-attribute

category distributions for descriptors within performer prompts. Through this test I aimed to find

out any relationships between attributes and the descriptors chosen by participants. Multiple

between groups t-tests and ANOVAs were employed to assess the relationship between attributes

and agreement scores, using attributes as the groups.

Finally, the data were analyzed to see whether descriptor categories and agreement scores

depended on performer-rated influence and association task accuracy. Cross-tabulations

procedures applied the Chi-square statistic to the categorical data, while independent t-tests

calculated between-group (accurate versus not accurate) differences for matching agreement

scores.

Results

The responses to the descriptor-matching task demonstrated clear differences between

performer prompts (table 4.46).

189 Table 4.46: Descriptor-Prompt Matches71

An ANOVA yielded a main effect of descriptors on frequency score, F(23, 336) = 4.28, p < .001.

LSD post hoc tests showed that participants used phrasing, emotion and expression, timbre,

blues influence, improvisational creativity, and virtuosity the most, and repetitiveness,

consonance, voice-leading, extramusical association, texture, communication and interaction,

contour, structure, dissonance, and time the least (p < .05). Cross-tabulation and Chi-square

procedures revealed significant differences in cell counts between performer prompts, X2 (322, N

71 As a reminder, the descriptor terms (in order of this chart) were: phrasing, emotion and expression, timbre, blues influence, improvisational creativity, virtuosity, groove, harmony and tonality, articulation, composition and orchestration, melodicism, rhythm, lyricism, risk taking, time, dissonance, structure, contour, communication and interaction, texture, extramusical association, voice-leading, consonance, and repetitiveness.

Term LA OC JC MD DE HH CH BH CM TM WM CP JP MR SR Sum SDphras 18 5 5 23 5 14 21 30 8 11 17 10 7 14 21 209 7.62emoex 16 16 14 21 10 3 8 39 13 7 2 6 9 3 5 172 9.43tmb 13 8 12 22 4 1 24 25 2 3 10 4 17 7 11 163 8.00blu 21 18 9 3 5 1 9 19 17 5 22 24 0 1 5 159 8.61impcr 8 19 16 10 3 10 4 4 8 14 3 17 10 7 15 148 5.30virt 8 1 27 1 0 9 4 2 8 2 6 26 35 11 5 145 10.84gro 0 1 0 5 8 18 2 0 11 0 28 0 17 16 10 116 8.75hmtn 1 1 31 2 13 26 8 0 4 8 6 9 4 0 3 116 9.26artc 15 4 2 3 3 3 7 4 6 15 9 10 9 12 13 115 4.56cmpor 1 6 2 4 49 7 0 0 30 11 1 0 1 2 1 115 13.77mel 13 10 3 12 8 6 19 3 2 4 8 9 2 2 12 113 5.05rhy 6 1 1 0 7 7 2 4 4 15 8 7 4 28 16 110 7.36lyr 11 7 4 15 8 4 11 15 2 1 2 8 7 1 4 100 4.70risk 5 21 6 12 1 3 4 1 15 12 0 3 3 2 10 98 6.12time 4 0 2 3 1 2 2 2 6 2 5 8 7 26 4 74 6.25diss 1 13 1 0 2 3 0 0 6 30 1 0 0 1 0 58 8.01struc 1 4 4 1 8 4 6 1 3 5 4 3 1 8 3 56 2.31cont 0 3 3 5 1 7 8 1 0 0 4 4 5 1 4 46 2.55cmint 3 6 4 6 2 9 1 0 1 0 2 0 0 6 3 43 2.80txt 0 3 1 2 7 7 2 2 4 1 4 0 6 3 1 43 2.33extrm 2 4 4 3 4 2 0 1 3 1 1 0 9 0 0 34 2.40vcld 2 1 1 0 3 7 5 0 0 3 6 3 0 0 2 33 2.31cons 4 0 0 0 0 0 6 0 0 0 2 1 0 0 2 15 1.81rep 0 1 1 0 1 0 0 0 0 3 2 1 0 2 3 14 1.10

190 = 2295) = 2103.82, p < .001, indicating uniquely matched descriptors for each prompt. For

instance, more participants matched blues influence and phrasing to the Armstrong prompt,

while more participants matched emotion and expression and phrasing to the Holiday prompt.

Rhythm and time were most likely to be paired with the Roach prompt than any other prompt.

Agreement scores for each performer prompt are included in table 4.47.

Table 4.47: Descriptor-Matching Agreement Scores

A one-way ANOVA yielded an F(14, 750) value of 31.38, indicating a main effect for prompt on

overall agreement score (p < .001). Post hoc tests indicated higher agreement for the Holiday,

Ellington, Coltrane, Roach, and Pastorius prompts (p < .01), and lower, but still significant,

agreement for the Rollins prompt (p < .05).

Prompt M Agreement Score SD

Billie Holiday 72.65 16.20Duke Ellington 61.67 9.01John Coltrane 49.55 15.29Max Roach 47.31 15.64Jaco Pastorious 47.08 14.56Miles Davis 43.43 12.72Charlie Parker 42.69 13.06Thelonious Monk 42.14 12.47Charles Mingus 40.84 13.22Wes Montgomery 40.76 16.26Coleman Hawkins 38.88 12.16Louis Armstrong 38.18 9.56Ornette Coleman 37.90 10.56Herbie Hancock 35.63 12.37Sonny Rollins 33.82 9.78

Mean 44.84 12.86

191 Participant Attribute, Accuracy, and Influence Rating Effects

On the whole, only a few of the participant attributes influenced the categorical matching

data for specific prompts. The Chi-square tests for all of the matching data revealed no

distribution differences between groups of age, instrument, experience, education, preferred

style, HC group, GN cluster, density, or community affiliation (p > .20).

Agreement score comparisons demonstrated statistically significant effects for only 2 of

the participant attributes. T-tests indicated no significant effect of the following attribute groups:

age, instrument, experience, density, and community affiliation (p > .90). Likewise, an ANOVA

analysis yielded no overall main effects of HC group GN cluster on agreement scores (p > .90).

However, significant agreement score differences were found between education (F(2, 762) =

3.60, p = .03) and performance style (F(2, 762) = 2.80, p = .05) groups. Post hoc tests showed

reliably higher agreement scores for participants with regard to educational background; note the

levels for graduate (M = 45.42, SD = 16.53) and undergraduate (M = 45.56, SD = 16.28)

education versus high school (M = 41.18, SD = 15.63) (p < .05). In addition, post hoc

comparisons resulted in higher scores for participants who listed jazz and jazz and other (M =

46.24, SD = 16.25), as opposed to jazz and improvised music (M = 42.66, SD = 15.92), as their

primary style of performances (p < .05). A two-way ANOVA analysis showed an interaction of

prompt and education group on agreement scores, F(28, 720) = 1.42, p = .05, but not of prompt

and performance style (p > .90). This implies that the extent to which participants agreed on their

responses depended on an interdependent relationship between the performer prompt and the

participants’ level of education, but not their preferred performance style.

There were several relationships between the association task ratings (typicality and

influence), accuracy, and the descriptor task responses. Chi-square analysis results showed a

192 relationship between descriptor category and task accuracy (X2(23, N = 2295) = 54.71, p =

.02) as well as influence ratings (X2(92, 2295) = 123.74, p = .02. The difference in agreement

scores for participants in the association task accurate group (M = 45.23, SD = 16.64) versus

those in the inaccurate group (M = 42.75, SD = 14.26), approached significance t(763) = 1.54, p

= .06. Significant between-influence group differences were found for agreement scores, such

that higher, versus moderate and low, influence ratings were associated with higher agreement

scores, F(2, 762) = 4.23, p = .02. A two-way ANOVA revealed no interaction between accuracy

and influence ratings on agreement scores (p = .13).

Summary of Results

As was the case with the association task, the responses to the matching task depended on

both performer prompts and participant characteristics. First, descriptor differences were related

to the conventional identities of each performer, which will be discussed in detail in the final

chapter. An example of this was the observation that rhythm and time were matched to the

drummer Max Roach, who, as a performer, fulfilled the conventional role of keeping time in an

ensemble as well as providing rhythmic variation. Second, agreement scores varied between

prompts, which suggests that certain performers, such as Holiday, Ellington, Coltrane, and

Roach, may be easier to define given the 24 descriptors. It might be suggested that these patterns

are due to these musicians’ well-defined presence in the standard jazz canon – authors of texts

agree on their contributions to the history of jazz (Gioia, 1997; Martin & Waters, 2002).

Participant attributes had a significant effect only on certain prompts with regard to the

categorical data. This pattern of results suggests that the effect was slight but not strong enough

to generalize across excerpts. Agreement scores differed between education and preferred

193 performance style groups, indicating that formalized knowledge of the performers may play a

role in cognitive processing.

Finally, there was a significant relationship between agreement scores and influence

ratings, and a smaller relationship between agreement scores and accuracy. Performers who were

rated higher on the influence scale received higher agreement scores for the task. In addition,

performers who were identified correctly had slightly higher agreement scores.

Comparison of Participant Attribute Influences

Since participant attributes had a varied effect on task responses, a comprehensive look

illustrates commonalities between tasks. Table 4.48 depicts the impact of participant attributes

and stimulus-related information on categorical responses, while table 4.49 shows the same for

collated agreement scores.

Table 4.48: Comparison of Influential Factors on Categorical Data72

72 The plus sign (+) indicates a significant relationship between the two variables, while the minus sign (-) symbolizes the opposite. No directionality is implied in these tables.

Factor Clustering Profiles Instrument Association Association Criteria DescriptorExcerpt/Prompt n/a + + +Age + + + -Experience + + + -Instrument - + + -Education - + + -Perf. Style + + + -HC Group n/a + + -GN Cluster n/a + + -Density + - - -Comm. Aff. - - + -

194 Table 4.49: Comparison of Influential Factors on Continuous Data (Agreement Scores)

A closer scrutiny of the tables reveals the impact of excerpt or prompt on all forms of data

(collaborator, association, and descriptor matching tasks) and the apparent differences between

the association and descriptor tasks. This finding will be explored further in the next chapter.

Chapter Summary

The results of the collaborator, association, and descriptor-matching tasks provide a

broad view of professional musicians’ cognitive representations of eminent performers, as seen

with both audio excerpts and simple prompts by performer name. The results of the collaborator

task uncovered distinct relations between cluster groups and participant attributes. The

participants’ associative responses illustrated patterns directly or indirectly related to the excerpt,

the instrument of the soloist, and at least 13 criterion categories. Moreover, participants’

associations with the excerpts and prompts depended on many factors, including age, experience,

instrument, education, performance style, and community affiliation – it is notable that the

effects of the latter were the strongest. An analysis of the descriptor data revealed significant

Factor Name A.S. Inst A.S. Criteria A.S. Descriptor A.S.Excerpt/Prompt + + + +Typicality + + - -Influence - + + +Accuracy + + - +Age - - - -Experience - - + -Instrument - - - -Education - - - +Perf. Style + - + +HC Group + - + -GN Cluster + - + -Density - - - -Comm. Aff. + - - -

195 effects of prompt, education, and performance style on the pairing of musical qualities with

performer prompts. The association and descriptor tasks were also affected by complex

interactions between typicality, influence, and identification accuracy. More detailed

interpretations of these results will be provided in the next chapter.

196 CHAPTER 5

DISCUSSION AND CONCLUSIONS

Introduction: Review of Objectives and Chapter Overview

Thus far, I have presented evidence for the associative representations of music, which

professional musicians use, and have addressed the influence of attributes and community

variables on these systems. In so doing, my methodologies and results have incorporated views

from a variety of disciplinary viewpoints, including those of cognitive psychology,

ethnomusicology, and music cognition. The literature in each of these fields illustrates the

complexity of interactions between cognitive mechanisms of interpretation and memory – but,

the processes and representative capacity of associative representations in music has been

practically ignored.

The goals of this final chapter are to tie together the previously described research,

methodological features, and overall results, and to give some summary of aspects of the

cognitive processing of jazz, as well as to evaluate the impact of community affiliations on these

cognitive processes. To frame the discussion, the previous chapters’ objectives are reiterated

below:

How are responses to the collaboration, association, and descriptor- matching tasks explained in light of previous studies in social network analysis, cognitive and cultural psychology, and music cognition?

How do the results from this dissertation contribute to the study of cognitive and cultural psychology, as well as music cognition, and what future directions are indicated? What practical benefits does this research offer to educators?

197 These questions will be addressed in turn. The results of the present study will first be

examined in the light of prior research. Next, a comprehensive overview of this study’s three

tasks will be sketched, along with suggestions as to how these results might advance research

into musicians’ cognitive associations for music and the community-based influences on their

cognitive structures and behaviors in music. The last section discusses the application of these

findings to jazz education and, more broadly, to the understanding of the general impact of

communities on musicians’ lives.

Interpretation of Results

Collaborator Task: Network Properties of Jazz Communities

The collaborator task used methods of social network analysis (SNA) to provide unique,

systematic measures of community structure and affiliation. Some psychological studies tend to

classify participants based on their responses to ratings on attitudinal and self-identity statements

(Heider, 1944), but this study attempted to expand on this by using more concrete questions

about particular individuals in musicians’ collaborative circles. The participants were queried on

their collaborative ties to professional musicians by listing 20 names and specifying how often

they discussed music with and how well they knew each musician. Although the sampling

procedures used here limited the analysis of this data as a typical social network, the results still

uncovered significant structural properties and patterns of connections between musicians.

Structural network properties, including geodesic distances, degrees, and correlations

between actor ties can be related to small world and jazz musician studies in the SNA literature.

Milgram (1967) identified clusters in a unique field study involving chain mail, in which he

observed the average length of time and number of people required to reach a particular

198 destination.73 His results showed a “small world effect,” in which clusters were connected by

an average path length of 5.5, and that a small number of “hub persons” supplied the integral

links to the destination. This advanced the notion of small communities and the connections

between them in the study of social networks. In the present study, the average geodesic

distance74 was 4.03 for the whole sample; thus, the shortest distance between any two musicians

in the network was approximately 4 ties. This is perhaps due to this study’s sampling technique;

the total set of connections between all 461 musicians were not known. The average geodesic

distance for the 51 participants was 2.30. As expected, professional musicians working in the

same metropolitan area are more closely connected than the acquaintances in Milgrim’s original

small network study. Related to this, in a study of jazz recordings between 1912 and 1940,75

Gleiser and Danon (2003) observed an average distance of 2.79 between approximately 1275

musicians. If they had collected artifacts from live performances (e.g. programs, recordings) to

supplement the data, the average distances might have been even smaller, as found in the present

study. The smaller distance found here may also be explained by the high music discussion and

friendship ratings; in other words, collaborations in this study were characterized by closer

relationships built up from conversation and bonding activities beyond those of previously

studied groups. This study’s additional network statistics confirmed a moderate average density

for the 51 participants (0.37), markedly higher than in the 461-node network (0.03). Other

sampling and data collection techniques would most likely increase the proportion of observed to

actual ties, revealing higher density values (Hanneman & Riddle, 2005).

73 296 letters were sent out to participants, and only 64 reached their destination at Milgram’s residence in Massachusetts. 74 This can also be referred to as average path length. 75 Data were drawn from personnel information on Red Hot Jazz Archive digital database recordings.

199 Measures of centrality illustrate the extent of social power evident in a network. In

this study, the average degree (k) between musician collaborators was 60.71, similar to the value

of 60.30 specified by Gleiser and Danon (2003). Their observations also indicated that certain

musicians, including Eddie Lang (k = 415), Frankie Trumbauer (k = 307), and Louis Armstrong

(k = 262), had more ties than other musicians. In the present study, we see parallels for DM (k =

112), QK (k = 103), DT (k = 91), and PM (k = 86),76 who were all male rhythm section players

and who specified that they prefer to play a variety of musical styles77 (e.g. jazz and other and

jazz and improvised music). In a recent SNA project at the University of Michigan, Giaquinto et

al. (2009) studied collaborations between jazz musicians in 1959. Their results show that

musicians with the highest degree centrality were also all rhythm section players, including Paul

Chambers (k = 169), Wynton Kelly (k = 99), Jimmy Cobb (k = 97), and Philly Joe Jones (k = 70).

They stated that “being part of a well-connected community and being able to cross musical style

boundaries seem to be a good way of being central in jazz” (p. 3).78 This finding supports the

notion that musicians who are musically flexible and concerned with group dynamics79 are

sought out more than those who are not (MacDonald & Wilson, 2005). MacDonald and Wilson

(2005) also theorized that this general trend relates to constructed identities and conventionalized

roles in jazz.

76 It is also noticeable that DM and PM had the highest values of closeness to other musicians, although this was not included in the network analysis. 77 This result can also be explained by the sampling procedures, which caused an overrepresentation of a certain type of musician, namely male, white, and living on the North-Side of Chicago. 78 The authors of this study might consider the difference between being musically and stylistically flexible; musicians who were deemed musically flexible, such as the example of Paul Chambers, seemed to be more central than musicians who crossed style boundaries. 79 This is not to say that “frontline” players (e.g. horns) are not concerned with blending of group dynamics; the urgency of this connection is simply heightened for rhythm section players. This finding is also in line with the colloquial observation that rhythm section players “get more work.”

200 This study’s results were also comparable to previous research on creative artists.

Smith (2006) compared the network properties of several populations, including rappers, movie

actors, board directors, and Brazilian pop musicians. Degree-degree correlations between artists

were relatively moderate for board directors (0.28) and movie actors (0.21), but lower for jazz

musicians and rappers (0.06 and 0.05, respectively). The correlations found for the present study

(0.04) are almost exactly equal to these lower values. Uzzi (2008) explained this trend as

reflecting the extent to which the most connected artists collaborate, such that higher values

indicate more collaboration between these individuals. In cases where the value is lower, Uzzi

commented, “…assortative mixing levels may be limited when the unique creative styles of

superstars may be incompatible” (2008, p. 4).80 Established musicians with more connections

may not have the creative energy to work with each other, resulting in a more dispersed pattern

of interaction with musicians. This study indicates that this may be the case for musicians in

various Chicago jazz and improvised music communities.

Jazz Communities as Attribute-Related Clusters

Previous studies have commented on the structure of communities, or tight-knit clusters,

revealed in social networks (Gleiser & Danon, 2003; Girvan & Newman, 2002; Arenas et al.,

2004). Here, hierarchical clustering (HC), Girvan-Newman clustering (GN), and density

measures indicated that three unequal, different communities form a part of the Chicago jazz and

improvised-music network. The HC algorithm grouped musicians into 5 clusters,81 whereas the

GN method provided three (figure 4.3). This study’s number of final clusters was based on the 80 According to Uzzi (2008), assortative mixing is directly measured by the degree-degree correlation, and higher assortativity values indicate more connections between well-connected actors. 81 HC iteration 211 placed musicians who were less likely to be named in cluster 4, and one pendant, which was excluded from additional analyses, in cluster 5.

201 observation that the clusters joined together after the removal of broker82 nodes, including

both participants (e.g. BP) and non-participants. Even though SNA studies typically analyze the

clusters as separate components (Giaqinto et al., 2009), the present study differed because of the

large number of liaisons and representatives.83 Even though some of the participants assumed

these roles in the network, most of their ties seemed to come from the community to which they

belonged, structurally speaking. In other words, only one or two of their ties came were directed

to or from outside communities (e.g. DM, PM, QK). In their article, Arenas and colleagues

(2004) assert that “there is no characteristic community size,” and that separation of these

communities depends on various hierarchical levels, each organized in a similar way. Here, the 4

HC groups and 3 GN clusters were likewise unequal in size, and furthermore, there were many

hierarchic levels, especially provided by the HC algorithm, included in the results. This confirms

the notion that jazz communities, like others, are composed of subgroups, which are themselves

composed of smaller subgroups, were further composed of pairs of collaborators, and finally

composed of individuals.

This study further showed that community groups related to self-ratings of community

affiliation and density values, as well as to participant attributes of age and preferred

performance style. The correlation between self-ratings and density values indicates high

agreement between systematic surveys, observations, and cognition of the self, which some

studies have disputed (Krackhardt & Porter, 1985; Krackhardt 1987a). Regarding participant

attributes, Gleiser and Danon (2003) found that communities of jazz musicians and bands from

82 Those with higher betweenness-centrality, who held special contact positions “between” clusters, or who were connected to more than one cluster. 83 Hanneman and Riddle (2005) discuss several types of brokerages, including liaisons, relations between groups of which they are not a part of, and representatives, who are the “contact people” of the group from the perspective of the outsider. These roles were not discussed further, since the ultimate purpose of using SNA was to group musicians into clusters rather than analyze structural and organizing factors.

202 the 1920s correlated significantly with geographical location of recording (e.g. Chicago, NY)

and race; however, Giaquinto and colleagues (2009) showed that modern jazz similarity

networks84 were influenced by other attributes, namely:

• Vocal jazz • Jazz influenced by other genres like Rock, Funk, and Pop • Contemporary Jazz • Smooth Jazz • Latin Jazz • Post Bop • Avant-Garde Jazz (p. 5)

With the exception of “Vocal jazz,” which reflects a particular instrumentation, these

components are separated by differences in genre. Another study (Killworth et al., 1990)

indicated that age played a significant role in the formation and size of personal networks. Even

though the present study did not collect information on personal circles as the Killworth study

did, the amount of correspondence is similar, considering the high friendship ratings. On a

personal note, as a participant-observer of performance and social events, I have frequently

overheard musicians elaborate on how close they are to those with whom they share musical

experiences. The influence of age in the present study, then, may reflect participants’ personal

preferences of performing with musicians in one’s social circle rather than pursuing purely

professional relationships. Or, the effect may pull in the opposite direction, whereby

accumulated musical experiences create opportunities for personal friendships. In a study of

classical-musician networks, Stebbins (1989) hypothesized that multiple identities, such as

“orchestral identities” (e.g. section member, concertmaster), “instrumental identities” (e.g. brass,

string, reed), and “performance identities” (e.g. orchestra, chamber group, solo), result in “shared

concerns” (p. 230). For example, violinists tend to be closer to one another in network

84 Drawn from recommendations provided by the All Music Guide.

203 relationships because of proximity and rehearsal time; and concertmasters are “more likely to

establish ties with players outside their occupational stations” because of “social-class

dimensions” (p. 239). Although Stebbins’ results were variable among identities, social roles

have been seen as a driving force in social relationships (Morgan & Spanish, 1985; Morgan &

Schwalbe, 1990). Further research on the dynamic qualities of such relationships will be required

in the future to understand how the formation of musical identities shapes collaborative practice.

Association Task: Semantic Memory for Eminent Jazz Performers

The responses to this study’s association task were interpreted above as indicators of

semantic memory content and structure for eminent recordings. In addition, the impact of

participant attributes and affiliations were evaluated using statistical measures. The participants

were asked to associate 3 musician names with each excerpt (15) and to provide self-reflections

of their approach during the task. They also guessed the main performer in the excerpt, and after

the correct answer was revealed, they rated the excerpt’s typicality and the influence of the

performer on their music. The ensuing coding procedures resulted in two forms of data,

qualitative categories and quantitative agreement scores, both related to excerpts as well as

attribute, accuracy, and rating variables. The analysis of these data through descriptive and

inductive statistical procedures showed multiple complex interaction patterns between all these

variables, thereby indicating that the process of assigning referential meaning depends not only

on absolute features of the stimulus, but also on affiliation-specific representations.

Overall, the participants in this study associated a broad range of names with the

experiment’s excerpts, demonstrating a diversity of associative listening styles. Although the

variability in names, instruments, and criteria are in opposition to the clear-cut definitions and

204 category boundaries found in word studies, one can discern some aspects of internal

structures for performer categories (Rosch, 1975). Each excerpt primed associations that were

relevant to a performer’s identity; thus, responses reflected on an integration of excerpt features

(directly related) and biographical information (indirectly related). Associations that directly

depended on excerpt information included musicians on the album from which the excerpt was

extracted, notably, Miles Davis’ Kind of Blue and Ornette Coleman’s The Shape of Jazz to Come.

Both of these recordings have been accredited the status of iconic albums that shaped the

development of the jazz genre. Just after its release, reviewers praised Kind of Blue as “a

remarkable album,” one that “will never be duplicated” (Down Beat, 1959; Garrigues, 1959).

Despite its apparent melodic and harmonic simplicity, the album impacted musicians and the

commercial market (Carr, 1999; Kahn, 2000; Nisenson, 2001). In an interview conducted by

Kahn (2000), Herbie Hancock reflected on the album’s influence on his generation of musicians:

“It presented a doorway for the musicians of my generation, the first doorway that we were

exposed to in our lifetimes…When Kind of Blue came out, I had never even conceived…another

approach to playing jazz” (p. 179). According to Kahn, the album sold over 87,000 copies by

1962 – an unheard of feat for the jazz industry – and since then, it has sold millions, making it a

multi-platinum recording. The Shape of Jazz to Come has received similar praise from musicians,

but has had less impact on the commercial market. Ake (2002) linked the music of this group to

the first installments of “free jazz,” and interpreted the composition Lonely Woman as

challenging “accepted notions of masculinity in jazz” (p. 25). These assessments by critics and

historians, when considering this study’s evidence of musicians’ associations of excerpts with

performers on the albums, shows that musicians’ representations for eminent performers include

album-related information. Professional jazz musicians have developed categories for each of

205 these albums, primed by the excerpts, each with a unique set of items, related directly to the

album (Medin & Shaffer, 1978). This categorized organization relies on a literal representation

of an item (e.g. Ornette Coleman’s solo on Lonely Woman), which allows for further retrieval of

category content (e.g. Don Cherry, Charlie Haden, Billy Higgins).

Indirect associations, as found in this study, included musicians who did not perform on

the albums, but were related by instrument or collaborator matches. The effect was particularly

clear for the Roach, Montgomery, Monk, Holiday, Mingus, and Hawkins excerpts. Two

possibilities could explain these tendencies. First, the presence of the performer, relative to other

instrumentalists in the excerpt, may have influenced the responses; the Roach and Mingus

excerpts only included drums and bass solos, and musicians associated with the excerpt were

musicians who played drums and bass, respectively. Second, the participants may have been

unsure about the performer’s identity, so information regarding biographical, or collaborator

information was less primed for retrieval. The low accuracy scores for the Roach and Mingus

excerpts lend support to this theory; but, it does not hold up for the other excerpts. The responses

here may be related to Quillian’s (1969) token and type nodes for a network structure, which

includes semantic similarity, dictionary-type content, and active control over retrieval (Collins &

Loftus, 1975). Two responses that exemplify this structure are: Wallace Roney isa type of Miles

Davis, and King Oliver isa type of Louis Armstrong. The feature comparison model emphasizes

the necessary features similar to both musicians, such as “plays the same instrument,” “has a

distinctive tone,” or “learned from the same teachers” (Smith et al., 1974). Other examples, such

as the Duke Ellington—Billy Strayhorn or Coleman Hawkins—Lester Young association, rely

on more specific links, such as “worked with” or “was a contemporary of.” In previous studies,

additional types of links are not situated at this level in the network hierarchy (Collins & Loftus,

206 1975); however, short distances between items and strong local clustering are two

characteristics unique to the kind of “small-world” structure evidenced in the present study

(Steyvers & Tenenbaum, 2005). This kind of detail is commonplace in models of semantic

memory for musicians, since they tend to engage higher-level thought processes in their

interactions with recorded music (Bangert et al., 2003). Moreover, the responses of this study’s

participants relied not only the extraction of features in the stimulus, but also on information

about the album personnel, which supports an integrative model of music processing

(Biederman, 1987).

The instrument and criteria responses detailed here further specify professional

musicians’ content of semantic memory systems for eminent performers. Since saxophonists and

pianists were over-represented in the excerpts, participants listed more musicians who played

both of these instruments. This is explained by a typicality effect, as the saxophone is often

viewed as an iconic symbol for jazz (Gelly & Bacon, 2000). Iconic representations and historic

accounts of jazz tend to contain reference to instruments that had the highest frequency scores in

this study: saxophone, piano, guitar, bass, drums, trumpet (Martin & Waters, 2002). In addition,

same-instrument associations were observed, which suggests that instrument is a defining feature

of a performer’s identity. The unaccompanied drums solo in the Roach excerpt primed listeners’

representation of Max Roach the drummer instead of Max Roach the political activist or the

Sonny Rollins collaborator.

The participants’ criteria for their associations seemed to involve similar, identity-related

characteristics. First of all, the present results demonstrated a broad range of criteria employed in

the task, supporting the view of different cognitive listening styles for different listeners (Myers,

207 1922; Kreutz et al., 2008). Myers (1922) found that participants described music using four

aspects of music,

i) The intra-subjective: for the sensory, emotional or conative experience which it aroused.

ii) The associative: for the associations which it suggested. iii) The objective: for its use or value considered as an object. iv) The character: for its character personified as a subject (p. 54).

showing breadth of qualitative descriptions provided by his participants and relating them to

personality differences. The present study painted a picture similar to the disparity of references

implied by Myer’s second aspect, but these individual differences depended on excerpts and

performers. In general, this study’s participants’ criteria focused more on musical and approach

elements, relating to both concrete (e.g. melodic patterns, time signature, tone) and abstract (e.g.

emotion, vibe, expression) facets of music. Historians have explicitly characterized these

eminent performers by their musical contributions, such as the use of octaves in Wes

Montgomery’s improvisations or the “brilliant use of pacing, structure, and rhythmic belief” in

Coleman Hawkins’ version of Body and Soul (De Stefano, 1995; Williams, 1993, p. 76). Other

musical identities were defined by participants’ knowledge of musician affiliations, such as

Miles Davis’ impeccable ability to form ensembles and Ornette Coleman’s unique roster of

collaborations. In addition, Louis Armstrong and Charlie Parker were seen to be related more to

the style criteria, supporting traditional biographical accounts of their contributions to New

Orleans jazz and bebop (Williams, 1993). The performer who was distinguished mostly by the

aspect of influence given was Thelonious Monk. This observation echoes comments by Williams

(1993), who described Monk as “one of the most original, self-made talents…Monk was not only

a productive musician after more than fifteen years of musical activity, but seemed still to be a

growing artist exploring his talent and extending his range” (p. 150). Despite early critiques of

208 his unabashed approach and angular improvisations, Monk came to be viewed as a creative

genius, who influenced the future of jazz performance and composition. Overall, the association

responses illustrate the multifaceted quality of musician categories and include musical

characteristics such as instrumentation, style, collaborations, and various levels (e.g. surface or

deeper) of musical features. Eminent musician categories have different levels of defining

features that depend on these and other attributes, and are essential to these categories’ many

meanings (Smith et al., 1974). Sets of defining features are communicated to a listener via sound

and biographical accounts, and they tend to vary between performers, as was reflected in the

agreement scores.

Association Task: Organization of Semantic Memory

The organization of associative content seems to be affected by the way in which the

stimulus primes a part of semantic memory. Deliège and colleagues (1996) have argued for the

advantage of cue abstraction in this process: “It appears that processes of cue-abstraction can

account for relations in cognition between components of the piece that exist not only at the

same hierarchical level but across hierarchical levels” (p. 155), although her work deals

primarily with explicit musical features. In the present study, agreement scores indicated the

availability of certain items in memory to describe a performer, given the cues abstracted from

the stimulus. In many semantic memory studies, faster word judgments specified the more

exemplary items in memory structures (Collins & Quillian, 1969; Rosch, 1975b). However,

reaction times were not a dependent variable in the present study; instead, agreement scores were

used to represent an item’s degree of influence in associative memory. Although some of the

name agreement scores were rather low, the differences between excerpts illustrate clearly

209 divergent associative representations, which were dependent on performers. The participants

agreed the most on association names for the Billie Holiday excerpt, which was distinctly

defined by its inclusion of voice. This is reminiscent of a study on audio identification which

suggested that performer identification is easier for vocal, as opposed to instrumental, segments

in pop and rock music (Berenzweig et al., 2002). The processing system proposed by the authors

apparently found qualities of the vocal segments to be more stable across performances than in

the instrumental portions, thus contributing to identification success. This may be the same in the

present study, as accuracy scores for the Holiday excerpt were relatively high. Likewise, a

positive correlation between agreement score and accuracy was evident for most of the excerpts,

suggesting more stable representations for certain performers.

The consistency in this study’s findings for name associations could also be explained by

rated typicality. Despite the inclusion of well-known excerpts for all the performers, the

participants’ responses were more homogenous for the most commercially popular tracks, such

as God Bless the Child, So What, and Body and Soul. This seems to relate to Rosch’s study

(1975b), which showed that people agreed more on typical representations of categories; blocks

were rated higher than sandbox for the toy category. An additional explanation would rely on the

retrieval of musical schemata. In their musical recognition study, Krumhansl and Castellano

(1983) found that inclusion of diatonic, as opposed to nondiatonic, tones resulted in memory

advantages for chord sequences. The authors noted, “…this supports the view that the sequence

engages a subset of the internal representation of chord relations that is organized according to

key distance” (p. 331). In the present study, some participants’ advantages in accuracy seem to

demonstrate that the retrieval of more typical names facilitates processes of recognition. The

processing implications here and in previous studies may involve the same set of factors.

210 Instrument-related information seemed to play a more influential role in our

participants’ semantic memory systems, placing it at a higher level within the hierarchical

structure. In general, the higher instrument agreement scores suggest that the first subordinate

level for each performer concept included his or her instrument. The best examples of this were

evident in the following superordinate-subordinate relations: Max Roach isa drummer, Wes

Montgomery isa guitarist, Charles Mingus isa bassist, and Coleman Hawkins isa saxophonist.

As a cue available to listeners, density of instrumentation influenced consistency of response,

since excerpts with fewer instruments (Roach, Mingus, Hawkins, and Montgomery) had the

highest agreement scores, and excerpts including larger ensembles (Ellington, Pastorius,

Armstrong) had relatively lower scores. This trend could also be influenced by the breadth of

known collaborators, as reflected in lower scores for Davis and Ellington, known for their

ensemble formations.85 On the contrary, the tendency to rely on a performer’s instrument may

relate to an issue of recognition. Previous studies have argued that activation of tonal scale and

contour-related schemata aids in the process of remembering pitches and melodies (Dowling &

Fujitani, 1971; Dewar et al., 1977; Deliège et al., 1996); however, Dowling’s experiment

(1978b) showed that listeners confused same-contour melodies in the memory task, producing

more false alarms. By way of comparison, in the present experiment, lower agreement scores

were observed for inaccurate performer identifications. There were also several cases in which

participants named the performer during the association task, but then guessed as incorrect

performers. This evidence is consistent with the detrimental effect of similarity in representations

of music.

85 This and other statements like this are not meant to deter away from additional contributions of these artists (e.g. Ellington’s orchestrations and Davis’ trumpet sound). Instead, the goal is to highlight some of the defining features of these performers.

211 Overall, the association criteria agreement scores convey a different, more varied

picture of participants’ mental organization of performer concepts. These disparities imply that

the structure of cognitive representations for eminent performers morphs over time, between and

within excerpts. Along these lines, Myers (1922) proposed a dynamic set of interactions between

aspects of cognitive listening styles, in which one would become activated before the other

“inhibited and replaced” aspect (p. 57). He used terms such as “higher” and “lower” to describe

this, implying a hierarchical mapping of the items in vertical space. In the present study, higher

criteria scores did not necessarily imply a stable, multifaceted performer identity, especially

since scores were distributed across eleven categories. On the contrary, higher agreement scores

generally indicated heightened response for one of the criteria. For example, the collaboration-

Davis pairing showed that information regarding his musical relationships (e.g. “he was in

Miles’ band) was more available to listeners than other characteristics of his music and approach.

Mingus’s associations tended to be discussed in terms of distinct musical features, such as his

bass sound and vision of ensemble texture. Musical techniques, such as “development of lines,

motives, and antiphonal effects,” as well as deep involvement in musical composition, have been

of paramount importance in musicological descriptions of Mingus’ music (Williams, 1993, p.

223; Mingus & King; 1971). However, Mingus was also known for the musical collaborations he

formed with musicians like Eric Dolphy, and for his personality, which was not evident in the

data presented here. Two theories could account for this trend. First of all, information relevant

to these features may not have been primed by the stimulus; for example, the Mingus excerpt

was characterized by a bass solo, involving very sparse inclusion of horns at its close. Thus, most

listeners would comment on the bass rather than the ensemble playing. Second, listeners perhaps

did not recognize the performer, and thus, only used the acoustic cues from the stimulus to

212 approach the task rather than other associations. This explanation does not hold up well,

though, since accuracy did not statistically interact with agreement scores. Another potential

explanation has to do with the association between high agreement scores and influence ratings.

Although prior studies (Berlyne, 1970; Hargreaves, 1984; North & Hargreaves, 1995) have

explained familiarity as a significant factor in musical taste, generally, influence has not been

considered as an influential variable. Overall, this study showed that listeners agreed more on the

identities of the performers that they were most influenced by, which suggests that particular

portions of these semantic representations are more accessible and thus, stable over time.

Attribute-Based Contexts of Associative Representation

As detailed in chapter 2, experience and sociocultural affiliations influence the way in

which individuals hear and remember musical objects. Demographic characteristics, like age,

ethnicity, gender, and socio-economic status, play a role in musical taste; but, strength of

influence varies between individuals, and researchers disagree on its extent. The majority of

demographic studies in music have been designed to uncover personality differences and

consumer behavior (Fung, 1994; Furnham & Walker, 2001). The current study examined the

extent to which experience, demographics, and sociocultural affiliations affected professional

musicians’ representations of musical associations. Even though this study’s results showed a

significant influence of many of these variables on categorical task responses, the Chi-Square

analyses lacks in the ability to highlight specific differences. I will now briefly consider

demographic variables, seeking specific details of the content of items in semantic memory.

Sociocultural affiliation variables and performance style characteristics affected participants’

213 categorical responses and agreement scores, illustrating the strength of these variables in

determining content and accessibility of items in semantic memory.

Examination of participants’ categorical differences showed effects of age and

experience. As seen in the literature review, individuals identify with groups on the basis of their

values, experiences, attitudes, and interests, which are often determined by age group.

Associative responses to music in this study were no different; specialized knowledge based on

age and professional experience had a significant effect on associative representations and

processing of music. Overall category distributions were similar between participant-attribute

groupings, but there were slight contrasts that warrant some speculative comment. Listening to

music recruits a complex set of interactions between attention and memory (Deliège, 1996).

Here, age differences showed heightened melodic instrument and musical criteria responses for

younger, but heightened rhythm section instrument and blank criteria responses for older

participants. Interestingly, Salthouse (1996) proposed a theory that processing speed and

relevancy decrease with age, due to time and resource limitations. This finding may be related to

the present study, in that older participants responded less typically to prominent instruments and

music-related strategies than did those who were younger. Although perceptual effects of

experience have also demonstrated slower, less accurate responses to memory and perceptual

tasks for less-experienced musicians (Meinz, 2000) the same trends were not observed in this

study. Overall, the impact of age and experience on response typicality must be carefully

interpreted, especially considering their lack of influence on agreement scores.

Domain-specific knowledge, as determined by education and performance style

preferences, interacted significantly with categorical responses and agreement scores. According

to Bjorkland and colleagues (1990), “domain-specific strategies can directly facilitate task

214 performance, as can context-independent strategies, both of which can in turn affect

subsequent metacognitive processes” (p. 97). This study focused on children, but it appears here

than this also applies to adult populations. Here we see correspondences between response and

participant instruments, which exemplify and support the hypothesis that listeners attend more to

sounds with which they are more familiar (Janata et al., 2002). In addition, participants with

collegiate-level music degrees tended to list more atypical instruments (e.g. composers, string

players), and their decision criteria focused more on collaborations than those without higher

education, who tended to focus more on rhythm section players and musical approach criteria.

These findings imply that formal education in music provides information beyond basic musical

characteristics, such as that related to history, biography, and canonical recordings. Indeed,

universities have demonstrated their efficacy in transmitting these tidbits of knowledge in jazz

history and listening courses – but the informal culture of non-academic learning offers its own

specialties (Prouty, 2002; Whyton, 2006).86 Although many music cognition studies attempt to

make distinctions between musicians and nonmusicians, none seem to ask respondents to specify

their performance style preferences. Such performance preferences imply that musicians educate

themselves on one style over others, producing a musical specialty. This study demonstrated the

largest categorical differences between musicians who play jazz versus those who also included

other styles, such that the latter were more concerned with interaction in the rhythm section than

with melodic patterns. Furthermore, higher agreement scores for the jazz group suggested that

they might have more solidified cognitive representations for jazz. Considering that the stimuli

included many central examples from the canon of recorded jazz, these findings also support the

86 The differences between these models will be explored further in the section on implications.

215 notion that domain-specific experience affects content and accessibility for performer

knowledge among professional musicians.

Finally, the significant interactions seen here between community affiliations and task

responses shed light on the relationship between sociocultural variables and associative

processing. Clustering data were significantly related to distributions of instrument and criteria

categories as well as typicality of responses; however, density of connection was not. Name and

criteria agreement scores, as well as accuracy and influence ratings, also interacted with cluster

groups and community affiliation ratings, although they did not interact with the network density

measure. Instrument agreement scores were unaffected by the sociocultural variables, most likely

since this feature was most affected by instrument-specific experience, which transcended

community affiliations. Even though cluster groups87 were associated with many of the

participants’ attributes, performance style was the only variable that produced significant

interactions with agreement scores, influence ratings, and accuracy. For example, all the

participants in HC/GN group 3 played jazz and improvised music, and their agreement scores,

influence ratings, and accuracy were lower than those of HC/GN groups 1 and 2, whose

members characterized themselves not only as jazz musicians, but also as competent in other

musical styles. Thus, a combination of domain-expertise and collaborative relationships interact

with content and accessibility of associative representations in memory.

Instead of relying on observational accounts or predetermined categories, the social

network clusters found in this study highlighted participant-defined connections, which provide

indications of collaborator affiliation and resultant musical identity. In so doing, this study

demonstrates the significance of socio-musically constructed knowledge systems in cognitive 87 Except HC group 4, which was the cluster that contained participants with fewest connections, rather than distinction commonalities in attributes.

216 processing of music and information about music. These systems seem to be built up by

informally shared processes of discussion, learning, and listening within communities, which

may parallel in some regards the process of musical taste development in adolescent

communities (Frith, 1981). Interacting with music provides opportunities for musicians to shape

and secure their sense of identity, and is thereby reliant on processes of repeated listening and

interest in particular recordings: “…what is sought is relational, not concrete, and with both the

musical work and the self, the object sought is a relational connection uniting a set of objects

found through relational connections” (Gracyk, 2004, p. 17). Gracyk’s argument can be further

elaborated to incorporate the personal connection between the search for meaning in the work

and in the self. This study showed that affiliations guide listeners’ attention to cues in the

stimulus, such as Thelonious Monk’s penchant for stride playing, present in the “Round

Midnight” excerpt, that listeners either pursued or ignored in the retrieval of associative memory.

For this particular excerpt, no information, aside from the characteristic “Monkisms,” suggested

that the performer collaborated with other musicians; however, many chose to hear Monk the

collaborator, which shows that listeners were drawing upon stable representations of this

performer and his music. Thus, music presents opportunities for associative processing within

itself, the listener, and the abstract relations between them as defined by the listener.

To summarize, the association task illustrated that listeners show their knowledge of a

musical style to be a set of interrelated representations in their minds. The content of this

knowledge depends on associations with similar musical styles, based on a number of

dimensions including musical features, domain-specificity, and sociocultural affiliations. These

knowledge schemes are routinely used to interpret and experience works of music.

217 Descriptor-Matching Task: Cognitive Instantiations of Performers

The descriptor-matching task investigated the way in which musicians describe

performers without the context of actual acoustic musical stimuli. The results were seen as

revealing similarities in representations for eminent performers. The task instructions asked

participants to choose 3 out of 24 musical elements to describe each performer prompt.

Statistical analyses assessed differences between performers, as well as interactions with

participant attributes, ratings, and accuracy on the association task. The patterns of descriptor

distribution showed that the participants were aware of performers’ common distinctions and

musical identities. Beyond these, only the attributes related to domain-specific knowledge

influenced responses to the task.

The results highlighted the most important characteristics of a performer’s musical

identity as encompassing phrasing, emotion and expression, timbre, blues influence,

improvisational creativity, and virtuosity. Blues influence and improvisational creativity are

inherently connected to jazz, as these qualities owe much to humble beginnings and subsequent

developments of New Orleans jazz (Gioia, 1997). Participants’ attention to the quality of

virtuosity pays homage to the influential masters of jazz, who worked to hone their craft and the

shape future developments in the genre – the very definition of virtuosity (Gebhardt, 2001).

Although the way in which jazz musicians swing has been a topic of much scrutiny, other

research has indicated that phrasing and expressive timing also contribute to a performer’s

identity. In his study relating the performance of jazz melodies to structural properties of music,

Ashley (2002) examined similarities and differences in expressive timing and phrasing between

jazz performers, including Miles Davis and John Coltrane, on two works. His results showed that

performers treated the melodies of these works deliberately, such that “…expressive alteration of

218 “nominal” rhythmic patterns in a manner related to structure is typical of jazz musicians’

strategies” (p. 331). However, Ashley warned against over-generalizing methods of phrasing and

timing, as each performer paints a unique picture of the structure with his or her own devices.

Timbre, the musical dimension of sound and tone quality, has been discussed as a complex set of

interactions between spectral energy distribution, spectral fluctuation, and attack point (Grey,

1977). With respect to jazz musicians, specifically saxophonists, Gridley (1983) suggested

timbre to be the most important quality available to listeners. He compared adjective descriptions

of saxophone timbre to those from jazz texts and record reviews and found disparity in

qualitative judgments, but similarities with bipolar-rating scale (e.g. rough vs. smooth)

judgments. In another experiment (Benadon, 2003), experienced jazz musicians identified

saxophonists from recordings within 2 to 3 seconds. This suggested that experienced jazz

listeners were inclined to attend to timbre, non-quantized rhythm, and expressive gestures as

opposed to pitch, rhythm, and contour. In the present study, such tendencies were evident in the

relatively lower usages of qualities that were less influential to a performer’s identity, such as

repetitiveness, consonance, voice-leading, extramusical associations, texture, communication and

interaction, and contour.

The prompt-descriptor task’s results related to common interpretations of each performer.

In biographical sources, liner notes, and jazz history texts, scholars have specified and

stereotyped aspects of musicians’ performance style. For instance, one compelling aspect of

Charlie Parker’s playing, commented upon in Tesser (1998) was the “unprecedented imagination

on unexpected chord progressions at unimagined tempos” (p. 63). Appropriately, respondents

tended to choose virtuosity as a primary descriptor for Parker. Blues influence was also paired

with Parker, supporting jazz historian Martin Williams’ (1993) statement that “Charlie Parker

219 was a bluesman, a great natural bluesman without calculated funkiness or rustic posturing”

(p. 142) – not to mention all of the melodies that Bird constructed for the 12-bar blues form.

Another prime example of an appropriate match was that between Duke Ellington and

composition and orchestration, since this characteristic of his identity was repeatedly evidenced

in the form of recordings and has since become the subject of analytical scrutiny (Ellington,

1976; Gioia, 1997). The current study’s participants seem to indicate a network hierarchy of such

associations; thus, one could argue that each performer exhibits all 24 qualities, but that only a

select few act as defining features at superordinate levels in the hierarchy. For instance, even

though Billie Holiday was not typically described as a composer or an orchestrator, she was

involved with the composition process of God Bless the Child (Clarke, 2002). On the other hand,

Clarke (2002) quoted one of her collaborators as saying “she has never written a line of words or

music” (p. 191). These would imply that her impact as a composer or orchestrator lies lower in a

musicians’ hierarchical representation of her musicianship. Overall, the pattern of responses seen

here suggest that there are typical ways of describing musicians and that these depend primarily

on features that commonly define them. My results further suggest an integration of set-theoretic

and hierarchical network models for performer semantic systems (Collins & Quillian, 1969,

1970; Smith et al., 1974).

This study’s agreement score trends show the degree to which participants employ shared

representations for each performer. Semantic memory contains a range of information, and

certain features are weighted more than others, as described in interactive-cue processing models

(Medin & Schaeffer, 1978). The Brunswick face experiments illustrated a processing facilitation

for items related to an exemplar. The present data revealed similar cognitive tendencies, as were

evident in higher frequency scores for one or two primary descriptors. These defining

220 characteristics might contribute to a performer-related exemplar, which was retrieved during

the task. Examples of this might include Billie Holiday with primary elements of emotion and

expression, phrasing, and timbre; Duke Ellington with composition and orchestration; John

Coltrane with harmony and tonality and virtuosity; Max Roach with rhythm and time; and Jaco

Pastorius with virtuosity. Lower scores seem to indicate that participants agreed less on defining

characteristics for particular performers, including Sonny Rollins, Herbie Hancock and Ornette

Coleman. It could be that the participants’ representations of these performers contain a more

distributed network of features, as opposed to those that concentrated on one or two defining

features. These patterns may also be a function of excess knowledge accumulation, resulting in

retrieval of redundant information. Described as the “expertise reversal effect,” this phenomenon

occurs when “cross-referencing and integration of related redundant components” that “require

additional working memory resources and might cause a cognitive overload “ (Kalyuga et al.,

2003, p. 24). However, this explanation may not be appropriate for the descriptor task results,

since association task accuracy and higher influence ratings were linked to heightened agreement

scores. This study’s patterns of results confirm the notion that listeners develop schematic

representations for eminent jazz performers and further elaborate upon the content and structure

of these representations.

Descriptor-Matching Task Attribute-Based Influences on Performer Representations

Unlike the association task, the results from the descriptor task revealed a low impact of

attribute variables on performer semantic representations. Overall, the lack of category-

distribution differences between groups illustrates the ubiquity of shared understandings for each

jazz musician’s musical identity. In addition, my results demonstrated the influence of domain-

221 specific knowledge, in the form of education and performance style. Participants with more

formal education and a preference for performing jazz were more likely to choose terms that

were commonly used to describe each performer, as demonstrated by disparities in agreement

scores. This trend may be a consequence of the paradigms of institutional learning and their

concentration on accepted jazz canons. On the functionality of these canons, specifically that of

iconic jazz-musician images, Whyton (2006) noted,

From establishing archives to designing curricular with supporting materials, the canon’s promotion of objective standards and a single- strand chronological narrative allows for benchmarking and uniformity both with an across institutional boundaries (p. 75).

The effect of these programs on the way in which musicians form their representations could be

elucidated by explicating specific interpretations of familiar music, as demonstrated in the results

of the association task. Moreover, the pairing of musical features transcends age, experience,

instrument, and community-related boundaries. In an experiment with some connections to this

study, Darrow and colleagues (1987) found that American and Japanese listeners chose similar

terms to describe a broad range of styles in Western music. Their analyses accentuated cultural

differences in chosen descriptors for Eastern music. The authors suggested that technological

advances provide Japanese listeners with more access to Western music than Americans have to

Eastern music. In contrast, the present study showed that all participants had vested interests in

jazz, and all the performers represented iconic examples of jazz musicians in the canon. Perhaps

if the list of performers had included musicians who embodied styles ancillary to jazz, such as

improvised music, community-based differences would be accentuated. Fundamentally, this

study’s results demonstrate the importance of domain-specific exposure on performer

222 representations, especially those in which musical descriptors are used to define performer

identity.

Suggestions for Future Research

This study has provided a detailed look at the content, structure, and function of semantic

memory systems in a number of different communities of professional musicians. The

collaborator task discovered patterns of interaction within musician communities, highlighting

the importance of local relationships in global network components. The main contribution of the

association and descriptor-matching task was the elucidation of content and structure in semantic

knowledge, as related to performer and participant identity. These two tasks highlighted

differences in the content and structure of memory for eminent jazz performers, who were still

dependent on prototypically relevant musician identities. Whereas participants’ reactions to

recordings produced community-affiliation effects, their responses to the names of musicians

drew attention to the impact of domain-specific knowledge. Similarly, differences were found

between free responses versus forced-choice interpretations of stimuli. The present study thus

sheds light on processes of associative meaning in music; however, many questions warrant

further inquiry.

First, systematic views investigating jazz and improvised music communities have the

potential to advance studies in ethnomusicology and in social network analysis. Instead of

relying on more traditional techniques of participant observation and multiple interview sessions,

which are often tedious and difficult to interpret,88 this study relied on methods of SNA to

88 This is not to say that ethnologies are unreliable; rather, this technique is time-consuming and relies on cultural immersion, which may be too invasive for the social scientist. In the present study, the focus group survey showed that musicians had different names and definitions for communities, such as

223 calculate broad network patterns as well as community affiliations. Future sociocultural

studies might consider measuring group affiliation with SNA methods, rather than relying on

rating scales with predetermined categories for ethnicity or social-group. However, this study is

pioneering in its effects, and has neither the depth nor complexity of statistics characterizing the

majority of research in the SNA literature (Wasserman & Faust, 1994). The bulk of these studies

focus on the effect of local social processes on structural and organizing network properties. As

it is difficult, if not impossible, to survey entire music communities, due to the sheer number of

participants, future studies might consider smaller groups and their ties to the community at

large. For instance, since this study’s HC/GN cluster 1 has a number of diverse patterns of ties to

HC clusters 2-4 and GN clusters 2-3, this community could be examined as a closed entity that

includes several brokerage links to “outsiders.” Such an approach would also allow for further

examination of crossover nodes, or musicians who are highly involved with more than one

community, including questions such as: do these practices lead to heightened personal success?

Longitudinal studies, examining dynamic changes of communities and individuals, could be

useful for predicting future successes and failures of genres, given the process of interaction for

various music scenes (Wasserman, 1979). The networks produced from these studies could be

presented to participants for purposes of verification and elucidation of community boundaries

via social practice. This approach may have the potential to detail our knowledge of inter- and

intra-group relations beyond the labeling of jazz musicians as social and musical outsiders

(Merriam & Mack, 1959; Becker, 1963).

Future research on associative musical meaning should expand upon the previously held

notion that concrete musical structures dominate semantic memory of music. One of the defining “North-side/South-side”, “Avant-garde/Straight-ahead”, “Free”, and “Jobbing”, that were difficult to collapse into unified groups.

224 characteristics of music is its ability to evoke images of events, situations, and objects, and

the extent of agreement for these associations could reinforce the idea of multiple, connected

meanings, as delineated by Meyer (1956, 1973, 1989). Given the present study’s findings,

associative properties of music clearly extend to jazz and can be represented not only by sound,

but also by visually-presented names of musicians. The organization and structure of

associations in memory could be examined further with reaction time and judgment studies. For

example, would a sample of Charles Mingus result in faster and more accurate recognition of his

collaborator Eric Dolphy, or his influential forebear, the bassist Jimmy Blanton? A more in-

depth examination of familiarity, preference, and identification of stimuli may also be valuable,

as the way these variables influence the experience of music is still relatively unclear

(Hargreaves, 1984; Teo et al., 2008). Such studies may help to provide a view of experience and

expertise that is focused on the stimulus, rather than on predetermined classes of genre and

education.

Other possibilities present themselves. For example, instead of presenting listeners with

15 different performers, concentrating on a sole performer’s repertoire and legacy might uncover

more details related to life history and musical “phases.” Miles Davis is a prime example of an

artist with a prolific and stylistically variant recording career (Szwed, 2002). Musicians’

responses to excerpts across his career might uncover exemplar-based partitions of their

memory, thereby supporting the notion of hierarchically structured systems with multiple levels

of defining and characteristic features. An alternative to this would be to extend the range of jazz

styles included, such as American and European examples of “free jazz” and “improvised

music,” which seem to play a larger role in modern interpretations of jazz (Jost, 1981; Ianuly et

225 al., 2009). Responses to these “on-the-fringe” styles might accentuate affiliation- and

domain-specific differences, further defining community boundaries.

Finally, qualitative interviewing and observation techniques couple expand our

understanding with insider knowledge. As revealed in the focus group sessions, participants’

views are disparate on the surface, but supplementary participant validation, or member checks,

could provide a naturalistic form of evaluating these findings (Lincoln & Guba, 1985). Questions

to focus on in these sessions might include: How does your musical community influence your

listening experience? What musical patterns do musicians comment on when they discuss

recordings with you? How are these utilized in your understanding of the record, performer, and

subsequent extensions of this music? Although observational interviewing techniques are

particularly invasive and tend to overstep boundaries and conventional social practice, they can

provide further insight on the cognitive and social impact of collaborative interaction. Future

research agendas should address these pressing concerns, especially as they contribute to

practical implications of education and professional development.

Practical Implications for Music Educators

The present study’s results have the potential to inform the practical concerns of

professional music educators, because of their relationship to the learning process. According to

Dunscomb and Hill (2002),

Jazz education is about teaching students skills in the art of improvisation, helping them acquire knowledge in the jazz idiom (history, theory, arranging, composition, and so on), and leading them to understand the fusion of cultures and music traditions that made and continue to make jazz a reflection of the diversity in America (p. 24).

226 This integrated view of learning about jazz places pressure on the educator and leads to the

question: how does one create a healthy balance of teaching all these skills in the classroom?

Given the results from the present study, institutionalized jazz education should present well-

accepted frameworks of the jazz genre and identity, based on the development of musical

vocabulary and repertoire, as well as personal setbacks and paying dues, suggested by well-

crafted biographical narratives. The stories told by jazz performers on how and what they learned

can offer just as much insight into the cognitive representations of jazz music as the focus on

musical transcription, improvisation, and repertoire building. By concentrating too much on

these latter activities, educators are contributing to a canon defined strictly by musical

performance and individual practice. These musical practices might be defined by a set of

rigorous assessment procedures that leave little room for the development of a unique musical

identity. Although Whyton (2006) suggested that the growth of the jazz canon provides a

reliable, objective model to assess learners, he also commented on its putative drawbacks: “…in

buying into the ideology of the canon, educators not only run the risk of relegating jazz to a

fossilised museum piece, they also lose the power of critical insight that is afforded to education

by its unique place in society” (p. 75). One solution, implied by the present study, would

encourage the development of multiple viewpoints in defining, and thus learning about

musicians and their performance practices. Rather than relying solely on analysis of

transcriptions and construction of identity-bound improvisations, performers could be interpreted

in terms of their abstract musical approaches, collaborative activities, supposed styles, the way

they influence others, and the way others influence them. These approaches would expand the

ways in which developing musicians attempt to hone their craft. Several researchers have already

commented on the importance of alternative, informal learning situations and experience in

227 shaping learners’ musical development (Berliner, 1994; Monson, 1996; Green, 2001). Putting

these alternative methods of interpretation into practice, educators could adopt a new system of

assessment, aside from the traditional criteria of the Western canon, particularly apparent in the

mold of jazz standards and bebop language. The judgment as to an educational method’s success

should come from not only learners’ musical representations, but also the way in which

alternative activities, such as those associative mechanisms explored in this study, embody the

identities of professional musicians.

Conclusion

The results reported in this study paint a complex picture of professional musicians’

cognitive representations for eminent jazz performers, as revealed in experiments primed by

excerpt features and dependent upon both domain- and affiliation-specific knowledge. The

multifaceted nature of associative structures in music is apparent in the complex interweaving of

responses we have seen. The interplay between these factors provides a rich, dynamic landscape

with which to continue research on the meaning of music.

While such associative representations and the structures on which they depend certainly

inform aspects of the experience of music, the connection between listening and performing lies

at the heart of the dynamic flux of musicians’ cognitive activity. In an article questioning the

direction of his music, John Coltrane shared a poignant view of musical meaning from the

performer’s perspective:

228 It’s more than beauty that I feel in music—that I think musicians feel in music. What we know we feel we’d like to convey to the listener. We hope that this can be shared by all. I think, basically, that’s about what it is we’re trying to do. We never talk about just what we were trying to do. If you ask me that question, I might say this today and tomorrow say something entirely different, because there are many things to do in music. But, over-all, I think the main thing a musician would like to do is to give a picture to the listener of the many wonderful things he knows of and senses in the universe. That’s what music is to me—it’s just another way of saying this is a big, beautiful universe we live in, that’s been given to us, and here’s an example of just how magnificent and encompassing it is. That’s what I would like to do too. I think that’s one of the greatest things you can do in life, and we all try to do it in someway. The musician’s is through his music (DeMichael, 1962, p.23).

Performing and interpreting music, then, are very personalized processes; but, as Coltrane

pointed out, they are connected with shared knowledge structures. This study confirms that

relationship. It is, perhaps not as vast as the universe, but surely is dependent upon on

hierarchically-defined cognitive representations, common to listeners and performers.

At this time of transition in the world and academia, we have the opportunity to revise

old theories and methodologies, while at the same time exploring new models of musical

meaning. The search for meaning pervades the life our lives; thus, it is my hope that our

academic disciplines will use their collaborative spirit as a metaphorical loom, weaving together

our seemingly disparate views of musical meaning.

229 TABLES

Table 3.4: Focus Group Discussion

230 Topic Theme Evidence Grp

Early Experiences

Family Influence

"I’d always listen to music like my dad always had like classic rock on all the time and stuff I knew about it but (.) didn’t really fe:el like in tune with it as much, until later" 1

Early Experiences

Family Influence

"I remember like being (.) I think fi:ve years old? My mom had a home day care?, ((clears throat)) so it was always full of little kids ((clears throat)) and one of the things that we always did was (.) put o:n records (.) and dance (.) it was like (.) a a fun (.) play type thing to do."

1

Early Experiences

Family Influence

"Before like cello lessons, you know like there was a cello kickin’ around the house and this guy musician friend of my parents taught me howta play [do do do do – sings Batman theme song]" 1

Early Experiences

Family Influence

"I used to only listen to music—like when I was really young—when my parents were in the car and they would just let the radio play. They didn’t really listen to music, they would just put a random station on (.) so a lot of like top 40’s stuff"

2

Early Experiences

Family Influence "Yeah I had that same experience like lo:ng car rides with my parents" 2

Early Experiences

Family Influence

"I just remember bein’ in a car with my brother who’s like twelve years older than me, and I musta been in like kindergarten (.) and (.) I just remember one (.) instance like he would always play cla:ssic rock stuff on the radio and I was like SO amazed about how he knew like who everyone was that came on the radio. And so I started kinda getting inta (.) inta that type of rock as opposed to like (.) what was (.) coming out at tha (.) ya know what was (.) current for the time."

2

Early Experiences

Vivid Experiences

"I’d never seen improvisation before (..) So, it was very exciting to see him (..) IF I remember right he [Peter Brotzmann] was bleeding from his no:se (..) and u:hh (.) There was all kinds of crazy stuff going on and uhh I’D Never seen something like that."

1

Early Experiences

Vivid Experiences

"I remember listening to it it was this really really hot day and I was mowing the lawn (.) Isthis (.) completely surreal I was just sweating listening to this youknow and if you know there tim berne is panned like hard left and Zorn’s like hard right...And it was (.) painful (..) bizarre."

1

231

Topic Theme Evidence Grp

Early Experiences

Vivid Experiences

"...it was like I had this record like (.) kinda only so that if somebody knew that then they could put it on and get me kind of upset you know" 1

Early Experiences

Vivid Experiences

"...when I was in the fourth grade in elementary school they brought these kids in from (.) the junior high school who played (.) youknow in the band(.) and they were doin’ this kinda like (.) fake Dixieland thing and they had like little hats and jackets and stuff (.)...they brought us students intathe library of the school nthey came in and played (.) And I remember I was tootally into the tru:mpets, cause they were playin this stuff and there was just kinda really specific (.) crackling sound the trumpets would make when they did a kinda lip slur or something(.) And I just loved it, I just couldn’t get enough I was sitting there and I was just totally entranced with that this sound that the trumpets would make."

1

Early Experiences

Vivid Experiences

"But like (.) one time (.) in the summer I was probably like (.) twelve or thirteen and I had been playing (..) like :all day in th-this camp (.) with like wearing nothing but shorts and I got sunburned like b:ad from head to toe."

1

Early Experiences

Vivid Experiences

"I couldn’t imagine how they could make—they could sound like that you know how anybody could make (.) you know play an instrument—to me it just seemed like magical" 2

Early Experiences

Vivid Experiences "I had seen Victor Wooten live like when I was in (.) middle school. Ah--was a big one" 2

Early Experiences

Vivid Experiences

"and hearing like really ba:d uh (.) Neil Sedaca and who’s the other Neil (..) Diamond?...YEAH [Singing]: On the Shiloh (.) I was young ((laughs)) (..) I used to call your name (..) So I can still recall some lyrics from these like bad pop tunes?"

2

Early Experiences Theme Songs "I remember the first time I separated watching a tv show from the music was UltraMan. I was really

into Ultraman and I remember I just the music it was so crazy and I was always like fascinated by it." 1

Early Experiences Theme Songs "Yea the Batman theme song was big for me. And the first thing I think I learned how to play on the

cello." 1

232

Topic Theme Evidence Grp

Early Experiences Theme Songs "I remember I liked—there was this terrible show called Medical Center...and I really liked the theme

song to that" 1

Early Experiences Theme Songs "I started watching um (...) ahh (.) Soultrane...and hearin like James Brown band and seeing the Jackson

Five—that like hit me just at a time when I was (.) I don’t know if I was twelve years old?" 2

Early Experiences Theme Songs "I mean I definitely don’t have that many early memories except for music on Fraggle Rock and Sesame

Street" 2

Early Experiences Active Pursuit "I can remember like dis-disti:nctly (..) like wanting tahear ss-specific music put it on listen to it and like

get down to it (.) really get into it (.) you know" 1

Early Experiences Active Pursuit

"I was-got really really excited about (.) the fact that I was saving up for bass (.) and was gonna play bass and I started like totally getting into bass and then (.) you know...I’d always listen to like Led Zepplen and you know like Dazed and Confused to learn about the bass."

1

Early Experiences Active Pursuit "I remembered singing some pop music, there was a pop song in Sweden when I was like five called Hej

Clown (..) that I used to sing...I used to play that record again and again I wore it out" 1

Early Experiences Active Pursuit "And I really liked the theme song to that and I had this little (.) portable (.) Panasonic cassette (.)

recorder and I would—when it came on I would like record it off the tv and then listen to it" 1

Early Experiences Active Pursuit "I remember like trying to learn like some Tone Loc beats on the drums when I was really young (.) stuff

like that just cause I thought it was cool you know." 1

Early Experiences Active Pursuit "I think it was like (.) I remember that and I think I just started kind of…pick th-the ones that that I like

and just try to record them and listen to them again" 2

Early Experiences Active Pursuit "I was just totally fascinated by it and I wanted-start out playing guitar." 2

233

Topic Theme Evidence Grp

Early Experiences Active Pursuit

"or what but then I wanted to buy records (.) and I wanted all the Jackson Five all the forty-fives like “ABC” and all those (.) those hits of that era you know like the early 70’s (indistinguishable)....And I think I got –I got the bug for saxophone from them"

2

Listening Routine

Routine Activities "I always tryta put on like if I’m doin some other sortof routine physical activit:y" 1

Listening Routine

Routine Activities

"I-I try to make space for it sometimes and it often seems to happen late at night like after I’ve gotten everything else out of the way" 1

Listening Routine

Routine Activities

"sometimes what often happens is if I’ve got like musicians from outta town staying at my place (.) which happens sometimes alotta times we’ll like stay up (.) and listen to music pretty late" 1

Listening Routine

Routine Activities

"or a long time when I had a car I had like one tape in the car I would listen to the one tape over and over and over" 1

Listening Routine

Routine Activities

"I listen to music all day at work but sometimes I can pay attention to it really well cause I’m just sitting down at my desk" 1

Listening Routine

Routine Activities "I do A LOTta listening while driving (.) probably the most (.) I listen to music is i-in-in the CAr" 1

Listening Routine

Routine Activities "I come home and make it a point to listen to like a record all the way through or a couple records" 1

Listening Routine

Alone vs. With Others "It happens more often I think with other people (.) for me" 1

Listening Routine

Alone vs. With Others

"Like just sitting and not doing anything but listening and it’s usually (.) because either I or someone else I’m with would wanto (.) share something (.) like you have to hear this youhavetohearthat And that’s when I end up (.) listening the Most"

1

Listening Routine

Alone vs. With Others "Um (.) and usually NOT with other people (.) I usually just sit there (.) n just do it." 1

234

Topic Theme Evidence Grp

Listening Routine

Alone vs. With Others

"And I was much more interested anyway in just...being in a communit:y... listening to music with other people rather than (.) by myself anyway " 1

Listening Routine

Repeated Listening "I would listen to the one tape over and over and over" 1

Listening Routine

Repeated Listening

"every-every single night I listen to the same thing as I go to sleep like the last thing I always listen to is always the same thing (.) so I listen to that" 1

Listening Routine

Repeated Listening "You can really getintasome music in the car it just keeps playin’ over and over" 1

Listening Routine

Repeated Listening

"Um (.) so I dunno sometimes I’m listening to like all these new things that I’ve-that I’ve gotten (.) and then other times I’m stuck on like one John Cale record " 1

Listening Routine

Repeated Listening

"And I was just like Wow this is the greatest thing on earth and you know—I was like anything Beatles – I was just like listening to it over and over" 2

Listening Routine

Repeated Listening "Pick th-the ones that that I like and just try to record them and listen to them again." 2

Listening Routine

Repeated Listening "when I first: um (.) I was listening to this a lot when I first started to playing (.) I mean improvising" 2

Listening Focus Preference

"One thing I listen for is (.) if I wanna keep listening to it...Like as I start to listen to something new it’s like (.) just making the decision on just like turning it off (.) or like (.) switching to something else based on preference"

1

Listening Focus Preference

"It’s just the whole thing of you know probabl:y yeah all music (.) can be (.) is capable of being loved if you (.) listen to it because you like listening to it (.) that’s a sortofah feedback (.) loop of (.) I like this (.) so I-I’m listening to it and then I like it."

1

Listening Focus Preference "that was the one that was like….”I don’t think I like this.” 2

235

Topic Theme Evidence Grp

Listening Focus

Knowledge Building

"there’s there’s a way to relate to all this music, I mean, none of it’s so unfamiliar that I have to look a it as being “what’s goin’ on here”, you know, so right there, you know, I suppose how—that accounts for how—part of how I listen to it"

2

Listening Focus

Distinct Dimensions

"And being cool with it being cool with like saying (.) (different voice) yea I-I actually do like Postal Service there’s somewhere in me that like:s the poppy electronic stuff." 1

Listening Focus

Distinct Dimensions

"I’m always sort of open to (.) to catch some of that from anything...just sort of constantly (..) you know gathering" 1

Listening Focus

Distinct Dimensions

"like I tend to listen to a lot musicians to hear the musicians (.) in you know not just to hear the music you know?" 1

Listening Focus

Distinct Dimensions

"I guess I’m listening to solos and a lot of times rhythm section if it jumps out…like if a drummer pops out or somethin'." 2

Listening Focus

Distinct Dimensions "So then I’m just listening for (.) kind of individual players what they’re doing." 2

Listening Focus

Distinct Dimensions "I also listen to (.) like (.) the s:ty:le of like the solos." 2

Listening Focus

Distinct Dimensions "I’m affected a lot by the textural tonal sound of how the group works together." 2

Listening Focus Mystery

"I don’t really wanna know what I like about music...Cause I feel like if I try to like identify it...And say like that I’m looking for this? (.) then I get scared that...I’m gonna like make these (.) judgments on this music and stuff that I normally just naturally...would be drawn to (.) are somehow like (.) tainted with these thoughts of like I'm looking f:or (.) a good sonic experience"

1

Listening Focus Mystery "I’m totally with you on that try not to decide in advance what it is you’re looking for in music" 1

236

Topic Theme Evidence Grp

Listening Focus Mystery

"I (.) used to t-I used to try to figure out what it was I liked (.) and I remember like (.) in high school (..) I was working you know (.) a lot (.) and (.) I remember like s-blowing (.) my entire paycheck (.) every week on mostly records thinking that like (.) eventually I'd have all the good records...(.) a-and I realized I couldn't (huuuh)"

1

Listening Focus Mystery

"Like there’s music that like you know when I was a kid my parents took me to go see Willie Nelson you know (.) and I (.) n:ow I listen to some of those type of songs or like Santana or something (.) and I really genuinely like the music (.) but I’m not quite sure why you know"

1

Listening Focus

Emotional Response

"I guess wh-whatever if I’m ever listening to anything that—(undeterminable) when I end up looking for something that (..) excites me" 1

Listening Focus

Emotional Response

"that’s what I end up (.) listening to (.) if it...doesn’t do-doesn’t bring me some sort of I guess it’s just like some sort of emotional or some sort of feeling" 1

Listening Focus

Emotional Response "Whether I'm in the mood for it" 2

Listening Focus

Emotional Response "It’s just like an emotional reaction, you know...how you feel (.) literally, about it." 2

237 Table 3.5: Focus Group Description of Excerpts

Grp P E1 - Monk E2 - Brotzmann

1 1 "I absolutely love this older, smart jazz writing…Got me into playing music"

"Textural music, strings, never listen to this type of thing at home but love it in live stituations"

1 2"Tmonk playing Trinkle Tinkle? It sounds like T M. I don't know how to describe what that sounds like"

"Hard for me to put into a context this kind of music in fragments. Stuttery bowed strings/drums & horns come in later - bass clar/trumpet. Sounds like Cecil Taylor band w/o CT"

1 3 "angular jazzy tunie tune" "Free improvised string chaos into sax shrieks and piano and drums!"

1 4 "Monk Criss Cross - one of my fav Monk tunes. Rhythmic energy, melodic angularity"

"Engaging texture/form. Shifting sound fields/energy soloing. I might get tired of it after a while (crossed out) - Ok after horns enter etc."

1 5 "Jazz. I love swinging bass, vibes, drums, and Monk. Med-tempo bebop"

"Intense. Strings. Cello. Dense with "noise". Extended string techniques. Sound mass! This is something that I could only listen to for a short amt of time - w/ larger section , I like more! Almost like animals dying a violent death."

1 6 "jazz" "Improvised jazz music"

1 7 "(Criss Cross) Monk piano, very interesting melody, rhythm, swinging"

"Excited, aggitated bowing. Improvised cello duo? "Free jazz" Extended technique, expressionist. Dynamic - multiple instruments show up, large ensemble"

2 1 "Monk tune. Criss Cross. Nice sax playing the head. Nice Monkish piano playing"

"Crazy strings. I'd rather hear classical music from these instruments. Crazy sawing (arco). It doesn't swing. A bit chaotic. Crazy bass clarinet"

2 2

"Monk quintet swing. Sax, vibes, piano, bass + drums. Good swing feel. Monk's quirky melodic sense. Good group sound, varied entrances by the instruments"

"A bit humorous. Can't quite be certain of the instrumentation. I think more than one stringed instrument w/ bow and other techniques. Makes me laugh + a good thing. Sax + drums. Piano"

2 3

"Angular piano, sounds like (Monk), not bebop but hinting at it, creative not lick oriented good rhythm section groove, funky earthy sax sound, interesting"

"Listen to a set though (?) free, atonal, erratic playing, creative but has a lot of unresolved tension, sounds like should be music in an art movie hinting at something ominous, mysterious about to happen"

2 4

"Quirky, a bit weird sounding (I.e. dissonant), but still in the straight-ahead jazz vain (w/ a walking bassline and swing beat). Very distinctly Monk."

"Has some interesting sounds and textures and different ways of bowing stringed instruments. Sounds like cello and/or bass. Wind and percussion instruments then enter and make similar frenzied noises"

2 5 "Monk small group. Typical Monk melody, strong rhythmic syncopation and articulation"

"Sonically dense, no pulse, no time only intensity levels, better live-looses its impact. Increase intensity through layering instruments"

238

Grp P E3 - Mingus E4 - Golombisky

1 1"Contemplative piano music always makes me wonder what kind of lives these guys live. These inward, harmonically intense passages"

"always a breath of fresh air to listen to thoughtful orchestral music. Doesn't sound played very well, but really nice writing"

1 2

"Solo piano - starts off rubato, very jazz style voicings but in a kind of pastoral application then some kind of left hand ostinato. I don't know if I like this or not, probably depends on my mood"

"Strings. Slow kind of Samuel Barber sound. Then horns come in and the strings go away. Like a wave. The mood stays the same. Horns/strings/in waves."

1 3 "Lush piano/whole tone tonality dominant" "Warm tonal strings then horns expand color palette"

1 4 "Liked it better after groove was established. Intro a bit pretty for me"

"String - winds transition interesting. Not much harmonic/melodic interest for me"

1 5

"Solo piano. Dramatic. Indian tinged w/ extended harmonic chordal moments. Love the mixture even though it feels contrived of moments. Like it. Wonder if it's introduction to some killin' groove. Love bass Mingus playing piano."

"Mine. Chamber orchestra. Strings. Dramatic. "Pretty" and "sad". Deceptive. Funny feeling to listen to your own work around great musicians!!! Nervous. But welcome it."

1 6 "jazz piano" "Classical"

1 7"Exciting harmonic spaces. Improvised piano. Quasi-classical quasi-jazz. Sorrowful, dark, expansive."

"Patient, emotional. Lyrical. Classical layered. Moving arrangement. Use of various sounds within the symphonic sound scape"

239

Grp P E5 - Biosphere E6 - Velvet Underground

1 1"Peaceful peaceful peaceful peaceful peaceful peaceful peaceful peaceful" -- into swirly drawing.

"This older heady rock stuff is great, but you have to be in the right "fuck all" kind of mindframe…which I rarely am"

1 2

"Here I am forming opinions again quickly. I know who it is & I know who chose it, so I feel like I have my head around the context. Droney and repetitive."

"Velvet Underground. How can you possibly describe this without experiencing it? Loose adjectives.. Thumpy drums, frantic maybe the best guitar solo ever"

1 3 "Thick dense sustained repetitive bass drone" "It's my pick!"

1 4 "I like rumbling texture. Otherwise too static for me"

"I heard Him Call my name! A pinnacle of rock music! The right balance of chaos and pulse; rawness of texture"

1 5

"Dense. Water. Acoustic mixed with possible post-produced manipulations. Slow moving, but very moving. "Beautiful" something for relaxation. Something to collect one's thoughts with. Repetitive but totally enthralling. I need this!"

"Yay rock. Voice now. Driving elec. Bass. Neat backing vocals. Messy distorted guitar solo, maybe if he just learned how to play? Haha it's fun though. Floor tom drums. Seemingly random dropping out of bass. Funny."

1 6 "Minimal" "Rock"

1 7"Big low end soundscape. Synth's, warm strings. Epic. Repetitive building vamp with added layers. Dramatic"

"Lou Reed? V. U.? Early punk. Driving beat/guitar. With bluesy singing. Soulful. Awesome rock guitar playing, verging on the abstract/noise plane. Great bass!"

240

Grp P E7 - Latin Play Boys E8 - Luc Ferrari

1 1"One of the best records I've ever owned. Is this pop music? Just great songwriting, form, sonics, etc. Reminds me of my family."

"This is field-recording-type-stuff…hard to tell what goes into it but it doesn't matter…magical in some ways. As long as you drop any expectations."

1 2

"Yes well I brought it. But how can this be put to words without sucking away the truth of it? Maybe focusing on the lyrics would work I guess. I think they're talking about an apparition and the 10 believers that are there"

"The sound of a a semi truck starting and driving away. Then other sounds…street sounds with a shaker going."

1 3 "Mellow rhythmic jam. Reggae-esque!" "Field recording of truck + ? + people + train?"

1 4 "Rhythm track/groove ok; don't like the vocals"

"I understand the idea of listening to a field recording for its purely sonic aspect but don’t hear anything interesting in this particular example"

1 5

"Rhythmic. Sounds like real percussion affected. Big bass sound!!! Crazy mix and quality of sounds, especially for more "normal" vocals. Actually very interesting difference!! Props to taking this leap. I'm more interested in the sounds of the perc. sound

"White noise. Tractor starts. Some field recording? Great quality!! Construction site. Making point of "everything is music?" Kids. These recordings are neat but maybe wouldn't buy them or listen unless I was looking for some sort of sample or something"

1 6 "Pop." "Musique concrete"

1 7"Cool sequenced beat. Mixed with peppy tendencies. Everything has a strange reverby vibe excerpt the vocals"

"Starts w/ the sound of someone entering a truck/vehicle and starting it up, driving off. Then, crickets? People talking/shouting. Street sounds. Nat. occuring field recording"

241

Grp P E9 - Lightnin' Hopkins

1 1"Hell yes! There are no singers like this around anymore. Is there anything more important than these classic blues recordings?"

1 2

"Perfect example of something that is INDESCRIBABLE. Categorizing it can be helpful after the fact, but reduces it. Even though there may be music that has similar form, inflection, etc. there is nothing that sounds like this and its impossible to put int

1 3 "Rural folk -- blues"

1 4 "Guitar/vocal performance…both feeling of direct expression"

1 5

"Blues. No woman. Love complexity hidden in seemingly simplicity. Phrasing and guitar accompaniment is wonderful. Striped down. Doesn't need a full band to totally feel the groove and the intensity"

1 6 "Blues"

1 7 "The real blues. Truth, soul, song about the human experience. The original blues guitar"

242

Grp P E10 - Wes Montgomery E11 - Cedar Walton Trio

2 1 "Nice swinging groove. Nice guitar. Good quartet. Unit 7. I like the drummer"

"I didn't know what time it was" "Nice piano trio. Great arrangement. Rhythms at the start of the A sections. Great kick into 2nd chorus."

2 2"Piano bass drums guitar. Swing. Not a contemporary recording, I think. Wes Montgomery?"

"Piano trio. I didn't know what time it was. Swing. Not sure who the pianist is"

2 3

"My favorite. My recording. Swinging, great rhythm section melodic guitar playing. Thematic. Bebop and some more modern intervalic movement. More diatonic though. Very rhythmic playing. Creative repetitive only in thematic way"

"Very good stride playing. Kind of reminds me of Ahmad Jamal trio playing with the hits. Definitely arranged, but in a hip way that builds energy and allows for creativity"

2 4"Swing beat, walking bass, guitar played with thumb, some interaction between soloist and rhythm section"

"Piano trio, solo intro features stride piano style. Melody features lots of hits and arranged sections"

2 5"Hard swing. Drums poorly recorded. Guitar solo - good melodic and rhythmic development. Piano, bass, guitar, drums"

"Trio well rehersed. Strong leadership from the piano player. Reacting well to one and other. Responsive drum and bass."

243

Grp P E12 - Miles Davis E13 - Bill Frissell

2 1 "Rhythm changes. Muted trumpet - Miles & Sonny Rollins. Love the strong bass"

"Very interesting how it can sound African, or Balkan, or polka. Interesting guitar. It sounds like folk music, but with a more abstract modern touch, a bit disjointed. I like how the beat keeps changing"

2 2

"What I like about this (I brought this one in) is the melodic sensibility all of the musicians bring to this work. Plus I love Miles Davis - really beautiful phrasing and sound on the instrument"

"Guitar, country feel + outside sound contrast, bass + drums. Another one that makes me smile. References to different country cliches, mocking?"

2 3

"Miles Davis Oleo (sorry I've listened to a lot!!) Prominent strong P.C. bass lines. Bridge going for a non trad theme that plays w/ harmony (chromatic decending movment) Sparse, Red Garland/Horace Silver? Comping hip though. Sonny Rollins hip sax rhythm."

"Eclectic melodic, + good use of sounds. Not trad. Jazz feel. Group playing/ensemble playing. Great guitar sound (Bill Frisell) eclectic (klezmer, polka feel)"

2 4

"Very tight ensemble sound, piano used very sparingly creates an interesting texture. Straight-ahead swing drums and walking bass roles"

"Great musical humor, very playful. Highly interactive. I love Bill Frisell. He has great phrasing, taste, and a very unique sound and vocabulary. I love Bill Frisell. Fractured drums still create a deep groove. Electric bass in a responsive, fractured st

2 5

"I like the sound of trumpet and sax on melody. The recording sounds like each instrument was isolated not organic less interaction from rhythm section"

"Guitar - effects. Bass guitar drums. Very broken feel - in time. Tango like. Lots of interaction or lots of written music"

244

Grp P E14 - Thelonious Monk

2 1

"I love Monk's music. Charlie Rouse. I think plays his stuff the best. He really digs into this tune. He can be so swinging, but yet real off. Some amazing 16th note lines. I love Frankie Dunlop's drumming too"

2 2

"Monk again. Quartet. Swing. Charlie Rouse long time associate of Monk. Sounds great playing Monk's tunes. He has the right sensibility for the music. Great swing feel in the rhythm section. More smiles. Rhythm sect. does a good job of supporting the sax

2 3

"Monk awesome. Great rhythm section feel. Great sax sound slightly out of tune. Not just a lick playing solo, but firmly rooted in bop. Thematic. Piano comping hip/sparse/rhythmic"

2 4"Simple, repetitive melody but not boring. Great swing feel fromo the drums - very propulsive. Piano comping creates a great texture of shifting accents"

2 5 "Monk Bemsha Swing. Hard swinging jazz combo"

245 Table 3.9: Pilot and Eminent Performer Study Descriptors

Pilot Study Descriptions Coded Descriptor

Subtle articulation; Note attack Articulation

Blues inflection; Down home blues Blues Influence

Transcendent communicator; Conversational; Accompaniment

Communication and Orchestration

Carefully balanced orchestration; Dense orchestration; Compositional vision

Composition and Orchestration

Shape of musical line; Intervallic contour Contour

Outside the harmonic backdrop; Aural openness outside of a perscribed harmony; Dissonant horns

Dissonance

Pathos; Humorous; Soulful; Struggle; Despair; Optimistic sorrow; Authoritative; Emotional investment in musical direction

Emotion and Expression

Political message; Leadership; Irreverence; Spiritual commonality; Lived relationship to music

Extramusical Association

Grooving; Funky; Swinging; Laid back; Tight without being too slick; Foundation; Lifting quality of quarter note

Groove

Use of pedal point; tonal; Purposeful harmonic mutilation; Harmonic class and sophistication; Single tonal center

Harmony and Tonality

Thinking beyond today; Spontaneous; Creative Improvisational Creativity

Lyrical; Dark dyrics; Masterful prose and concision Lyricism

Triadic melody; Melodic; Melodically quirky Melodicism

Space as musical statement; Rhythmically varied phrasing Phrasing

Repetitive; The same pattern over and over Repetition

Rhythmic variety; Rhythmic maturity; Fluid rhythmic support and interaction

Rhythm

Risk-taking; Stepping out of boundaries; Creative risking Risk-taking

Highly structured; Symmetrical harmony and form Structure

Texturally interesting; Density of melodic line Texture

Dark tone; Sonic pallete; Vocal sound quality; Singing tone; Colors in brass; Unique timbre; Raw; Gritty sound quality; Beautiful vocal sound

Timbre

Tempo change; Relaxed time; Sole time keeper; Ability to stretch time

Time

Effortless virtuosity; Virtuosic Virtuosity

Subtle counterpoint; Voice-leading apparent Voice Leading

246 Table 4.3: Geodesic Counts Between Participants

P_ID AK AU AB AH AS BS BP BT CB CG DB DC DD DT DH DM FLM GB GW JD1AK 1AU 3 1AB 16 1 1AH 12 1 5 1AS 3 3 1 6 1BS 2 41 9 4 1 1BP 1 4 3 6 1 1 1BT 2 18 1 1 3 1 2 1CB 2 3 4 4 2 1 1 2 1CG 3 2 1 14 2 1 3 1 1 1DB 3 7 4 6 4 18 3 6 2 1 1DC 1 3 4 11 1 29 2 1 1 14 2 1DD 1 1 1 8 5 1 1 1 6 2 1 5 1DT 1 1 5 2 1 4 3 1 5 10 1 1 5 1DH 1 4 2 3 5 1 1 1 5 1 1 4 1 9 1DM 3 2 4 3 4 4 1 1 5 2 1 1 1 1 2 1FLM 1 1 10 1 8 3 7 1 3 19 10 20 13 3 1 4 1GB 12 2 1 10 1 1 2 1 6 7 3 5 1 3 1 1 1 1GW 22 1 8 2 2 2 3 1 3 1 5 1 1 5 5 1 1 1 1JD1 3 10 3 1 1 1 1 9 1 13 5 10 2 1 8 1 1 3 6 1JS1 2 6 1 1 3 4 3 1 4 1 5 2 1 1 4 1 2 2 5 1JS2 1 1 13 1 8 4 7 1 4 21 8 22 18 2 1 4 1 1 4 1JG1 3 1 2 1 2 2 3 9 2 13 1 11 4 8 1 1 3 1 1 1JK 12 1 4 12 6 4 5 23 4 12 8 9 6 3 3 3 1 9 20 1JH 2 3 4 13 1 1 3 26 7 2 7 7 1 1 1 1 1 4 1 8JG2 1 2 3 1 1 13 2 6 21 1 3 8 2 1 4 8 2 1 1 3JD2 2 10 1 10 3 1 1 6 3 2 1 2 13 1 12 10 13 5 1 1JB 1 1 14 1 13 1 12 2 1 1 19 25 19 4 1 6 1 1 3 3JM 2 4 1 3 1 1 1 1 6 4 21 5 1 3 2 4 3 1 1 1JS3 1 1 1 5 10 1 10 3 1 4 1 7 18 2 4 4 4 18 2 4JW 1 1 2 1 1 1 4 12 3 1 7 3 1 5 2 11 1 3 12 4KK 1 3 1 4 1 1 2 3 1 1 4 3 1 3 5 3 6 10 5 4KJ 1 1 6 1 7 4 8 1 4 1 11 13 10 2 3 4 1 13 2 1KB 2 2 3 25 9 2 4 2 1 3 1 1 5 1 1 1 1 1 2 1LB 2 9 3 14 3 1 2 1 1 1 6 9 3 1 1 1 17 1 1 8MS1 1 4 21 5 1 8 1 2 1 2 2 1 1 1 3 1 7 1 1 2MR 2 13 14 1 2 1 1 3 1 1 1 1 14 1 3 6 1 18 40 1MG 1 7 1 2 1 4 1 1 4 10 3 3 3 10 11 1 2 5 1 1MK 1 1 7 4 11 11 1 4 2 1 2 2 5 2 2 1 5 1 3 3MA 1 2 2 6 2 9 1 3 22 1 1 13 1 3 1 1 9 1 1 2MS2 2 8 3 11 2 1 3 8 1 1 5 9 3 5 1 1 14 1 1 1NH 3 2 1 14 1 2 3 1 2 2 2 16 7 12 24 2 19 6 1 16PM 3 1 1 11 2 1 1 1 1 1 1 8 5 8 17 1 15 5 1 11QK 3 11 41 3 3 1 3 11 1 10 5 3 2 1 10 1 2 3 1 1RK 19 4 1 14 3 3 3 7 3 1 4 1 2 6 2 1 1 1 4 1RM 3 2 12 31 5 2 7 23 2 5 15 38 10 18 2 2 40 2 2 4RS 2 4 2 1 2 10 3 1 2 9 3 2 1 4 3 3 1 1 4 5SM 1 1 3 10 1 3 2 4 3 1 5 10 3 6 7 1 13 2 5 1TF 1 3 2 1 1 3 1 1 3 1 4 2 33 6 1 7 1 1 4 1TD 2 1 4 1 2 16 1 4 21 4 3 5 4 1 14 1 1 3 4 4TS 1 1 12 10 16 13 1 2 14 3 2 1 2 4 6 1 1 2 3 3

247

P_ID JS1 JS2 JG1 JK JH JG2 JD2 JB JM JS3 JW KK KJ KB LB MS1 MR MG MK MAAKAUABAHASBSBPBTCBCGDBDCDDDTDHDMFLMGBGWJD1JS1 1JS2 1 1JG1 7 2 1JK 1 1 2 1JH 4 1 8 12 1JG2 3 3 5 1 14 1JD2 8 14 1 10 1 4 1JB 1 1 4 1 1 3 30 1JM 2 3 4 3 4 1 3 9 1JS3 2 5 3 5 1 2 2 1 1 1JW 51 1 7 1 3 1 3 1 1 4 1KK 1 6 2 4 1 1 2 9 1 16 1 1KJ 2 1 3 1 17 1 1 1 3 5 1 7 1KB 10 1 2 24 2 4 1 1 1 3 1 5 1 1LB 1 18 9 11 10 6 6 1 1 2 3 1 19 1 1MS1 2 6 2 6 1 2 1 10 1 9 27 8 6 2 2 1MR 4 1 3 1 1 2 3 1 2 1 26 11 1 3 2 1 1MG 1 2 1 1 2 3 7 3 2 2 63 1 3 12 1 1 3 1MK 2 5 2 4 1 3 2 10 1 1 3 6 6 10 3 2 13 1 1MA 1 13 4 4 2 1 3 14 10 9 4 2 7 2 3 1 10 3 1 1MS2 6 16 1 9 9 4 1 29 3 3 6 2 17 2 8 1 2 1 2 3NH 1 21 14 12 3 1 2 1 5 6 15 8 1 2 1 2 1 11 2 6PM 1 17 11 9 2 1 1 1 4 4 8 4 1 1 1 1 1 9 1 4QK 1 3 1 3 5 3 9 1 4 1 70 2 3 12 11 2 6 1 3 2RK 1 16 7 13 3 2 5 2 2 2 1 1 1 6 7 1 2 1 2 1RM 18 45 3 29 2 13 2 2 3 1 21 6 50 4 20 1 7 1 2 6RS 31 1 4 64 1 8 2 2 2 1 3 1 2 1 2 10 12 2 2 1SM 5 13 1 11 1 2 1 1 1 1 7 2 16 1 3 1 2 5 1 1TF 1 1 7 1 3 2 1 3 2 2 3 2 2 1 1 13 3 8 1 20TD 4 1 1 1 5 1 3 1 1 1 1 4 1 11 3 2 13 5 2 2TS 3 1 1 9 1 1 2 1 13 13 1 13 1 4 2 1 1 5 1 1

248

P_ID MS2 NH PM QK RK RM RS SM TF TD TSAKAUABAHASBSBPBTCBCGDBDCDDDTDHDMFLMGBGWJD1JS1JS2JG1JKJHJG2JD2JBJMJS3JWKKKJKBLBMS1MRMGMKMAMS2 1NH 1 1PM 1 1 1QK 9 12 9 1RK 6 1 1 1 1RM 1 1 5 2 18 1RS 3 7 3 1 24 1 1SM 8 1 1 1 5 1 1 1TF 5 1 1 1 9 17 2 5 1TD 3 4 3 7 2 8 26 3 2 1TS 2 3 2 5 2 8 11 3 3 1 1

249 Table 4.4: Geodesic Distances Between Participants

P_ID AK AU AB AH AS BS BP BT CB CG DB DC DD DT DH DM FLM GB GW JD1AK 0AU 3 0AB 4 2 0AH 3 2 3 0AS 2 3 2 3 0BS 2 4 3 3 2 0BP 2 3 3 3 1 2 0BT 3 3 1 2 3 2 3 0CB 4 6 5 5 4 2 4 4 0CG 3 3 3 4 3 3 3 3 5 0DB 2 3 3 3 2 3 2 3 5 2 0DC 3 2 2 3 2 3 3 2 5 3 2 0DD 2 2 2 4 3 3 2 3 5 3 2 3 0DT 1 2 3 2 1 2 2 2 4 3 1 2 3 0DH 3 2 2 3 3 2 2 3 4 3 2 2 2 3 0DM 2 2 3 2 2 2 1 2 4 2 1 2 2 1 2 0FLM 2 2 4 1 3 3 3 3 5 4 3 3 4 2 2 2 0GB 3 2 2 3 2 2 2 2 4 3 2 1 2 2 1 1 2 0GW 3 2 3 2 2 2 2 2 4 2 2 2 2 2 2 1 2 1 0JD1 2 3 3 2 1 1 1 3 3 3 2 3 3 1 3 1 2 2 2 0JS1 2 3 3 2 2 2 2 2 4 2 2 3 3 1 3 1 2 2 2 1JS2 2 2 4 1 3 3 3 3 5 4 3 3 4 2 2 2 1 2 2 2JG1 2 2 3 2 2 2 2 3 4 3 1 2 3 2 2 1 2 1 1 1JK 3 2 4 1 3 3 3 3 5 4 3 3 4 2 3 2 1 3 3 2JH 3 2 1 4 3 3 3 2 5 3 3 2 2 2 1 2 3 2 2 3JG2 2 2 2 2 1 3 2 3 5 2 2 1 2 1 2 2 2 1 1 2JD2 2 3 2 3 2 2 2 3 4 2 1 2 2 1 3 2 3 2 1 1JB 2 2 4 1 3 2 3 3 4 3 3 3 4 2 2 2 1 2 2 2JM 2 2 2 3 2 2 2 2 4 3 3 2 3 2 2 2 3 2 2 1JS3 2 2 3 3 3 2 3 3 4 3 2 3 4 2 3 2 3 3 2 2JW 3 1 2 3 3 3 3 3 5 2 3 2 1 3 2 3 3 2 3 3KK 3 2 2 4 3 2 3 3 4 3 3 3 1 3 3 3 4 3 3 3KJ 2 2 4 1 3 3 3 3 5 3 3 3 4 2 3 2 1 3 2 2KB 3 2 2 4 3 3 3 3 5 3 2 2 1 2 2 2 3 1 2 2LB 2 3 2 3 2 1 2 1 3 2 2 2 3 1 2 1 3 1 1 2MS1 2 3 3 3 2 3 2 3 5 3 2 2 3 1 3 1 3 2 2 2MR 2 3 4 1 2 2 2 3 4 3 2 3 4 1 3 2 1 3 3 1MG 2 3 3 2 1 2 1 2 4 3 2 3 3 2 3 1 2 2 1 1MK 2 1 2 3 3 3 2 3 5 2 2 2 3 2 2 1 3 2 2 2MA 2 2 2 3 2 3 1 3 5 2 1 2 1 2 1 1 3 1 1 2MS2 2 3 3 3 2 2 2 3 4 2 2 2 3 2 2 1 3 1 1 1NH 3 3 3 4 2 2 3 3 4 2 2 3 4 3 4 2 4 3 2 3PM 2 2 2 3 2 2 2 2 4 1 1 2 3 2 3 1 3 2 1 2QK 2 3 4 2 2 1 2 3 3 3 2 3 3 1 3 1 2 2 1 1RK 3 3 3 3 2 2 2 3 4 2 2 3 3 2 3 1 2 2 2 1RM 2 3 4 4 3 2 3 4 4 3 3 3 4 3 3 2 4 2 2 2RS 3 3 1 4 2 4 3 2 6 4 3 2 1 3 3 3 4 2 3 3SM 1 2 3 3 2 2 2 3 4 2 2 3 3 2 3 1 3 2 2 1TF 2 3 3 2 2 2 2 2 4 2 2 3 3 2 3 2 2 2 2 1TD 2 1 3 1 2 3 2 3 5 3 2 2 3 1 3 1 1 2 2 2TS 1 2 4 2 3 3 2 3 5 3 2 3 3 2 3 1 1 2 2 2

250

P_ID JS1 JS2 JG1 JK JH JG2 JD2 JB JM JS3 JW KK KJ KB LB MS1 MR MG MK MAAKAUABAHASBSBPBTCBCGDBDCDDDTDHDMFLMGBGWJD1JS1 0JS2 2 0JG1 2 2 0JK 2 1 2 0JH 3 3 3 4 0JG2 2 2 2 2 3 0JD2 2 3 1 3 2 2 0JB 1 1 2 1 3 2 3 0JM 2 3 2 3 2 2 2 3 0JS3 2 3 2 3 2 2 2 2 1 0JW 4 3 3 3 2 2 2 3 2 3 0KK 3 4 3 4 2 2 2 4 3 4 1 0KJ 2 1 2 1 4 2 2 1 3 3 3 4 0KB 3 3 2 4 2 2 1 3 2 3 1 2 3 0LB 1 3 2 3 3 2 2 2 2 2 3 3 3 2 0MS1 2 3 2 3 2 2 2 3 2 3 4 4 3 3 2 0MR 2 1 2 1 3 2 2 1 2 2 4 4 1 3 2 2 0MG 1 2 1 2 3 2 2 2 2 2 4 3 2 3 1 2 2 0MK 2 3 2 3 1 2 2 3 1 1 2 3 3 3 2 2 3 2 0MA 2 3 2 3 2 1 2 3 3 3 2 2 3 2 2 2 3 2 2 0MS2 2 3 1 3 3 2 1 3 2 2 3 3 3 2 2 2 2 1 2 2NH 2 4 3 4 3 2 2 3 3 3 4 4 3 3 2 3 3 3 2 3PM 1 3 2 3 2 1 1 2 2 2 3 3 2 2 1 2 2 2 1 2QK 1 2 1 2 3 2 2 1 2 1 4 3 2 3 2 2 2 1 2 2RK 1 3 2 3 3 2 2 2 2 2 3 2 2 3 2 2 2 1 2 2RM 3 4 2 4 3 3 2 3 2 1 4 4 4 3 3 2 3 2 2 3RS 4 4 3 5 2 3 2 4 3 3 2 2 4 1 3 4 4 3 3 2SM 2 3 1 3 2 2 1 2 1 1 3 3 3 2 2 2 2 2 1 2TF 1 2 2 2 3 2 1 2 2 2 3 3 2 2 1 3 2 2 2 3TD 2 1 1 1 3 1 2 1 2 2 2 3 1 3 2 2 2 2 2 2TS 2 1 1 2 3 2 2 1 3 3 3 4 1 3 2 2 1 2 2 2

251

P_ID MS2 NH PM QK RK RM RS SM TF TD TSAKAUABAHASBSBPBTCBCGDBDCDDDTDHDMFLMGBGWJD1JS1JS2JG1JKJHJG2JD2JBJMJS3JWKKKJKBLBMS1MRMGMKMAMS2 0NH 2 0PM 1 1 0QK 2 3 2 0RK 2 2 1 1 0RM 1 2 2 2 3 0RS 3 4 3 3 4 3 0SM 2 2 1 1 2 1 2 0TF 2 2 1 1 2 3 3 2 0TD 2 3 2 2 2 3 4 2 2 0TS 2 3 2 2 2 3 4 2 2 1 0

252 Table 4.5: Degree-Degree Correlations Between Participants

P_ID AK AU AB AH AS BS BP BT CB CG DB DC DD DT DHAK 1.00AU -0.04 1.00AB -0.04 0.01 1.00AH -0.04 -0.02 -0.04 1.00AS 0.11 -0.04 0.00 -0.04 1.00BS 0.03 -0.04 -0.04 -0.04 -0.01 1.00BP 0.01 -0.04 -0.04 -0.04 0.20 -0.02 1.00BT -0.04 -0.04 0.00 -0.01 -0.04 0.02 -0.04 1.00CB -0.04 -0.04 -0.04 -0.04 -0.04 -0.01 -0.04 -0.04 1.00CG -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 1.00DB 0.08 -0.04 -0.04 -0.04 0.19 -0.04 0.05 -0.04 -0.04 0.00 1.00DC -0.04 0.07 0.12 -0.04 0.00 -0.04 -0.04 -0.03 0.09 -0.04 0.12 1.00DD -0.03 0.00 -0.01 -0.04 -0.04 -0.04 -0.02 -0.04 -0.04 -0.04 -0.03 -0.04 1.00DT 0.02 -0.03 -0.05 0.01 0.06 0.12 0.08 0.00 -0.05 -0.05 0.11 -0.01 -0.05 1.00DH -0.04 0.17 0.07 -0.04 -0.04 -0.03 -0.03 -0.04 -0.04 -0.04 -0.04 0.10 -0.03 -0.05 1.00DM 0.04 -0.02 -0.05 0.04 0.06 0.16 0.01 0.00 -0.05 -0.02 0.04 0.00 -0.03 0.17 0.00FLM 0.00 -0.02 -0.04 0.32 -0.04 -0.05 -0.04 -0.04 -0.05 -0.05 -0.04 -0.04 -0.04 0.04 -0.04GB -0.04 0.05 -0.01 -0.04 -0.01 -0.02 0.01 -0.01 -0.04 -0.04 0.03 0.09 0.00 0.02 0.27GW -0.04 -0.02 -0.05 0.06 0.01 0.06 0.01 0.00 -0.05 -0.03 0.14 -0.03 -0.02 0.14 0.06JD1 0.04 -0.04 -0.04 -0.02 0.05 0.06 0.12 -0.04 -0.04 -0.04 0.14 -0.04 -0.04 0.29 -0.04JS1 0.05 -0.04 -0.04 -0.01 0.06 0.13 0.02 0.01 -0.04 -0.01 0.16 -0.04 -0.04 0.29 -0.04JS2 -0.01 -0.02 -0.04 0.54 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 0.01 -0.04JG1 0.06 -0.02 -0.04 -0.01 0.01 0.10 0.05 -0.04 -0.04 -0.04 0.15 -0.04 -0.04 0.31 -0.02JK -0.04 -0.02 -0.04 0.42 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 0.10 -0.04JH -0.03 0.09 0.13 -0.03 -0.03 -0.04 -0.03 -0.03 -0.03 -0.04 -0.03 0.23 -0.03 0.02 0.18JG2 -0.03 0.00 0.09 -0.01 -0.01 -0.04 0.12 -0.04 -0.04 -0.02 0.03 -0.04 0.02 0.12 0.04JD2 0.00 -0.04 0.03 -0.05 0.05 -0.01 -0.02 -0.04 -0.05 0.04 0.15 0.03 -0.05 0.03 -0.05JB -0.01 -0.03 -0.04 0.62 -0.04 -0.03 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 0.01 -0.04JM 0.04 0.16 0.03 -0.04 -0.01 -0.01 -0.02 -0.02 -0.04 -0.04 -0.04 0.13 -0.04 0.07 0.06JS3 0.03 -0.03 -0.04 -0.04 -0.04 -0.01 -0.04 -0.04 -0.04 -0.04 0.04 -0.04 -0.04 0.02 -0.04JW -0.04 0.09 0.06 -0.04 -0.03 -0.04 -0.04 -0.03 -0.04 -0.01 -0.04 0.03 0.34 -0.04 0.02KK -0.04 0.14 0.01 -0.04 -0.04 -0.02 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 0.29 -0.04 -0.04KJ 0.02 -0.02 -0.04 0.45 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 0.02 -0.04KB -0.04 0.10 0.18 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 0.00 0.00 0.17 -0.02 0.00LB 0.01 -0.04 -0.05 -0.04 0.07 -0.05 0.00 -0.04 -0.05 -0.03 0.18 -0.04 -0.05 0.19 -0.02MS1 -0.02 -0.04 -0.04 -0.04 0.00 -0.04 -0.01 -0.04 -0.04 -0.04 0.04 -0.01 -0.04 0.03 -0.04MR 0.04 -0.04 -0.04 0.31 0.04 -0.01 -0.02 -0.04 -0.04 -0.04 -0.01 -0.04 -0.04 0.06 -0.04MG 0.01 -0.04 -0.04 0.00 0.02 0.17 0.06 0.00 -0.04 -0.04 0.09 -0.04 -0.04 0.42 -0.04MK 0.04 0.00 -0.03 -0.03 -0.03 -0.04 -0.02 -0.03 -0.03 -0.03 0.01 0.08 -0.03 0.05 0.07MA -0.03 0.10 0.04 -0.04 0.01 -0.05 -0.03 -0.04 -0.04 -0.01 -0.01 -0.04 0.02 -0.01 0.08MS2 0.02 -0.04 -0.04 -0.04 0.01 0.00 0.03 -0.04 -0.04 -0.01 0.17 -0.04 -0.04 0.15 0.01NH -0.04 -0.04 -0.04 -0.04 -0.02 0.04 -0.04 -0.04 -0.04 0.00 0.11 -0.04 -0.04 -0.04 -0.04PM 0.03 -0.04 -0.02 -0.05 0.01 -0.02 -0.03 -0.01 -0.05 -0.05 0.13 -0.05 -0.05 0.14 -0.05QK 0.05 -0.04 -0.05 0.04 0.06 0.06 0.03 -0.04 -0.05 -0.05 0.15 -0.04 -0.05 0.28 -0.05RK -0.04 -0.04 -0.04 -0.04 0.00 0.08 0.00 -0.03 -0.04 0.00 0.10 -0.04 -0.04 0.17 -0.04RM 0.03 -0.03 -0.04 -0.03 -0.03 0.01 -0.03 -0.03 -0.04 -0.04 -0.04 -0.03 -0.04 -0.04 -0.04RS -0.04 -0.04 0.05 -0.04 0.06 -0.04 -0.04 -0.01 -0.04 -0.04 -0.04 0.04 0.05 -0.05 -0.04SM 0.05 0.01 -0.04 -0.04 0.00 0.09 0.01 -0.04 -0.04 -0.02 0.12 -0.04 -0.04 0.16 -0.04TF 0.01 -0.04 -0.04 -0.02 -0.01 0.08 -0.02 0.02 -0.04 -0.01 0.22 -0.04 -0.04 0.18 -0.04TD 0.01 -0.04 -0.04 0.36 0.05 -0.04 -0.02 -0.04 -0.04 -0.04 0.05 -0.04 -0.04 0.07 -0.04TS -0.04 -0.03 -0.04 0.36 -0.04 -0.04 -0.01 -0.04 -0.04 -0.04 0.03 -0.04 -0.04 0.07 -0.04

253

P_ID DM FLM GB GW JD1 JS1 JS2 JG1 JK JH JG2 JD2 JB JM JS3AKAUABAHASBSBPBTCBCGDBDCDDDTDHDM 1.00FLM 0.09 1.00GB 0.18 -0.01 1.00GW 0.27 -0.04 0.24 1.00JD1 0.33 -0.03 0.07 0.26 1.00JS1 0.22 0.05 0.00 0.09 0.23 1.00JS2 0.08 0.64 -0.02 0.06 -0.02 -0.01 1.00JG1 0.58 0.04 0.18 0.37 0.59 0.24 0.01 1.00JK 0.05 0.54 -0.04 -0.05 0.00 -0.02 0.70 0.01 1.00JH -0.03 -0.04 0.11 -0.04 -0.04 -0.03 -0.04 -0.03 -0.03 1.00JG2 0.24 0.03 0.25 0.21 0.10 0.05 0.03 0.21 0.00 -0.03 1.00JD2 0.28 -0.05 0.08 0.03 0.20 0.25 -0.05 0.24 -0.05 -0.02 0.10 1.00JB 0.08 0.61 -0.01 0.02 0.00 -0.03 0.68 0.04 0.64 -0.04 0.05 -0.05 1.00JM 0.08 -0.05 -0.02 -0.04 0.14 0.05 -0.04 0.16 -0.04 0.23 0.02 0.07 -0.05 1.00JS3 0.05 -0.05 -0.04 0.03 0.17 0.00 -0.04 0.13 -0.04 -0.02 0.04 0.02 -0.04 0.09 1.00JW -0.05 -0.04 0.08 -0.04 -0.04 -0.03 -0.04 -0.04 -0.04 0.08 0.00 0.09 -0.04 -0.01 -0.04KK -0.05 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.04 -0.02 0.00 0.03 -0.04 -0.04 -0.04KJ 0.07 0.60 -0.04 0.05 -0.02 0.02 0.76 0.03 0.60 -0.03 0.00 -0.03 0.71 -0.04 -0.04KB -0.02 -0.05 0.10 0.04 -0.01 -0.04 -0.04 0.01 -0.04 0.01 0.12 0.10 -0.04 -0.03 -0.04LB 0.26 -0.05 0.08 0.15 0.34 0.21 -0.05 0.41 -0.05 -0.04 0.19 0.18 -0.04 0.02 0.02MS1 0.04 -0.04 0.02 0.03 0.04 0.00 -0.04 0.10 -0.04 0.01 0.00 -0.02 -0.04 -0.01 -0.04MR 0.17 0.50 -0.04 -0.05 0.04 0.08 0.65 0.08 0.66 -0.04 0.02 0.06 0.55 0.04 -0.02MG 0.36 0.01 0.13 0.27 0.54 0.47 0.01 0.57 -0.01 -0.04 0.07 0.15 0.02 0.06 0.04MK 0.00 -0.04 -0.02 0.02 0.17 -0.01 -0.04 0.09 -0.03 0.24 0.02 0.02 -0.04 0.51 0.14MA 0.07 -0.05 0.08 0.13 -0.01 -0.03 -0.05 0.11 -0.04 0.00 0.06 0.03 -0.05 -0.05 -0.05MS2 0.26 -0.05 0.09 0.36 0.28 0.21 -0.04 0.48 -0.04 -0.03 0.14 0.18 -0.04 0.10 0.10NH -0.01 -0.04 -0.04 0.00 -0.04 0.03 -0.04 -0.04 -0.04 -0.03 0.01 0.05 -0.04 -0.04 -0.04PM 0.15 -0.06 0.09 0.22 0.32 0.23 -0.05 0.41 -0.05 -0.02 0.13 0.22 -0.04 0.09 0.05QK 0.35 0.01 0.09 0.20 0.38 0.48 0.07 0.52 0.08 -0.04 0.08 0.26 0.00 0.08 0.01RK 0.16 -0.01 -0.02 0.10 0.29 0.49 -0.04 0.23 -0.04 -0.03 0.06 0.17 0.00 0.03 0.02RM 0.02 -0.04 0.03 0.02 0.07 -0.03 -0.04 0.06 -0.04 -0.03 -0.04 0.01 -0.04 0.02 0.12RS -0.05 -0.05 -0.02 -0.05 -0.04 -0.04 -0.04 -0.04 -0.04 -0.02 -0.04 0.04 -0.04 -0.04 -0.04SM 0.21 -0.05 0.00 0.14 0.35 0.14 -0.04 0.28 -0.04 0.03 0.07 0.13 -0.04 0.36 0.12TF 0.20 -0.02 -0.02 0.13 0.27 0.34 -0.02 0.29 -0.01 -0.04 0.08 0.30 0.00 0.08 0.02TD 0.13 0.54 0.03 0.13 0.13 0.05 0.57 0.15 0.38 -0.04 0.02 0.04 0.53 0.00 -0.02TS 0.10 0.27 0.02 0.07 0.08 0.06 0.33 0.14 0.41 -0.04 -0.02 0.01 0.41 -0.04 -0.04

254

P_ID JW KK KJ KB LB MS1 MR MG MK MA MS2 NH PM QK RKAKAUABAHASBSBPBTCBCGDBDCDDDTDHDMFLMGBGWJD1JS1JS2JG1JKJHJG2JD2JBJMJS3JW 1.00KK 0.12 1.00KJ -0.04 -0.04 1.00KB 0.17 0.22 -0.04 1.00LB -0.04 -0.05 -0.04 -0.02 1.00MS1 -0.04 -0.04 -0.04 -0.04 0.05 1.00MR -0.04 -0.04 0.62 -0.04 0.05 -0.02 1.00MG -0.04 -0.04 -0.01 -0.04 0.35 0.06 0.06 1.00MK 0.05 -0.03 -0.03 -0.04 0.01 0.04 -0.04 0.00 1.00MA 0.08 0.05 -0.04 0.06 0.07 -0.01 -0.05 0.02 -0.03 1.00MS2 -0.04 -0.04 -0.04 0.03 0.33 0.02 0.05 0.33 0.01 0.09 1.00NH -0.04 -0.04 -0.04 -0.04 -0.01 -0.04 -0.04 -0.04 0.00 -0.04 0.03 1.00PM -0.05 -0.05 -0.04 -0.02 0.29 -0.02 -0.02 0.25 0.05 0.03 0.36 0.00 1.00QK -0.04 -0.05 0.01 -0.05 0.41 0.04 0.15 0.54 0.02 0.03 0.36 -0.05 0.22 1.00RK -0.03 -0.02 0.00 -0.04 0.24 -0.01 0.01 0.35 0.00 -0.03 0.17 0.04 0.29 0.13 1.00RM -0.03 -0.03 -0.03 -0.04 -0.04 -0.03 -0.04 0.02 0.01 -0.04 0.15 -0.01 0.12 -0.01 -0.03RS 0.11 -0.01 -0.04 0.04 -0.05 -0.04 -0.04 -0.04 -0.03 -0.04 -0.04 -0.04 -0.05 -0.05 -0.04SM -0.04 -0.04 -0.04 -0.02 0.11 0.00 0.07 0.25 0.29 -0.03 0.33 0.00 0.12 0.21 0.14TF -0.04 -0.04 0.01 0.02 0.21 -0.04 0.09 0.26 -0.02 -0.05 0.24 0.02 0.26 0.22 0.36TD -0.03 -0.04 0.47 -0.04 0.12 0.05 0.49 0.19 -0.01 0.00 0.08 -0.04 0.03 0.25 0.02TS -0.04 -0.04 0.39 -0.04 0.08 0.03 0.31 0.13 -0.01 -0.01 0.04 -0.04 0.02 0.09 0.12

255

P_ID RM RS SM TF TD TSAKAUABAHASBSBPBTCBCGDBDCDDDTDHDMFLMGBGWJD1JS1JS2JG1JKJHJG2JD2JBJMJS3JWKKKJKBLBMS1MRMGMKMAMS2NHPMQKRKRM 1.00RS -0.04 1.00SM 0.10 0.04 1.00TF -0.04 -0.04 0.23 1.00TD -0.04 -0.04 0.08 0.05 1.00TS -0.04 -0.04 0.03 0.09 0.30 1.00

256 Table 4.6: Hierarchical-Clustering Iterations

P_N P_ID HC_1 HC_150 HC_200 HC_211 HC_2161 AK 1 1 1 3 12 AU 2 2 2 2 13 AB 3 3 3 4 14 AH 4 4 1 3 15 AS 5 5 4 1 16 BS 6 6 5 1 17 BP 7 7 4 1 18 BT 8 8 5 1 19 CB 9 9 6 4 110 CG 10 10 7 5 111 DB 11 11 5 1 112 DC 12 12 3 4 113 DD 13 13 8 2 114 DT 14 14 5 1 115 DH 15 15 9 2 116 DM 16 16 5 1 117 FLM 17 4 1 3 118 GB 18 17 9 2 119 GW 19 16 5 1 120 JD1 20 16 5 1 121 JS1 21 18 5 1 122 JS2 22 4 1 3 123 JG1 23 16 5 1 124 JK 24 4 1 3 125 JH 25 17 10 2 126 JG2 26 7 4 1 127 JD2 27 18 5 1 128 JB 28 4 1 3 129 JM 29 19 10 2 130 JS3 30 20 11 2 131 JW 31 13 8 2 132 KK 32 21 8 2 133 KJ 33 4 1 3 134 KB 34 22 11 2 135 LB 35 16 5 1 136 MS1 36 23 5 1 137 MR 37 4 1 3 138 MG 38 16 5 1 139 MK 39 19 10 2 140 MA 40 24 10 2 141 MS2 41 25 12 1 142 NH 42 26 5 1 143 PM 43 27 5 1 144 QK 44 16 5 1 145 RK 45 18 5 1 146 RM 46 28 12 1 147 RS 47 29 8 2 148 SM 48 19 10 2 149 TF 49 30 8 2 150 TD 50 4 1 3 151 TS 51 4 1 3 1

257 Table 4.7: Girvan-Newman Partitions

P_N P_ID Part 10 Part 8 Part 5 Part 3 Part 21 AK 1 1 1 1 12 AU 4 4 2 2 13 AB 4 4 2 2 14 AH 3 3 3 3 25 AS 1 1 1 1 16 BS 1 1 1 1 17 BP 1 1 1 1 18 BT 4 4 2 2 19 CB 0 0 0 0 110 CG 9 8 5 1 111 DB 1 1 1 1 112 DC 4 4 2 2 113 DD 2 2 2 2 114 DT 1 1 1 1 115 DH 4 4 2 2 116 DM 1 1 1 1 117 FLM 3 3 3 3 218 GB 4 4 2 2 119 GW 1 1 1 1 120 JD1 1 1 1 1 121 JS1 1 1 1 1 122 JS2 3 3 3 3 223 JG1 1 1 1 1 124 JK 3 3 3 3 225 JH 4 4 2 2 126 JG2 4 4 2 2 127 JD2 1 1 1 1 128 JB 3 3 3 3 229 JM 4 4 2 2 130 JS3 8 7 1 1 131 JW 4 4 2 2 132 KK 2 2 2 2 133 KJ 3 3 3 3 234 KB 2 2 2 2 135 LB 1 1 1 1 136 MS1 10 1 1 1 137 MR 3 3 3 3 238 MG 1 1 1 1 139 MK 5 4 2 2 140 MA 2 2 2 2 141 MS2 1 1 1 1 142 NH 7 6 1 1 143 PM 1 1 1 1 144 QK 1 1 1 1 145 RK 1 1 1 1 146 RM 8 7 1 1 147 RS 6 5 4 2 148 SM 1 1 1 1 149 TF 1 1 1 1 150 TD 3 3 3 3 251 TS 3 3 3 3 2

258 Table 4.8: Density Values for Participants

P_N P_ID Ties Density1 AK 6 1.582 AU 10 2.633 AB 14 2.774 AH 122 29.055 AS 20 5.266 BS 6 1.587 BP 20 5.268 BT 2 0.539 CB 0 0.0010 CG 0 0.0011 DB 30 7.8912 DC 12 2.8613 DD 30 7.1414 DT 82 11.6815 DH 26 6.1916 DM 166 16.7317 FLM 146 26.4518 GB 64 11.5919 GW 86 12.2520 JD1 134 22.3321 JS1 122 32.1122 JS2 168 30.4323 JG1 112 26.6724 JK 140 36.8425 JH 24 6.3226 JG2 34 8.9527 JD2 70 11.6728 JB 168 30.4329 JM 30 7.1430 JS3 24 5.7131 JW 18 4.7432 KK 10 2.6333 KJ 160 38.1034 KB 28 7.3735 LB 72 12.0036 MS1 8 2.1137 MR 130 28.1438 MG 126 19.3839 MK 40 10.5340 MA 24 4.0041 MS2 94 24.7442 NH 2 0.5343 PM 124 14.2544 QK 136 19.3745 RK 64 16.8446 RM 18 4.7447 RS 12 3.1648 SM 70 16.6749 TF 62 14.7650 TD 128 25.3051 TS 82 19.52

259 Figures

Figure 4.2: Professional Jazz Musician Collaborators Network

260 Figure 4.3: Professional Jazz Musician Collaborator Network in Three Main Clusters

261 Figure 4.4: Louis Armstrong Excerpt Associations Network

262 Figure 4.5: Ornette Coleman Excerpt Associations Network

263 Figure 4.6: John Coltrane Excerpt Associations Network

264 Figure 4.7: Miles Davis Excerpt Associations Network

265 Figure 4.8: Duke Ellington Excerpt Associations Network

266 4.9: Herbie Hancock Excerpt Associations Network

267 Figure 4.10: Coleman Hawkins Excerpt Associations Network

268 Figure 4.11: Billie Holiday Excerpt Associations Network

269 Figure 4.12: Charles Mingus Excerpt Associations Network

270 Figure 4.13: Thelonious Monk Excerpt Associations Network

271 Figure 4.14: Wes Montgomery Excerpt Associations Network

272 Figure 4.15: Charlie Parker Excerpt Associations Network

273 Figure 4.16: Jaco Pastorius Excerpt Associations Network

274 Figure 4.17: Max Roach Excerpt Associations Network

275 Figure 4.18: Sonny Rollins Excerpt Associations Network

276 References

Adorno, T. W. (Ed.). (2002). Essays on music Berkeley: University of California Press. Agar, M., & Hobbs, J. R. (1985). How to grow schemata out of interviews. In J. W. D.

Dougherty (Ed.), Directions in cognitive anthropology. Urbana, IL: University of Illinois Press.

Aiello, R. (1994). Music and language: Parallels and contrasts. In R. Aiello (Ed.), Musical

Perceptions (pp. 40-63). New York: Oxford University Press. Ake, D. (2002). Jazz cultures. Berkeley, CA: University of California Press. Alexander, P. A. (2003). The development of expertise: The journey from acclimation to

proficiency. Educational Researcher, 32(8), 10-14. Anderson, R. C. (1923). The hobo: The sociology of the homeless man. Chicago: University of

Chicago Press. Anderson, R. C. (1977). The notion of schemata and the educational enterprise. In R. C.

Anderson, R. J. Spiro & W. E. Montague (Eds.), Schooling and the acquisition of knowledge (pp. 415-431). Hillsdale, NJ: Erlbaum.

Arenas, A., Danon, L., Diaz-Guilera, A., Gleiser, P. M., & Guimera, R. (2004). Community

analysis in social networks. The European Physical Journal B, 38, 373-380. Aristotle (1968, original work published ca. 350 B. C.). De Anima (D. W. Hamlyn, Trans.).

London: Oxford University Press. Asch, S. E. (1952). Social Psychology. Englewood Cliffs, NJ: Prentice-Hall Inc. Ashley, R. (2002). Do[n't] change a hair for me: The art of jazz rubato. Music Perception, 19(3),

311-332. Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control

processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation (volume 2) (pp. 89-195). New York: Academic Press.

Austin, J. L. (1962). How to do things with words. Oxford: Oxford University Press. Baily, J. (1996). Using tests of sound perception in fieldwork. Yearbook for Traditional Music,

28, 147-173.

277 Bakagiannis, S., & Tarrant, M. (2006). Can music bring people together? Effects of shared music

preference on intergroup bias in adolescence. Scandinavian Journal of Psychology, 47, 129-136.

Balkwill, L.-L., & Thompson, W. F. (1999). A cross-cultural investigation of the perception of

emotion in music: Psychophysical and cultural cues. Music Perception, 17(1), 43-64. Bangert, M., Peschel, T., Schlaug, G., Rotte, M., Drescher, D., Hinrichs, H., et al. (2003). Shared

networks for auditory and motor processing in professional pianists: Evidence from fMRI conjunction. NeuroImage, 30(3), 917-926.

Bar-Yosef, A. (2007). A cross-cultural structural analogy between pitch and time organizations.

Music Perception, 24(3), 265-280. Barsalou, L. W. (1993). Flexibility, structure, and linguistic vagary in concepts: Manifestations

of a compositional system of perceptual symbols. In A. C. Collins, S. E. Gathercole & M. A. Conway (Eds.), Theories of memory (pp. 29-101). London: Lawrence Erlbaum Associates.

Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge,

England: Cambridge University Press. Becker, H. (1963). Outsiders: Studies in sociology of deviance. London: Free Press of Glencoe. Becker, H. S. (1951). The professional dance musician and his audience. The American Journal

of Sociology, 57(2), 136-144. Békésy, G. v. (1960). Experiments in hearing. Oxford, England: Mcgraw Hill. Benadon, F. (2003). Spectrographic and calligraphic cues in the identification of jazz

saxophonists. Paper presented at the 5th Triennial ESCOM Conference, Hanover University of Music and Drama, Germany.

Berenzweig, A., Ellis, D., & Lawrence, S. (2002). Using voice segments to improve artist

classification of music. Paper presented at the AES 22nd International Conference. Berlin, B., & Kay, P. (1969). Basic color terms: Their universality and evolution. Berkeley:

University of California Press. Berliner, P. (1994). Thinking in jazz: The infinite art of improvisation. Chicago: The University

of Chicago Press. Berlyne, D. E. (1970). Novelty, complexity, and hedonic value. Perception and Psychophysics,

8(5-A), 279-286.

278 Biederman, I. (1987). Recognition-by-components: A theory of human image understanding.

Psychological Review, 94, 115-147. Biederman, I., Beiring, E., Ju, G., & Blickle, T. (1985). A comparison of the perception of partial

vs. degraded objects. Unpublished manuscript, State University of New York at Buffalo. Bjorkland, D. F., Muir-Broaddus, J. E., & Schneider, W. (1990). The role of knowledge in the development of strategies. In D. F. Bjorkland (Ed.), Children's strategies: Contemporary views of cognitive development (pp. 93-128). Hillsdale, NJ: Erlbaum.

Blacking, J. (1974). How musical is man? Seattle: University of Washington Press. Blackmore, S. J. (1998). Imitation and the definition of a meme. Journal of Memetics –

Evolutionary Models of Information Transmission, 2(2), 1-14. Bloom, H. (1973). The anxiety of influence. New York: Oxford University Press. Borgatti, S. P. (2002). Netdraw network visualization. Harvard, MA: Analytic Technologies. Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). UCINET for windows: Software for

social network analysis. Harvard, MA: Analytic Technologies. Bourdieu, P. (2003). Participant objectivation. Journal of the Royal Anthropological Institute, 9,

281-294. Bransford, J. D., & Franks, J. J. (1971). The abstraction of linguistic ideas. Cognitive

Psychology, 2, 331-350. Bregman, A. S., & Campbell, J. (1971). Primary auditory stream segregation and perception of

order in rapid sequences of tones. Journal of Experimental Psychology, 89, 244-249. Brewer, M. (2000). Research design and issues of validity. In H. Reis & C. Judd (Eds.),

Handbook of research methods in social and personality psychology. Cambridge: Cambridge University Press.

Brewer, W. F. (1987). Schemas versus mental models in human memory. In P. Morris (Ed.),

Modelling cognition (pp. 187-197). Chichester: Wiley. Brittin, R. V. (1991). The effect of overtly categorizing music on preference for popular music

styles. Journal of Research in Music Education, 39(2), 143-151. Broadbent, D. E. (1957). A mechanical model for human attention and immediate memory.

Psychological Review, 64, 205-307. Brooks, L. R. (1978). Nonanalytic concept formation and memory for instances. In E. Rosch &

B. B. Lloyd (Eds.), Cognition and categorization (pp. 169-211). Hillsdale, NJ: Erlbaum.

279 Brown, C., & Lloyd-Jones, T. J. (2006). Beneficial effects of verbalization and visual

distinctiveness on remembering and knowing faces. Memory and Cognition, 34, 277-286. Bruce, R., & Kemp, A. (1993). Sex-stereotyping in children's preferences for musical

instruments. British Journal of Music Education, 10, 213-217. Bruner, J., Goodnow, J., & Austin, G. (1956). A study of thinking. New York: John Wiley. Bruner, J. S. (1957). Going beyond the information given. In J. S. Bruner, E. Brunswik, L.

Festinger, F. Heider, K. F. Muenzinger, C. E. Osgood & D. Rapaport (Eds.), Contemporary approaches to cognition (pp. 218-238). Cambridge, MA: Harvard University Press.

Burkholder, P. (2007). A simple model for associative musical meaning. In B. Almen & E.

Pearsall (Eds.), Approaches to meaning in music. Bloomington, IN: Indiana University Press.

Burt, R. S. (1984). Network items and the general social survey. Social Networks, 6(293-339). Burt, R. S. (1985). General social survey items. Connections, 8, 119-122. Carr, I. (1999). Miles Davis: The definitive biography. New York: Thunder's Mouth Press. Castellano, M. A., Bharucha, J. J., & Krumhansl, C. L. (1984). Tonal hierarchies in the music of

north India. Journal of Experimental Psychology: General, 113(3), 394-412. Cermak, L., & Craik, F. (1979). Levels of processing in human memory. Hillsdale, NJ: Erlbaum. Chaffin, R., & Imreh, G. (1997). "Pulling teeth and torture": Musical memory and problem

solving. Thinking and Reasoning, 3(4), 315-336. Chaffin, R., & Imreh, G. (2002). Practicing perfection: Piano performance as expert memory.

Psychological Science, 13(4), 342-349. Chase, W. G., & Simon, H. A. (1973a). Perception in chess. Cognitive Psychology, 4, 55-81. Chase, W. G., & Simon, H. A. (1973b). The mind's eye in chess. In W. G. Chase (Ed.), Visual

information processing. New York: Academic Press. Chomsky, N. (1966). Topics in the theory of generative grammar. The Hague: Mouton. Clark, H. H. (1996). Using language. Cambridge, MA: Cambridge University Press.

280 Clark, H. H., & Brennan, S. A. (1991). Grounding in communication. In L. B. Resnick (Ed.),

Perspectives on socially shared cognition (pp. 127-149): American Psychological Association.

Clarke, E. F. (2005). Ways of listening: An ecological approach to the perception of musical

meaning. New York: Oxford University Press. Clarke, E. F., & Krumhansl, C. L. (1990). Perceiving musical time. Music Perception, 7(3), 213-

252. Cohen, A. K., & Short, J. F. (1958). Research in delinquent subcultures. The Journal of

Sociological Issues, 14(3), 20-37. Cohen, B. H. (1963). Recall of categorized word sets. Journal of experimental Psychology 66,

227-234. Cohen, S. (1991). Rock culture in Liverpool. Oxford: Oxford University Press. Coker, W. (1972). Music and meaning: A theoretical introduction to musical aesthetics. New

York: Free Press. Collins, A. M., & Loftus, E. F. (1975). A spreading activation theory of semantic processing.

Psychological Review. Collins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. Journal of

Verbal Learning and Verbal Behavior, 8(2), 240-247. Collins, A. M., & Quillian, M. R. (1970). Facilitating retrieval from semantic memory: The

effect of repeating part of an inference. Acta Psychologica, 33, 304-314. Collins, A. M., & Quillian, M. R. (1972). Experiments on semantic memory and language

comprehension. In L. W. Gregg (Ed.), Cognition in learning and memory (pp. 117-138). New York: John Wiley.

Conrad, C. (1972). Cognitive economy in semantic memory. Journal of Experimental

Psychology, 55, 75-84. Cooke, D. (1959). The language of music. Oxford: Oxford University Press. Craik, F. I. M., & Lockhart, R. S. (1972). Levels of processing: A framework for memory

research. Journal of Verbal Learning and Verbal Behavior, 11, 671-684. Cross, I. (2001). Music, cognition, culture, and evolution. Annals of the New York Academy of

Sciences, 930, 28-42.

281 Cuddy, L., & Badertscher, B. (1987). Recovery of the tonal hierarchy: Some comparisons across

age and levels of musical experience. Perception and Psychophysics, 41, 609-620. Curtis, M. E., & Bharucha, J. J. (2009). Memory and musical expectation for tones in cultural

context. Music Perception, 26(365-75). Darrow, A. A., Haack, P. A., & Kuribayashi, F. (1987). Descriptors and preferences for Eastern

and Western musics by Japanese and American nonmusic majors. Journal of Research in Music Education, 35, 237-248.

Davidson, J. W., & Edgar, R. (2003). Gender and race bias in the judgment of western art music

performance. Music Education Research, 5(2), 169-182. Davidson, J. W., & Good, J. M. (2002). Social and musical co-ordination between members of a

string quartet: An exploratory study. Psychology of Music, 30(2), 186-201. Davis, C. A., & Ashley, R. (2005). Jazz as a musical conversation: How do jazz musicians

communicate with each other? Paper presented at the Performance Matters! International Conference on Practical, Psychological, Philosophical and Educational Issues in Musical Performance, Porto, Portugal, 14-17 September.

Davis, M., & Troupe, Q. (1989). Miles: The autobiography. New York: Touchstone. Dawkins, R. (1976). The selfish gene. New York, NY: Oxford University Press. de Groot, A. D. (1965). Thought and choice in chess (1st ed.). The Hague, Netherlands: Mouton

Publishers. De Stefano, R. (1995). Wes Montgomery's improvisational style (1959-63): The riverside years.

University of Montreal, Montreal. Deese, J. (1965). The structure of associations in language and thought. Baltimore, MD: The

Johns Hopkins Press. Deliège, I. (1987). Grouping conditions in listening to music: An approach to Lerdahl &

Jackendoff's grouping preference rules. Music Perception, 4(4), 325-360. Deliège, I. (1989). A perceptual approach to contemporary musical forms. Contemporary Music

Review, 4, 213-230. Deliège, I. (1991). L'organisation psychologique de l'écoute de la musique. Des marques de

sédimentation - indice, empreinte - dans la represésentation mentale de l'oeuvre., Thèse doctrale, Université de Liège, Liège.

282 Deliège, I. (1992). Recognition of the Wagnerian leitmotiv. Jahrbuch der Deutschen

Gesellschaft für Musikpsychologie, 9, 25-54. Deliège, I. (2006). Analogy: Creative support to elaborate a model of music listening. In I.

Deliége & G. Wiggins (Eds.), Musical creativity: Multidisciplinary research in theory and practice (pp. 63-77). London: Psychology Press.

Deliége, I., Melen, M., Stammers, D., & Cross, I. (1996). Musical schemata in real-time listening

to a piece of music. Music Perception, 14(2), 117-160. DeMichael, D. (1962). John Coltrane and Eric Dolphy answer the jazz critics. Downbeat, April

2, 20-23. DeNora, T. (2000). Music in everyday life. Cambridge: Cambridge University Press. DeNora, T. (2003). After Adorno: Rethinking music sociology. Cambridge: Cambridge

University Press. Denzin, N. K., & Lincoln, Y. S. (2003). Strategies of qualitative inquiry. Thousand Oaks,

California: Sage. Desain, P. (1992). A (de)composable theory of rhythm perception. Music Perception, 9(4), 439-

454. DeSoto, C. B. (1961). The predilection for single orderings. Journal of Abnormal and Social

Psychology, 62, 16-24. Deustch, D. (1975). Two-channel listening to musical scales. Journal of the Acoustical Society of

America, 57, 1156-1160. Deustch, D. (1999). The psychology of music. New York: Academic Press. Deustch, D., Henthorn, T., & Dolson, M. (2004). Absolute pitch, speech, and tone language:

Some experiments and a proposed framework. Music Perception, 21(3), 339-356. Dewar, K. M., Cuddy, L. L., & Mewhort, D. J. (1977). Recognition memory for single tones

with and without context. Journal of Experimental Psychology: Human Learning and Memory, 3, 60-67.

Dewey, M. E. (1983). Coefficients of agreement. British Journal of Psychiatry, 143, 487-489. Dienes, Z., & Perner, J. (1999). A theory of implicit and explicit knowledge. Behavioral and

Brain Sciences, 22, 735-808.

283 Dorr, N. (2008). Jazz? A look at the blur between jazz and everything else with the vanguard of

Brooklyn's avant scene. Features. Retrieved March 1, 2009, from http://www.imposemagazine.com/jazz/10346/.

Dowling, J. (1973). The perception of interleaved melodies. Cognitive Psychology, 5, 322-337. Dowling, J., & Fujitani, D. S. (1971). Contour, interval, and pitch recognition in memory for

melodies. Journal of the Acoustical Society of America, 49, 524-531. Dowling, J., & Harwood, D. (1986). Music cognition. San Diego: Academic Press. Dowling, W. J. (1978). Scale and contour: Two components of a theory of memory for melodies.

Perception and Psychophysics, 23(4), 341-354. Dunn, D. (1999). Purposeful listening in complex states of time (score). In B. LaBelle & S.

Roden (Eds.), Site of sound: Of architecture and the ear. Copenhagen/Los Angeles: Errant Bodies Press.

Dunscomb, R., & Hill, W. L. (2002). Jazz pedagogy: The jazz educator's handbook and resource

guide. New York: Alfred Publishing. Edmonds, E. M., & Smith, M. E. (1923). The phenomenological description of musical intervals.

American Journal of Psychology, 34, 287-291. Einhorn, H., Kleinmuntz, D., & Kleinmuntz, B. (1979). Linear regression and process-tracing

models of judgment. Psychological Review, 465-85. Ekman, P. (1972). Universals and cultural differences in facial expressions of emotion. In J. Cole

(Ed.), Nebraska symposium on motivation (Vol. 19, pp. 207-283). Lincoln, NE: University of Nebraska Press.

Ekman, P., & Friesen, W. V. (1969). The repertoire of nonverbal behavior: Categories, origins,

usage, and coding. Semiotica, 1, 49-98. Ekman, P., & Friesen, W. V. (1971). Constants across cultures in the face and emotion. Journal

of Personality and Social Psychology, 17, 124-129. Ekman, P., Friesen, W. V., O'Sullivan, M., Chan, A., Diacoyanni-Tarlatzis, I., Heider, K., et al.

(1987). Universals and cultural differences in the judgments of facial expressions of emotion. Journal of Personality and Social Psychology, 53(4), 712-717.

Ellington, D. E. (1976). Music is my mistress. Garden City, NY: Doubleday. Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102,

211-245.

284 Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis. Cambridge, MA: MIT Press. Erlewine, M., Bogdanov, V., Woodstra, C., & Yanow, S. (1998). All music guide to jazz: The

experts' guide to the best jazz recordings. San Francisco: Miller Freeman Books. Estes, W. K. (1976). The cognitive side of probability learning. Psychological Review, 83, 37-64. Fernandez, A., Diez, E., Alonso, M. A., & Beato, M. S. (2004). Free-association norms for the

Spanish names of the Snodgrass and Vanderwart pictures. Behavior Research Methods, Instruments, and Computers, 36(3), 577-583.

Finnäs, L. (1989). A comparison between young people's privately and publicly expressed

musical preferences. Psychology of Music, 17, 132-145. Finnegan, R. (2007). The hidden musicians: Music-making in an English town, 2nd edn,

Music/Culture series. Middletown: Wesleyan University Press. Fisher, R. A. (1925). Applications of "student's" distribution. Metron, 5, 90-104. Fodor, J. A. (1983). Modularity of mind: An essay on faculty psychology. Cambridge, MA: MIT

Press. Francès, R. (1958). La perception de la musique. Paris: Vrin. Freedman, J. L., & Loftus, E. F. (1971). Retrieval of words from long-term memory. Journal of

Verbal Learning and Verbal Behavior, 10, 107-115. Frith, S. (1981). Sound effects: Youth, leisure and the politics of rock 'n' roll. New York:

Pantheon. Fung, H. H. (1994). The socialization of shame in young Chinese children. Unpublished doctoral

dissertation. University of Chicago, Chicago. Furnham, A., & Walker, J. (2001). Personality and judgments of abstract, pop art, and

representational paintings. European Journal of Personality, 15, 57-72. Gabrielsson, A., & Juslin, P. (1996). Emotional expression in music performance: Between the

performer's intention and the listener's experience. Psychology of Music, 24, 68-91. Galton, F. R. S. (1879). Psychometric experiments. Brain, 2, 149-162. Garrigues, C. H. (1959, October 11). Recapturing the magic of miles. San Francisco Examiner.

285 Gebhardt, N. (2001). Going for jazz: Musical practices and American ideology. Chicago:

University of Chicago Press. Gelly, D., & Bacon, T. (2000). Masters of jazz saxophone: The story of the players and their

music. London: Balafon Books. Giaquinto, G., Bledsoe, C., & McGuirk, B. (2009). Influence and similarity between

contemporary jazz artists, plus six degrees of kind of blue. University of Michigan. Gibson, E. J. (1969). Principles of perceptual learning and development. New York: Appleton. Gibson, E. J., & Gibson, E. (1955). Perceptual learning: Differentiation or enrichment?

Psychological Review, 62, 32-41. Gibson, J. J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin. Gigerenzer, G., Todd, P. M., & Group, t. A. R. (1999). Simple heuristics that make us smart.

New York: Oxford University Press. Gil-White, F. J. (2001). Sorting is not categorization: A critique of the claim that Brazilians have

fuzzy racial categories. Journal of Cognition and Culture, 1(3), 219-249. Gioia, T. (1997). The history of jazz. New York: Oxford University Press. Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks.

Proceedings of the National Academy of Sciences, 99(12), 7821-7826. Gleiser, P. M., & Danon, L. (2003). Community structure in jazz. Advances in complex systems,

6(4), 565-573. Gourse, L. (1997). Straight, no chaser: The life and genius of Thelonious Monk New York:

Schirmer Trade Books. Gracyk, T. (2004). Does everyone have a musical identity? Reflections on Musical Identities.

Action, Criticism, and Theory for Music Education, 3(1). Graham, J. A., & Argyle, M. (1975). A cross-cultural study of the communication of extra-verbal

meaning by gestures. International Journal of Psychology, 10(1), 57-67. Green, L. (2001). How popular musicians learn. Aldershot, UK: Ashgate Publishers. Grey, J. M. (1977). Multidimensional perceptual scaling of musical timbre. Journal of the

Acoustical Society of America, 61, 1270-1277.

286 Grice, H. P. (1975). Logic and conversation. In P. Cole & J. L. Morgan (Eds.), Syntax and

semantics, vol. 3 (pp. 41-58). New York, NY: Academic Press. Gridley, M. C. (1983). Perception of saxophone tone color. Proceedings of NAJE Research, 3,

47-51. Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods. Riverside, CA:

University of California Riverside (published in digital form at http://faculty.ucr.edu/~hanneman/).

Hanslick (1891). The beautiful in music: A contribution to the revisal of musical aesthetics (G.

Cohen, Trans.). London: Novello, Ewer and Co. Hargreaves, D. J. (1984). The effects of repetition on liking for music. Journal of Research in

Music Education, 32, 35-47. Hargreaves, D. J. (1999). Response to "Improvised conversations: Music, collaboration, and

development," by Keith Sawyer. Psychology of Music, 27(2), 205-207. Hargreaves, D. J., & North, A. C. (1997). The social psychology of music. Oxford: Oxford

University Press. Haygood, R. C., & Bourne, L. E. (1965). Attribute- and rule-learning aspects of conceptual

behavior. Psychological Review, 72, 175-195. Hebb, D. O. (1949). The organization of behavior. New York: Wiley. Heider, F. (1944). Social perception and phenomenal causality. Psychological Review, 51, 358-

374. Hellevik, O. (1988). Introduction to causal analysis: Exploring survey data by cross-tabulation

(2nd edition). New York, NY: Oxford University Press. Helmholtz, H. L. F. (1877). On the sensations of tone: As a physiological basis for the theory of

music. New York: Dover Publications. Hevner, K. (1935a). The affective character of the major and minor modes in music. American

Journal of Psychology, 47, 103-118. Hevner, K. (1935b). Expression in music: A discussion of experimental studies and theories.

Psychological Review, 47, 186-204. Hevner, K. (1936). Experimental studies of the elements of expression in music. American

Journal of Psychology, 48, 223-248.

287 Hevner, K. (1937). The affective value of pitch and tempo in music. American Journal of

Psychology, 49, 621-630. Hintzman, D. L. (1978). The psychology of learning and memory. San Francisco: Freeman

Publishers. Hood, M. (1960). The challenge of "bi-musicality". Ethnomusicology, 4(2), 55-59. Hull, C. L. (1920). Quantitative aspects of the evolution of concepts. Psychological Monographs,

XXVIII(1.123), 1-86. Huron, D. (2006). Sweet anticipation: Music and the psychology of expectation. Cambridge,

MA: MIT Press. Huron, D., & Ollen, J. (2003). Agogic contrast in French and English themes: Further support for

Patel and Daniele (2003). Music Perception, 21(2), 267-271. Iacobucci, D. (1994). Graph theory. In S. Wasserman & K. Faust (Eds.), Social network

analysis: Methodology and applications (pp. 92-166). New York: Cambridge University Press.

Ianuly, T. N. p., Cornerford, J. W. p., Dahlia, B. a. p., Larson, L. d., Rivoira, M. d., & Vogt, P. J.

d. (2009). Icons among us [motion picture]. New York: Paradigm Studio. Inglefield, H. (1968). The relationship of selected personality variables to conformity behavior

reflected in the musical preferences of adolescents when exposed to peer group leader influences. Unpublished doctoral dissertation, Ohio State University.

Inglefield, H. (1972). Conformity behavior reflected in the musical preference of adolescents.

Contributions to Music Education. Iyer, V. (2004). Exploding the narrative in jazz improvisation. In R. G. O'Meally, B. H. Edwards

& F. J. Griffin (Eds.), Uptown conversation: The new jazz studies. New York: Columbia University Press.

Izard, C. E. (1971). The face of emotion. New York: Appleton-Century-Crofts. Jackson, T. A. (1998). Performance and musical meaning: Analyzing "jazz" on the New York

scene. Unpublished Ph.D. Dissertation, Columbia University, New York. Jaffe, A. (1983). Jazz theory. Dubuque, Iowa: WM. C. Brown Company Publishers. Janata, P., Tillman, B., & Bharucha, J. J. (2002). Listening to polyphonic music recruits domain-

general attention and working memory circuits. Cognitive, Affective, & Behavioral Neuroscience, 2(2), 121-140.

288 Jarvis, B. G. (2008). MediaLab (Version 2008.1.21) [Computer Software]. New York:

Empirisoft Corporation. Jenkins, J. J., & Palermo, D. S. (1965). Further data on changes in word associations norms.

Journal of Personality and Social Psychology, 1, 303-309. Jenkins, J. J., & Russell, W. A. (1952). Associative clustering in recall. Journal of Abnormal and

Social Psychology, 47, 818-821. Jost, E. (1981). Free jazz. New York: DaCapo Press. Juola, J. F., & Atkinson, R. C. (1971). Memory scanning for words versus categories. Journal of

Verbal Learning and Verbal Behavior, 10, 522-527. Juslin, P., Jones, S., Olsson, H., & Winman, A. (2003). Cue abstraction and exemplar memory in

categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(5), 924-941.

Kahn, A. (2000). Kind of blue: The making of the Miles Davis masterpiece. New York: DaCapo. Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect.

Educational Psychologist, 38(1), 23-31. Kant, I. (1781/1787). Critique of pure reason (P. Guyer & A. Wood, Trans.). Cambridge:

Cambridge University Press, 1997. Katz, D., & Kahn, R. L. (1966). The social psychology of organizations. New York: Wiley. Kernfield, B. (1995). What to Listen For in Jazz. New Haven: Yale University Press. Killworth, P. D., Johnsen, E. C., Bernard, H. R., Shelley, G. A., & McCarty, C. (1990).

Estimating the size of personal networks. Social Networks, 12, 289-312. Kippen, J. (1987). An ethnomusicological approach to the analysis of musical cognition. Music

Perception, 5(2), 173-196. Kitayama, S., & Cohen, D. (Eds.). (2007). Handbook of cultural psychology. New York: The

Guilford Press. Kline, T. (2005). Psychological testing: A practical approach to design and evaluation.

Thousand Oaks, CA: Sage. Knapp, R. (2001). Utopian agendas: Variation, allusion, and referential meaning in Brahms's

symphonies. Brahms Studies, 3, 129-190.

289 Koelsch, S., Kasper, E., Sammler, D., Schulze, K., Gunter, T., & Friederici, A. D. (2004). Music,

language and meaning: Brain signatures of semantic processing. Nature Neuroscience, 7(3), 302-307.

Koniari, D., Predazzer, S., & Melen, M. (2001). Categorization and schematization processes

used in music perception by 10- to 11-year-old children. Music Perception, 18(3), 297-324.

Krackhardt, D. (1987a). Cognitive social structures. Social Networks, 9, 109-134. Krackhardt, D., & Porter, L. W. (1985). When friends leave: A structural analysis of the

relationship between turnover and stayer's attitudes. Administrative Science Quarterly, 30, 242-261.

Kraus, D. P. a. D., & Davis, J. P. (2007). Musician [Motion picture]. Chicago, IL: Sheriffmovie. Kreutz, G., Schubert, E., & Mitchell, L. A. (2008). Cognitive styles of music listening. Music

Perception, 26(1), 57-73. Krueger, R., & Casey, M. A. (2000). Focus groups: A practical guide for applied research (3rd

Edition). Thousand Oaks, California: Sage. Krumhansl, C. L. (1990). Cognitive foundations of musical pitch. New York: Oxford University

Press. Krumhansl, C. L., & Castellano, M. A. (1983). Dynamic processes in music perception. Memory

and Cognition, 11, 325-334. Krumhansl, C. L., & Jusczyk, P. W. (1990). Infants' perception of phrase structure in music.

Psychological Science, 1, 70-73. LaBarre, W. (1947). The cultural basis of emotions and gestures. Journal of Personality, 16(1),

49-68. Langer, S. (1954). Philosophy in a new key: A study in the symbolism of reason, rite, and art.

New York: Mentor Books. Laumann, E., Marsden, P., & Prensky, D. (1989). The boundary specific problem in network

analysis. In L. C. Freeman, D. R. White & A. Kimball (Eds.), Network analysis. Fairfax, VA: George Mason University Press.

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation.

Cambridge: University of Cambridge Press.

290 Leach, E. R. (1964). Anthropological aspects of language. In E. H. Lenneberg (Ed.), New

directions in the study of language. Cambridge, MA: The MIT Press. LeBlanc, A. (1981). Generic style music preferences of fifth-grade students. Journal of Research

in Music Education, 27(4), 255-270. Leman, M., & Schneider, A. (1997). Origin and nature of cognitive and systematic musicology.

In M. Leman (Ed.), Music, gestalt, and computing: Studies in cognitive and systematic musicology. Berlin: Springer.

Lerdahl, F., & Jackendoff, R. (1983). A generative theory of tonal music. Cambridge, MA: The

MIT Press. Levine, J. M., Resnick, L. B., & Higgins, E. T. (1993). Social foundations of cognition. Annual

Review of Psychology, 44, 585-612. Levine, M. (1995). The jazz theory book. Petaluma, CA: Sher Music. Lewin, K. (1936). Principles of topical psychology. New York: McGraw-Hill. Lewin, K., Lippitt, R., & White, R. K. (1939). Patterns of aggressive behavior in experimentally

created "social climates". Journal of Social Psychology, 10(3), 43-195. Lewis, G. E. (1996). Improvised music after 1950: Afrological and eurological perspectives. In

D. Fischlin & A. Heble (Eds.), The other side of nowhere: Jazz, improvisation, and communities in dialogue (pp. 131-162). Middletown: Wesleyan University Press.

Lewis, G. E. (2008). A power stronger than itself: The AACM and American experimental music.

Chicago: University of Chicago Press. Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 140, 1-

55. Lincoln, Y., & Guba, E. (1985). Naturalistic inquiry. New York: Sage. Locke, J. (1690; 1997). An essay concerning human understanding. In R. Woolhouse (Ed.). New

York: Penguin Books. Loftus, E. (1974). Reconstructing memory: The incredible eyewitness. Psychology Today, 116-

119. Loftus, E. F. (1973a). Category dominance, instance dominance, and categorization time.

Journal of Experimental Psychology, 97, 70-74.

291 Loftus, E. F. (1973b). Activation of semantic memory. American Journal of Psychology, 86,

331-337. Lomax, A. (1959). Folk song style American Anthropologist, 61(6), 927-954. Lomax, A. (1968). Folk song style and culture. Washington, D.C.: Colonial Press Inc, American

Association for the Advancement of Science. Lombard, M., Snyder-Duch, J., & Bracken, C. C. (2002). Content analysis in mass

communication: Assessment and reporting of intercoder reliability. Human Communication Research, 28, 587-604.

Longfellow, H. W. (1835). Outre-Mer: A pilgrimage beyond the sea. New York: Harper. Lubke, G. H., & Muthen, B. O. (2004). Applying multigroup confirmatory factor models for

continuous outcomes to Likert scale data complicates meaningful group comparisons. Structural Equation Modeling, 11, 514-534.

Macdonald, R. A. R., Hargreaves, D. J., & Miell, D. (Eds.). (2002). Musical identities. New

York: Oxford University Press. Macdonald, R. A. R., & Wilson, G. (2005). Musical identities of professional jazz musicians: A

focus group investigation. Psychology of Music, 33(4), 395-417. Macdonald, R. A. R., & Wilson, G. B. (2006). Constructions of jazz: How jazz musicians present

their collaborative musical practice. Musicae Scientiae, 59-85. Martin, H., & Waters, K. (2002). Jazz: The first 100 years. Boston: Wadsworth-Schirmer. Mayo, E. (1933). The human problems of an industrial civilization. New York: Macmillan. McAdams (1993). The stories we live by: Personal myths and the making of the self. New York:

William C. Morrow and Co. McClary, S. (1991). Feminine endings: Music, gender, and sexuality. Minneapolis: University of

Minnesota Press. McKay, C. (2004). Automatic genre classification as a study of the viability of high-level

features for music classification. Paper presented at the Proceedings of the International Computer Music Conference.

Medin, D. L., Ross, B. H., & Markman, A. B. (1992). Cognitive psychology. New York:

Harcourt Brace College Publishers.

292 Medin, D. L., Ross, N. O., Atran, S., Cox, D., Coley, J., Proffitt, J., et al. (2006). Folkbiology of

freshwater fish. Cognition, 99(3), 237-273. Medin, D. L., Ross, N. O., & Cox, D. (2006). Culture and resource conflict: Why meanings

matter. New York: Russell Sage Foundation. Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological

Review, 85(3), 207-238. Medin, D. L., & Smith, E. E. (1981). Strategies and classification learning. Journal of

Experimental Psychology: Human Learning and Memory, 7, 241-253. Meinz, E. J. (2000). Experience-based attenuation of age-related differences in music cognition

tasks. Psychology and Aging, 15(2), 297-312. Merriam, A. P. (1964). The anthropology of music. Chicago Northwestern University Press. Merriam, A. P., & Mack, R. W. (1960). The jazz community. Social Forces, 38, 211-222. Mertens, D. M. (1998). Research methods in education and psychology: Integrating diversity

with quantitative and qualitative approaches. Thousand Oaks, California: Sage Publications.

Merton, R. K., Fiske, M., & Kendall, P. L. (1990). The focused interview: A manual of problems

and procedures (2nd Ed.). London: Collier MacMillan. Merton, R. K., & Kendall, P. L. (1946). The focused interview. American Journal of Sociology,

51, 541-557. Mervis, C. B., & Rosch, E. (1981). Categorization of natural objects. Annual Review of

Psychology, 32, 89-115. Meyer, D. E. (1970). On the representation and retrieval of stored semantic information.

Cognitive Psychology, 1, 242-300. Meyer, L. (1956). Emotion and meaning in music. Chicago: University of Chicago Press. Meyer, L. (1967). Music, the arts, and ideas. Chicago: University of Chicago Press. Meyer, L. B. (1973). Explaining music: Essays and explorations. Berkeley, CA: University of

California Press. Meyer, L. B. (1989). Style and music: Theory, history, and ideology. Philadelphia: University of

Pennsylvania Press.

293 Meyer, L. M. (1980). Exploiting limits: Creation, archetypes and style-changes. Daedalus,

109(2), 177-205. Meyer, R. K., Palmer, C., & Mazo, M. (1998). Affective and coherent responses to Russian

laments. Music Perception, 16(1), 135-150. Milgram, S. (1967). The small-world problem. Psychology Today, 1, 61-67. Miller, G. A., & Heise, G. A. (1950). The trill threshold. Journal of the Acoustical Society of

America, 22, 720-725. Mingus, C., & King, N. (1971). Beneath the underdog: His world as composed by Mingus. New

York: Alfred A. Knopf, Inc. Minsky, M., & Papert, S. (1974). Artificial intelligence. Eugene, Oregon: Condon Lectures,

Oregon/State System of Higher Education. Moisala, P. (1991). Cultural cognition in music: Continuity and change in the gurung music of

Nepal. Jyvaskyla: Gummerus Kirjapaino Oy. Moisala, P. (1995). Cognitive study of music as culture: Basic premises for cognitive

ethnomusicology. Journal of New Music Research, 24, 8-20. Monson, I. (1994). Doubleness and jazz improvisation: Irony, parody, and ethnomusicology.

Critical Inquiry, 20, 283-313. Monson, I. (1996). Saying something: Jazz improvisation and interaction. Chicago: University

of Chicago Press. Moore, C., Romney, A. K., & Hsia, T. (2002). Cultural, gender, and individual differences in

perceptual and semantic structures of basic colors in Chinese and English. Journal of Cognition and Culture, 2(1), 1-28.

Moore, D. S., & McCabe, G. P. (1999). Introduction to the practice of statistics. New York:

W.H. Freeman & Company. Morgan, D. L. (1997). Focus groups as qualitative research (2nd Edition). London: Sage. Morgan, D. L., & Schwalbe, M. L. (1990). Mind and self in society: Linking social structure and

social cognition. Social Psychology Quarterly, 53(2), 148-164. Morgan, D. L., & Spanish, M. T. (1985). Social interaction and cognitive organisation of health-

relevant behavior. Sociology of Health and Illness, 7, 401-422.

294 Muir-Broaddus, J. E. (1998). Name seven words: Demonstrating the effects of knowledge on

rate of retrieval. Teaching of Psychology, 25(2), 119-120. Murphy, J. P. (1990). Jazz improvisation: The joy of influence. The Black Perspective in Music,

18, 7-19. Myers, C. S. (1922). Individual differences in listening to music. British Journal of Psychology,

13, 52-71. Narmour, E. (1974). The melodic structure of tonal music. University of Chicago, Chicago. Narmour, E. (1977). Beyond schenkerism: The need for alternatives in music analysis. Chicago:

University of Chicago Press. Neisser, U. (1967). Cognitive psychology. Englewood Cliffs, NJ: Prentice-Hall. Neisser, U. (1982). Memory observed: Remembering in natural contexts. New York: W. H.

Freeman and Company. Nelson, D. L., & McEvoy, C. (2000). What is this thing called frequency? Memory and

Cognition, 28(4), 509-522. Nelson, D. L., McEvoy, C., & Dennis, S. (2000). What is free association and what does it

measure? Memory and Cognition, 28(6), 887-899. Nelson, D. L., McEvoy, C., & Schreiber, T. A. (2004). The University of South Florida free

association, rhyme, and word fragment norms. Behavior Research Methods, Instruments, and Computers, 36(3), 402-407.

Nelson, D. L., & Zhang, N. (2000). The ties that bind what is known to the recall of what is new.

Psychonomic Bulletin & Review, 7, 604-617. Nettl, B. (1956). Music in primitive culture. Cambridge: Harvard University Press. Neville, H., & Heppner, M. J. (1999). Reviewing sequel and proposing a culturally inclusive

ecological model of sexual assault recovery. Applied and Preventive Psychology, 8, 41-62.

Newell, A. (1982). The knowledge level. Artificial Intelligence, 18(1), 87-127. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice

Hall. Nisenson, E. (2001). Making of kind of blue: Miles Davis and his masterpiece. New York: St.

Martin's Press.

295 Norman, D. A. (1967). Temporal confusions and limited capacity processors. Acta Psychologica,

27, 85-94. Norman, D. A. (1973). Memory, knowledge, and the answering of questions. In R. L. Solso

(Ed.), Contemporary issues in cognitive psychology: The loyola symposium. Washington: Winston.

Norman, D. A., Rumelhart, D. E., & Group, t. L. R. (1975). Explorations in cognition. San

Francisco: Freeman. North, A. C., Colley, A. M., & Hargreaves, D. J. (2003). Adolescents' perception of the music of

male and female composers. Psychology of Music, 31, 139-154. North, A. C., & Hargreaves, D. J. (1995). Subjective complexity, familiarity, and liking for

popular music. Psychomusicology, 14, 77-93. North, A. C., & Hargreaves, D. J. (1999). Music and adolescent identity. Music Education

Research, 1, 75-92. Nosofsky, R. M. (1992). Exemplar-based approach to relating categorization, identification, and

recognition. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition. Hillsdale, NJ: Erlbaum.

Osgood, C. E., Suci, G., & Tannenbaum, P. (1957). The measurement of meaning. Urbana, IL:

University of Illinois Press. Packman, J. (2009). Signifyin(g) Salvador: Professional musicians and the sound of flexibility in

Bahia, Brazil's popular music scenes. Black Music Research Journal, 29(1), 83-126. Palermo, D. S., & Jenkins, J. J. (1964). Paired-associate learning as a function of the strengths of

links in the associative chain. Journal of Verbal Learning and Verbal Behavior, 3, 171-175.

Patel, A. D. (2003). Language, music, syntax and the brain. Nature Neuroscience, 6(7), 674-681. Pearson, K. (1896). Mathematical contributions to the theory of evolution. III. Regression,

heredity and panmixia. Philosophical transactions of the royal society A, 187, 253-318. Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the

case of a correlated system of variables is such that it can reasonably be supposed to have arisen from random sampling. Philosophical Magazine, 50, 157-175.

Peirce, C. S. (Ed.). (1931-1958). Collected writings. Cambridge, MA: Harvard University Press.

296 Peretz, I. (2001). Listen to the brain: The biological perspective on musical emotions. In J. A.

Sloboda & P. Juslin (Eds.), Music and emotion: Theory and research. Oxford: Oxford University Press.

Peretz, I., & Gagnon, L. (1999). Dissociation between recognition and emotional judgment for

melodies. Neurocase, 5, 21-30. Peretz, I., Gagnon, L., & Bouchard, B. (1998a). Music and emotion: perceptual determinants,

immediacy, and isolation after brain damage. Cognition, 68(1998), 111-141. Peretz, I., Gaudreau, D., & Bonnel, A. M. (1998b). Exposure effects on music preference and

recognition. Memory and Cognition, 26(5), 884-902. Peretz, I., & Zatorre, R. J. (Eds.). (2003). The cognitive neuroscience of music. New York: NY:

Oxford University Press. Peshkin, A. (1994). The presence of self: Subjectivity in the conduct of qualitative research.

Bulletin of the Council for Research in Music Education, 122, 45-56. Peterson, J. B., & Christenson, P. A. (1987). Political orientation and music preference in the

1980's. Popular Music and Society, 11(4), 1-18. Piaget, J. (1926). The language and thought of the child. New York: Harcourt, Brace. Pierce, C. S. (1880). On the algebra of logic. American Journal of Mathematics, 3, 15-57. Pinker, S. (1997). How the mind works. New York: W.W. Norton. Plato (360 B.C.E.). The laws (B. Jowett, Trans.). Whitefish, MT: Kessinger Publishing Plomp, R., & Levelt, J. M. (1965). Tonal consonance and critical bandwidth. Journal of the

Acoustical Society of America, 38, 548-560. Posner (1973). Cognition: An introduction. Glenview, IL: Scott Foresman. Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental

Psychology, 77, 353-363. Posner, M. J., & Mitchell, R. F. (1967). Chronometric analysis of classification. Psychological

Review, 74(392-409). Posner, M. J., & Mitchell, R. F. (1967). Chronometric analysis of classification. Psychological

Review, 74, 392-409.

297 Povel, D. J., & Jansen, E. (2001). Perceptual mechanisms in music perception. Music Perception,

19, 169-199. Pritchard, R. D., Jones, S. D., Roth, P. L., Stuebing, K. K., & Ekeberg, S. E. (1988). Effects of

group feedback, goal setting, and incentives on organizational productivity. Journal of Applied Psychology, 73(2), 337-358.

Prouty, K. E. (2002). From storyville to state university: The intersection of academic and non-

academic learning cultures in post secondary jazz education. Unpublished doctoral dissertation. Pennsylvania State University.

Puffet, D. (1989). Richard Strauss: Salome. Cambridge: Cambridge University Press. Purwins, H., & Hardoon, D. (2009). Trends and perspectives in music cognition research and

technology. Connection science, 21(2-3), 85-88. Quillian, M. R. (1966). Semantic memory. Unpublished doctoral dissertation. Carnegie Institute

of Technology. Rapport, E. J. (2006). The musical repertoire of Bukharian Jews in Queens, New York. City

University of New York, New York. Ratliff, B. (2009). The jazz ear: Conversations over music. New York: Henry Holt and Co. Reed, S. K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3(3), 382-407. Richards, W. J., & Waters, R. H. (1948). The relationship between verbalization and remote

association. Journal of General Psychology, 39, 167-173. Rips, L. J., Shoben, E. J., & Smith, F. E. (1973). Semantic distance and the verification of

semantic relations. Journal of Verbal Learning and Verbal Behavior, 12, 1-20. Roberson, D., Davies, I., & Davidoff, J. (2000). Color categories are not universal: Replications

and new evidence from a stone-age culture. Journal of Experimental Psychology, 129(3), 369-398.

Rojek, C. (2004). Frank Sinatra. Cambridge, UK: Polity Press. Romney, A. K., & D'Andrade, R. G. (1964). Cognitive aspects of English kin terms. American

Anthropologist, 66(3), 146-170. Romney, A. K., & Moore, C. (1998). Toward a theory of culture as shared cognitive structures.

Ethos, 26(3), 314-337.

298 Romney, A. K., Weller, S. C., & Batchelder, W. H. (1986). Culture as consensus: A theory of

culture and informant accuracy. American Anthropologist, 99, 313-338. Rosch, E. (1977). Classification of real-world objects: Origins and representations in cognition.

In P. N. Johnson-Laird & P. C. Wason (Eds.), Thinking: Reading in cognitive science (pp. 212-222). Cambridge, England: Cambridge University Press.

Rosch, E. (1978). Principles of categorization. . In E. Rosch & B. Lloyd (Eds.), Cognition and

categorization. Hillsdale, N.J.: Erlbaum. Rosch, E., & Mervis, C. B. (1975). Family resemblances: Studies in the internal structure of

categories. Cognitive Psychology, 7, 573-605. Rosch, E. H. (1973). Natural categories. Cognitive Psychology, 4, 328-350. Rosch, E. H. (1975a). Cognitive reference points. Cognitive Psychology, 7, 532-547. Rosch, E. H. (1975b). Cognitive representation of semantic categories. Journal of Experimental

Psychology, 104(3), 192-233. Ross, N. (2004). Culture and cognition: Implications for theory and method. Thousand Oaks,

CA: Sage. Rumelhart, D. E., Lindsay, P. H., & Norman, D. A. (1972). A process model for long-term

memory. In E. Tulving & W. Donaldson (Eds.), Organization and memory. New York: Academic Press.

Rumelhart, D. E., & Ortony, A. (1977). The representation of knowledge in memory. In R. C.

Anderson, R. J. Spiro & W. E. Montague (Eds.), Schooling and the acquisition of knowledge. Hillsdale, NJ: Erlbaum.

Rumelhart, D. E., & Todd, P. M. (1993). Learning and connectionist representations. In D. E.

Meyer & S. Kornblum (Eds.), Attention and performance XIV: Synergies in experimental psychology, artificial intelligence, and cognitive neuroscience (pp. 3-30). Cambridge, MA: MIT Press.

Russell, J. A. (1991b). Negative results on a reported facial expression of contempt. Motivation

and Emotion, 15, 281-291. Russell, J. A. (1993). Forced-choice response format in the study of facial expression. Motivation

and Emotion, 17, 41-51. Russell, J. A. (1994). Is there universal recognition of emotion from facial expression?: A review

of the cross-cultural studies. Psychological Bulletin, 115, 102-141.

299 Russell, P. (1997). Musical tastes and society. In D. J. Hargreaves & A. C. North (Eds.), The

social psychology of music. Oxford: Oxford University Press. Russell, R. (1996). Bird lives! The high life and times of Charlie (yardbird) Parker. New York:

DaCapo Press. Salthouse, T. A. (1996). The processing-speed theory of adult ages differences in cognition.

Psychological Review, 103, 403-428. Salzer, F. (1952). Structural hearing: Tonal coherence in music. New York: Charles Boni. Sapir, E. (1929). The status of linguistics as a science. Language, 5, 209. Schaeffer, B., & Wallace, R. (1970). The comparison of word meanings. Journal of

Experimental Psychology, 86, 144-152. Schenker, H. (1954). Harmony (E. Mann-Borgese, Trans.). In O. Jones (Ed.). Chicago:

University of Chicago Press. Schiffrin, D., Tannen, D., & Hamilton, H. E. (Eds.). (2003). The handbook of discourse analysis.

Oxford: Blackwell Publishers. Schlaug, G. (2003). The brains of musicians. Annals of the New York Academy of Sciences, 930,

281-299. Schmid, M. D. (2003). The Richard Strauss companion. Westport, Connecticut: Praeger

Publishers. Schneider, W. (1985). Toward a model of attention and the development of automatic

processing. In M. Posner & O. S. Marin (Eds.), Attention and performance XI (pp. 475-492). Hillsdale, NJ: Erlbaum.

Schouten, J. F. (1938). The perception of subjective tones. Proceedings of the Koninklijke

Nederlandse Akademie van Wetenschappen, 34, 1418-1424. Searle, J. R. (1969). Speech acts: An essay in the philosophy of language. Cambridge:

Cambridge University Press. Searle, J. R. (1995). The construction of social reality. New York: Free Press. Seeger, A. (1987). Why Suyá Sing: A musical anthropology of an amazonian people. Cambridge:

Cambridge University Press.

300 Selfridge, O. G. (1958). Pandemonium: A paradigm for learning. In D. V. Blake & A. M. Uttley

(Eds.), Proceedings of the symposium on mechanisation of thought processes (pp. 511-529). London.

Selz, O. (1913). Uber die gesetze des geordneten denkverlaufs. Stuttgart: Spemann. Selz, O. (1922). Zur psychologie des produktiven denkens und des irrtums. Bonn: Spermann. Sharifian, F. (2003). On cultural conceptualisations. Journal of Cognition and Culture, 3(3), 187-

207. Shaw, C. R., & McKay, H. D. (1942). Juvenile delinquency in urban areas. Chicago: University

of Chicago Press. Shepard, R. N. (1984). Ecological constraints on internal representation: Resonant kinematics of

perceiving, imaging, thinking, and dreaming. Psychological Review, 91, 417-447. Sheppard, J. A. (1993). Productivity loss in performance groups: A motivational analysis.

Psychological Bulletin, 113(67-81). Sherif, M., Harvey, O. J., White, B. J., Hood, W. R., & Sherif, C. W. (1954). Study of positive

and negative intergroup attitudes between experimentally produced groups: Robbers cave study. University of Oklahoma.

Sherif, M., White, B. J., & Harvey, O. J. (1955). Status in experimentally produced groups.

American Journal of Sociology, 60, 370-379. Sidran, B. (1971). Black talk. New York: Holt, Rinehart and Winston. Sloboda, J. (1985). The musical mind. New York: Oxford University Press. Sloboda, J. A. (1991). Music structure and emotional response: Some empirical findings.

Psychology of Music, 19, 110-120. Sloboda, J. A., & Parker, D. H. H. (1985). Immediate recall of melodies. In P. Howell, I. Cross

& R. West (Eds.), Musical structure and cognition (pp. 143-167). New York: Academic Press.

Smith, F. E., Shoben, E. J., & Rips, L. J. (1974). Structure and process in semantic memory: A

featural model for semantic decisions. Psychological Review, 1, 214-241. Smith, R. D. (2006). The network of collaboration among rappers and its community structure.

Journal of Statistical Mechanics: Theory and Experiment, 2(1).

301 Smits, P. B., Verbeek, J. H., & de Buisonje, C. D. (2002). Learning in practice: Problem based

learning in continuing medical education: A review of controlled evaluation studies. British Journal of Medicine, 324, 153-156.

Snodgrass, J. G., & Vanderwart, M. (1980). A standardized set of 260 pictures: Norms for name

agreement, familiarity and visual complexity. Journal of Experimental Psychology: Human Learning and Memory, 6, 174-215.

Solis, G. (2009). Genius, improvisation, and the narratives of jazz history. In G. Solis & B. Nettl

(Eds.), Musical improvisation: Art, education, and society. Champaign, IL: University of Illinois Press.

Spearman, C. (1937). Psychology down the ages (Vol. 2). Oxford, UK: Macmillan. Stacy, A. W., Leigh, B. C., & Weingardt, K. (1997). An individual-difference perspective

applied to word association. Personality and Social Psychology Bulletin, 23(3), 229-237. Stebbins, R. A. (1989). Music among friends: The social networks of amateur musicians. In A.

R. Blau & J. W. Foster (Eds.), Art and society (pp. 227-242). Albany: State University of New York Press.

Stevens, S. S., & Davis, H. (1936). Psychophysiological acoustics: Pitch and loudness. Journal

of the Acoustical Society of America, 8(1), 1-13. Stewart, D. W., & Shamdasani, P. N. (1990). Focus groups: Theory and practice. London: Sage. Steyvers, M., Shiffrin, R. M., & Nelson, D. L. (2005). Word association spaces for predicting

semantic similarity effects in episodic memory. In A. Healy (Ed.), Experimental cognitive psychology and its applications: Festschrift in honor of Lyle Bourne, Walter Kintsch, and Thomas Landauer. Washington, D.C.: American Psychological Association.

Stich, S. (1983). Beyond belief: From folk psychology to cognitive science. Cambridge, MA:

MIT Press. Storr, A. (1993). Music and the mind. New York: The Free Press. Stravinsky, I. (1936). Chronicle of my life. London: Gollancz. Stycos, J. M. (1981). A critique of focus group and survey research: The machismo case. Studies

in Family Planning, 12(12 P1 1), 871-875. Szwed, J. (2002). So what: The life of Miles Davis. New York: Simon and Schuster.

302 Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin

& S. Worchel (Eds.), The social psychology of intergroup relations. Monterey, CA: Brooks-Cole.

Tarrant, M., North, A. C., & Hargreaves, D. J. (2001). Social categorization, self-esteem, and the

estimated musical preferences of male adolescents. Journal of Social Psychology, 141, 565-581.

Teo, T., Hargreaves, D. J., & Lee, J. (2008). Musical preference, identification, and familiarity.

Journal of Research in Music Education, 56(1), 18-32. Tesser, N. (1998). The playboy guide to jazz: A selective guide to the most important CDs--and

to the history of jazz. New York: Penguin Putnam, Inc. Thelen, E., & Smith, L. B. (1996). A dynamic systems approach to the development of cognition

and action. Cambridge, MA: MIT Press. Thomas, J. C. (1975). Chasin' the trane: The music and mystique of John Coltrane. New York:

Da Capo. Thompson, W. (2004). From sounds to music: The contextualizations of pitch. Music

Perception, 21(3), 431-456. Thompson, W. F., Russo, F. A., & Quinto, L. (2008). Audio-visual integration of emotional cues

in song. Cognition and Emotion, 22(8), 1457-1470. Thrasher, F. M. (1927). The gang: A study of 1,313 gangs in Chicago. Chicago: University of

Chicago Press. Toney, G. T., & Weaver, J. B. (1994). Effects of gender and gender role self-perceptions on

affective reactions to rock music videos. Sex Roles, 30(7/8), 567-583. Tovey, D. F. (1935). Essays in musical analysis. Oxford: Oxford University Press. Tulving, E. (1972). Episodic and semantic memory. In E. Tulving & W. D. (Eds.) (Eds.),

Organization of memory (pp. 382-402). New York, NY: Academic Press. Tulving, E. (1985). How many memory systems are there? American Psychologist, 40, 385-398. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433-460. Turner, J. C. (Ed.). (1978). Social categorization and social discrimination in the minimal group

paradigm. London: Academic Press.

303 Turner, J. C., Hogg, M. A., Oakes, P. J., Reicher, S. D., & Wetherell, M. S. (1987).

Rediscovering the social group: A self-categorization theory. Oxford: Blackwell. Uzzi, B. (2008). A social network's changing statistical properties and the quality of human

innovation. Journal of Physics A: Mathematical and Theoretical, 41, 1-12. van Noorden, L. (1975). Temporal coherence in the perception of tone sequences. Eindhoven

University of Technology, Eindhoven. Vargas, J. H. C. (2008). Jazz and male blackness: The politics of sociability in south central Los

Angeles. Popular Music and Society, 31(1), 37-56. Voss, J., Vesonder, G., & Spilich, G. (1980). Text generation and recall by high-knowledge and

low-knowledge individuals. Journal of Verbal Learning and Verbal Behavior, 19, 651-667.

Walker, R. (1978). Perception and music notation. Psychology of Music, 6(1), 21-46. Walker, R. (1985). Mental imagery and musical concepts: Some evidence from the congenitally

blind. Bulletin of the Council for Research in Music Education, 85, 229-238. Walker, R. (1987). The effects of culture, environment, age and musical training on choices of

visual metaphors for sounds. Perception and Psychophysics, 42(5), 491-502. Walker, R. (1997). Musically significant acoustic parameters and their notations in vocal

performance across difference cultures. Journal of New Music Research, 26(4), 315-345. Walker, R. (2004). Cultural memes, innate proclivities and musical behaviour: A case study of

the western traditions. Psychology of Music, 32(2), 153-190. Wang, R. (2003). Captain Walter Henri Dyett (1901-1969) Retrieved

http://www.jazzinchicago.org/educates/journal/articles/captain-walter-henri-dyett-1901-1969, 2009.

Wasserman, S. (Ed.). (1979). A stochastic model for directed graphs with transition rates

determined by reciprocity. San Francisco: Jossey-Bass. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press. Weinstein, D. (1983). Rock: Youth and its music. Popular Music and Society, 4(3), 2-16. Welker, R. L. (1982). Abstraction of themes from melodic variations. Journal of Experimental

Psychology: Human Perception and Performance, 8(3), 435-447.

304 Wertheimer, M. (1924). A source book of Gestalt psychology (W. Ellis, Trans.). London:

Routledge & Kegan Paul. Whorf, B. (1956). Language, thought and reality. Cambridge, MA: MIT Press. Whyton, T. (2006). Birth of the school: Discursive methodologies in jazz education. Music

Education Research, 8(1), 65-81. Williams, J. K. (1988). Archetypal schemata in jazz themes of the bebop era. Annual Review of

Jazz Studies, 4, 49-74. Williams, M. (1993). The jazz tradition (2nd Edition). New York: Oxford University Press. Wilson, G. B., & MacDonald, R. A. R. (2005). The meaning of the blues: Musical identities in

talk about jazz. Qualitative Research in Psychology, 2, 341-363. Wober, M. (1974). Towards an understanding of the Kiganda concept of intelligence. In J. W.

Berry & P. R. Dasen (Eds.), Culture and cognition: Readings in cross-cultural psychology (pp. 261-280). London: Methuen.

Zbikowski, L. (2002). Conceptualizing music: Cognitive structure, theory, and analysis. New

York: Oxford University Press. Zwicker, E., Flottorp, G., & Stevens, S. S. (1957). Critical bandwidth in loudness summation.

Journal of the Acoustical Society of America, 29, 548-557.

305 APPENDIX A

Focus Group Background Survey Please list the instruments you play and practice, the age at which you started each, and the type of training you received (group lessons, private training, self-taught, or Suzuki)

Instrument Age Type of training __________________________ ________ _____________________ __________________________ ________ _____________________ __________________________ ________ _____________________ __________________________ ________ _____________________ If different from the above age, at what age did you begin playing the instrument consistently (on daily basis), and how many years have you played each consistently? Instrument Age Years _________________________ ________ _______ _________________________ ________ _______ _________________________ ________ _______ _________________________ ________ _______ Do/did you practice? ! Yes ! No For “proficiency” please rate on a scale from 1-10, one being early beginning and 10 being professional level. For “years applicable,” please break down practice tendencies into appropriate time periods (i.e. 1990-1997, 45 min/day 5 days/wk). Instrument Proficiency Hrs per Day/Week Years Applicable __________________ __________ _______________ ___________ __________________ __________ _______________ ___________ __________________ __________ _______________ ___________ __________________ __________ _______________ ___________ Have you participated in school music activities (band, orchestra, choir, or other musical group)? ! Yes ! No. If so, please indicate below:

Type of group Years Participated ____________________________ _______________________ ____________________________ _______________________ ____________________________ _______________________ ____________________________ _______________________ Did you participate in music activities outside of school? ! Yes ! No If so, please indicate below: Type of group Years Participated ____________________________ _______________________ ____________________________ _______________________ ____________________________ _______________________ ____________________________ _______________________ Have you participated in ear training/aural skill courses (any level)? ! Yes ! No Do you have absolute (perfect) pitch? ! Yes ! No

306 Have you taken music courses at the university level? ! Yes ! No. If so, please indicate below (note: this does not have to be an exhaustive list, but should illustrate those courses most significant to your development as a musician): Course Year(s) ___________________________ ___________ ___________________________ ___________ ___________________________ ___________ ___________________________ ___________ ___________________________ ___________ Do you have a degree in music? ! Yes ! No. If so, please describe: ____________________. Do you or have you taught music? ! Yes ! No. If yes, please indicate: Type of class/lessons Years Instructed ___________________________ ___________ ___________________________ ___________ ___________________________ ___________ ___________________________ ___________ Can you read music? ! Yes ! No ! Some. How often do you read music (on a weekly basis), and how would you rate your music-reading proficiency on a scale from 1 to 10? ____________________. How many times do you perform per week (on average)? ______________________. How would you describe the of music you play on a regular basis? You may include a variety of styles or descriptions. Description How Often? ________________________________ ___________ ________________________________ ___________ ________________________________ ___________ ________________________________ ___________ Do you identify yourself with any particular music community in Chicago? What is your primary motivation for collaborating with particular musicians on a regular basis? Please explain: _________________________________________________________________________ _________________________________________________________________________ _________________________________________________________________________ _________________________________________________________________________ _________________________________________________________________________

307 APPENDIX B

Focus Group Study Circle Diagrams Focus Group 1, Participant 1

308 Focus Group 1, Participant 2

309 Focus Group 1, Participant 3

310 Focus Group 1, Participant 4

311 Focus Group 1, Participant 5

312 Focus Group 1, Participant 6

313 Focus Group 1, Participant 7

314 Focus Group 2, Participant 1

315 Focus Group 2, Participant 2

316 Focus Group 2, Participant 3

317 Focus Group 2, Participant 4

318 Focus Group 2, Participant 5

319 APPENDIX C

Name Associations Excerpt: Louis Armstrong, Heebie Jeebies

Perf./No. Name Freq Perf./No. Name FreqLA1 Wynton Marsalis 13 LA45 Jabbo Smith 1LA2 King Oliver 13 LA46 Tito Carrillo 1LA3 Roy Eldridge 7 LA47 Pharez Whitted 1LA4 Sidney Bechet 6 LA48 Bob Perna 1LA5 ng 6 LA49 Orbert Davis 1LA6 Bix Beiderbecke 6 LA50 Bob Koester 1LA7 Baby Dodds 5 LA51 Keefe Jackson 1LA8 Louis Armstrong 5 LA52 Jack Teagarden 1LA9 Lil Armstrong 4 LA53 Steven Bernstein 1LA10 Ella Fitzgerald 3 LA54 Lestor Bowie 1LA11 Nicholas Payton 3 LA55 Charlie Christian 1LA12 Duke Ellington 3 LA56 Peter Bartols 1LA13 Cootie Williams 3 LA57 Franz Jackson 1LA14 Miles Davis 3 LA58 Rick Falato 1LA15 Jelly Roll Morton 3 LA59 Bryan Tipps 1LA16 Sweets Edison 3 LA60 Tony Alaniz 1LA17 Buddy Bolden 3 LA61 Clark Terry 1LA18 Thelonious Monk 2 LA62 Red Norvo 1LA19 Art Davis 2 LA63 Lester Young 1LA20 Dizzy Gillespie 2 LA64 Von Freeman 1LA21 Johnny Dodds 3 LA65 George Bean 1LA22 Jon Faddis 2 LA66 Zutty Singleton 1LA23 Josh Berman 2 LA67 Pee Wee Russell 1LA24 Earl Hines 2 LA68 Johnny St. Cyr 1LA25 Bobby Lewis 1 LA69 Kenny G 1LA26 Frank Sinatra 1 LA70 Charlie Haden 1LA27 Al Hirt 1 LA71 James Davis 1LA28 Fats Waller 1 LA72 Hot Five 1LA29 Dan DeLorenzo 1 LA73 Don Byron 1LA30 Tommy Dorsey 1LA31 Composer of Moonlight the Stars and You 1LA32 Freddie Hubbard 1LA33 Chet Baker 1LA34 Tom Waits 1LA35 Don Cherry 1LA36 Cannonball Adderley 1LA37 Erroll Garner 1LA38 Charlie Parker 1LA39 Nappy Tradier 1LA40 Ruby Braff 1LA41 Zaide Krisberg 1LA42 Rob Parton 1LA43 Kermit Ruffins 1LA44 Kid Ory 1

320 Excerpt: Ornette Coleman, Lonely Woman

Perf./No. Name Freq Perf./No. Name FreqOC1 Don Cherry 16 OC45 Mike Lebrun 1OC2 Charlie Haden 16 OC46 Jeb Bishop 1OC3 Charlie Parker 9 OC47 Mars Williams 1OC4 Dewey Redman 7 OC48 Muddy Waters 1OC5 Ornette Coleman 5 OC49 Johnny Hodges 1OC6 Ed Blackwell 4 OC50 Pat Metheny 1OC7 Billy Higgins 4 OC51 Charles Lloyd 1OC8 John Zorn 4 OC52 Arthur Blythe 1OC9 ng 4 OC53 Keefe Jackson 1OC10 John Coltrane 3 OC54 Lee Konitz 1OC11 Eric Dolphy 3 OC55 Tony Malaby 1OC12 Caroline Davis 3 OC56 Jim Black 1OC13 Miles Davis 3 OC57 Marty Tilton 1OC14 Sarah Vaughn 2 OC58 Geof Bradfield 1OC15 Joe Lovano 2 OC59 Quin Kirchner 1OC16 Julius Hemphill 2 OC60 Ethan Iverson 1OC17 Greg Ward 2 OC61 Clark Sommers 1OC18 Josh Berman 2 OC62 Mike Lewis 1OC19 Cannonball Adderley 2 OC63 Dave Liebman 1OC20 Kenny Garrett 2 OC64 Rob Mazurek 1OC21 Jeff Parker 2 OC65 Ted Sirota 1OC22 Jackie Mclean 2 OC66 Mahalia Jackson 1OC23 Albert Ayler 2 OC67 Sonny Simmons 1OC24 Rudy Manthahappa 1 OC68 Jameel Moondoc 1OC25 Charles Gorczynski 1 OC69 Chris Vielleux 1OC26 Greg Osby 1 OC70 Sun Ra 1OC27 Fred Anderson 1 OC71 Dave Douglas 1OC28 Hank Crawford 1 OC72 Chris Potter 1OC29 Aram Shelton 1 OC73 Keith Jarrett 1OC30 Richard Davis 1 OC74 Scott Colley 1OC31 Jeff Beer 1 OC75 Dave Rempis 1OC32 Dan DeLorenzo 1OC33 Dave Bryant 1OC34 John Turner 1OC35 Charles Mingus 1OC36 Andrew D'Angelo 1OC37 Ken Vandermark 1OC38 Remi LeBouf 1OC39 James Spaulding 1OC40 Anthony Braxton 1OC41 Louis Armstrong 1OC42 Sam Rivers 1OC43 Ron Dewar 1OC44 Chris McBride 1

321 Excerpt: John Coltrane, Giant Steps

Perf./No. Name Freq Perf./No. Name FreqJC1 Tommy Flanagan 12 JC45 Matt Martin 1JC2 Sonny Rollins 11 JC46 Josh Burke 1JC3 Elvin Jones 10 JC47 Chris Weller 1JC4 Michael Brecker 7 JC48 Coleman Hawkins 1JC5 McCoy Tyner 7 JC49 Johnny Griffin 1JC6 Wayne Shorter 6 JC50 Eric Alexander 1JC7 Charlie Parker 4 JC51 Ron Perrillo 1JC8 Miles Davis 4 JC52 Ron Dewar 1JC9 ng 4 JC53 Jim Gailloretto 1JC10 Jerry Bergonzi 3 JC54 Jackie McLean 1JC11 Lester Young 3 JC55 Cameron Pfiffner 1JC12 John Coltrane 3 JC56 Wynton Kelly 1JC13 Jimmy Garrison 3 JC57 Dexter Gordon 1JC14 Dave Liebman 3 JC58 Rob Clearfield 1JC15 Paul Chambers 3 JC59 John Smillie 1JC16 Joe Lovano 2 JC60 Art Davis 1JC17 George Garzone 2 JC61 Pat LaBarbara 1JC18 John Wojciechowski 2 JC62 Mark Turner 1JC19 Hank Mobley 2 JC63 Art Taylor 1JC20 Benny Golson 2 JC64 Steve Lacy 1JC21 James Moody 2 JC65 Jimmy Heath 1JC22 Rob Haight 2 JC66 Tom Garling 1JC23 Dewey Redman 2 JC67 Freddie Hubbard 1JC24 Nick Mazzarella 2 JC68 Red Garland 1JC25 Scott Burns 2 JC69 Chip McNeill 1JC26 Charles Lloyd 2 JC70 Pat Metheny 1JC27 Steve Grossman 2 JC71 Branford Marsalis 1JC28 Kenny Garrett 2JC29 Steve Coleman 2JC30 Doug Rosenberg 1JC31 Greg Ward 1JC32 Cannonball Adderley 1JC33 Pharoah Sanders 1JC34 Alice Coltrane 1JC35 Shadow Wilson 1JC36 Bob Mintzer 1JC37 Joe Henderson 1JC38 Charlie Rouse 1JC39 Geof Bradfield 1JC40 Karel van Beekom 1JC41 Ralph Bowen 1JC42 Phil Woods 1JC43 Max Krukoff 1JC44 Mike Lebrun 1

322 Excerpt: Miles Davis, So What

Perf./No. Name Freq Perf./No. Name FreqMD1 Bill Evans 19 MD45 Mike Smith 1MD2 John Coltrane 15 MD46 John Smillie 1MD3 Paul Chambers 11 MD47 Thad Franklin 1MD4 Jimmy Cobb 9 MD48 Mulgrew Miller 1MD5 Cannonball Adderley 8 MD49 Ron Dewar 1MD6 Wallace Roney 6 MD50 Jeff Parker 1MD7 Freddie Hubbard 6 MD51 Johnny Coles 1MD8 Wayne Shorter 5 MD52 Tim Hagens 1MD9 Art Farmer 5 MD53 Ahmad Jamal 1MD10 Gil Evans 4MD11 Philly Joe Jones 3MD12 Wynton Marsalis 3MD13 James Davis 3MD14 Dizzy Gillespie 3MD15 Miles Davis 3MD16 Louis Armstrong 3MD17 Herbie Hancock 3MD18 ng 3MD19 Chet Baker 2MD20 Tony Williams 2MD21 Wynton Kelly 2MD22 Lee Morgan 2MD23 Art Davis 2MD24 Charlie Parker 2MD25 Roy Hargrove 1MD26 Thad Jones 1MD27 Nat Adderley 1MD28 Ramin Khamsei 1MD29 Greg Duncan 1MD30 Benje Daneman 1MD31 Tom Harrell 1MD32 Andrew Oom 1MD33 Tito Carrillo 1MD34 John Hart 1MD35 Fats Navarro 1MD36 Marquis Hill 1MD37 Josh Berman 1MD38 George Benson 1MD39 Pharez Whitted 1MD40 Bobby Broom 1MD41 Jaimie Branch 1MD42 Frank Rosaly 1MD43 Dave Douglas 1MD44 Larry Bowen 1

323 Excerpt: Duke Ellington, Take the ‘A’ Train

Perf./No. Name Freq Perf./No. Name FreqDE1 Count Basie 16 DE45 Jodie Christian 1DE2 Billy Strayhorn 14 DE46 Joe Pass 1DE3 Johnny Hodges 10 DE47 Lee Rothenberg 1DE4 Cootie Williams 7 DE48 Lester Brown 1DE5 Thelonious Monk 6 DE49 Red Mitchell 1DE6 Duke Ellington 5 DE50 Yoko Noge 1DE7 Glenn Miller 4 DE51 Kens Kilian 1DE8 Benny Goodman 4 DE52 John Rapson 1DE9 ng 3 DE53 Laurence Oliver 1DE10 Earl Hines 3 DE54 Jo Jones 1DE11 Woody Herman 3 DE55 Erma Thompson 1DE12 Jimmy Blanton 3 DE56 Eddie Johnson 1DE13 Harry Carney 2 DE57 Allison Orobia 1DE14 Fletcher Henderson 2 DE58 Rick Falato 1DE15 Lester Young 2 DE59 Bill O'Connell 1DE16 Bob Mintzer 2 DE60 Teddy Wilson 1DE17 Louis Armstrong 2 DE61 Chris Potter 1DE18 Art Tatum 2 DE62 Rex Stewart 1DE19 Charles Mingus 2 DE63 George Fludas 1DE20 Sonny Greer 2 DE64 Clark Sommers 1DE21 Mel Torme 2 DE65 Brian O'Hern 1DE22 The Manhattan Transfer 1 DE66 Bob Dogan 1DE23 Billy Eckstine 1 DE67 Wynton Marsalis 1DE24 Nat King Cole 1 DE68 Sid Catlett 1DE25 Gunther Schuller 1 DE69 Cab Calloway 1DE26 Coleman Hawkins 1 DE70 Ben Webster 1DE27 Billie Holiday 1 DE71 James P. Johnson 1DE28 Carl Atkins 1 DE72 Jaki Byard 1DE29 Thad Jones 1 DE73 Juan Tizol 1DE30 Anthony Bruno 1 DE74 Eric Haas 1DE31 Harry Allen 1 DE75 every "current" big band 1DE32 The St. Charles North HS Jazz Ensemble 1 DE76 Carmen McRae 1DE33 Slim Gaillard 1 DE77 Lincoln Center Jazz Orchestra1DE34 Hank Jones 1 DE78 Oscar Peterson 1DE35 Wycliffe Gordon 1DE36 Jimmie Lunceford 1DE37 Joel Spencer 1DE38 Josh Moshier 1DE39 Oscar Pettiford 1DE40 Allan Gressick 1DE41 Doug Stone 1DE42 Clark Terry 1DE43 Laurence Brown 1DE44 Fats Waller 1

324 Excerpt: Herbie Hancock, Dolphin Dance

Perf./No. Name Freq Perf./No. Name FreqHH1 Tony Williams 13 HH45 Kevin Hays 1HH2 Ron Carter 11 HH46 Larry Grenadier 1HH3 Ron Perrillo 11 HH47 Mel Rhyne 1HH4 Chick Corea 10 HH48 Patrick Mulcahy 1HH5 Miles Davis 7 HH49 Paul Chambers 1HH6 Bill Evans 6 HH50 Phil Mattson 1HH7 Wynton Kelly 6 HH51 Rufus Reid 1HH8 Brad Mehldau 5 HH52 Sam Jones 1HH9 Bud Powell 5 HH53 Scott Hesse 1HH10 Keith Jarrett 5 HH54 Stefon Harris 1HH11 ng 5 HH55 Victor Feldman 1HH12 Wayne Shorter 5 HH56 Willie Pickens 1HH13 Herbie Hancock 4HH14 McCoy Tyner 4HH15 Oscar Peterson 4HH16 Freddie Hubbard 3HH17 Dan Cray 2HH18 Gary Peacock 2HH19 Jack DeJohnette 2HH20 Joan Hickey 2HH21 Jodie Christian 2HH22 John Coltrane 2HH23 Mulgrew Miller 2HH24 Red Garland 2HH25 Rob Clearfield 2HH26 Aaron Parks 1HH27 Andres Castillo 1HH28 Andrew Hill 1HH29 Bill Stewart 1HH30 Brian Ritter 1HH31 Cecil Taylor 1HH32 Charles Lloyd 1HH33 Chet Baker 1HH34 Chucho Valdez 1HH35 Danilo Perez 1HH36 Dave Miller 1HH37 Dennis Luxion 1HH38 Eric Alexander 1HH39 Horace Silver 1HH40 Jacky Terrasson 1HH41 Jim Baker 1HH42 Joe Henderson 1HH43 Keith Hall 1HH44 Kenny Kirkland 1

325 Excerpt: Coleman Hawkins, Body and Soul

Perf./No. Name Freq Perf./No. Name FreqCH1 Lester Young 23 CH45 Keefe Jackson 1CH2 Ben Webster 12 CH46 Kenny Poole 1CH3 Sonny Rollins 10 CH47 Lena Horne 1CH4 Charlie Parker 7 CH48 Lester Brown 1CH5 Dexter Gordon 7 CH49 Lin Halliday 1CH6 Johnny Hodges 6 CH50 Matt Wilson 1CH7 ng 6 CH51 Pablo Casals 1CH8 Count Basie 5 CH52 Red Mitchell 1CH9 Stan Getz 5 CH53 Rich Moore 1CH10 Billie Holiday 3 CH54 Rick Falato 1CH11 Coleman Hawkins 3 CH55 Ron Perrillo 1CH12 Don Byas 3 CH56 Scott Mason 1CH13 Duke Ellington 3 CH57 Tim Haldeman 1CH14 Franz Jackson 3CH15 John Coltrane 3CH16 Leon "Chu" Berry 3CH17 Paul Gonsalves 3CH18 Charles Mingus 2CH19 Eddie Johnson 2CH20 Fletcher Henderson 2CH21 Jimmy Blanton 2CH22 Joe Lovano 2CH23 Louis Armstrong 2CH24 Lucky Thompson 2CH25 Von Freeman 2CH26 Albert Ayler 1CH27 Art Blakey 1CH28 Art Tatum 1CH29 Ben Jansson 1CH30 Benny Golson 1CH31 Benny Goodman 1CH32 Bill O'Connell 1CH33 Bud Powell 1CH34 Chris Cheek 1CH35 Dave Todd 1CH36 David Sanchez 1CH37 Eddie "Lockjaw" Davis 1CH38 Fred Anderson 1CH39 George Garzone 1CH40 Hattush Alexander 1CH41 Herschel Evans 1CH42 James Moody 1CH43 Jimmy Dorsey 1CH44 Jimmy Hamilton 1

326 Excerpt: Billie Holiday, God Bless the Child

Perf./No. Name Freq Perf./No. Name FreqBH1 Ella Fitzgerald 24 BH45 Lee Rothenberg 1BH2 Lester Young 20 BH46 Lena Horne 1BH3 Sarah Vaughn 14 BH47 Liz Johnson 1BH4 Carmen McRae 5 BH48 Maria Schneider 1BH5 Louis Armstrong 5 BH49 Mike Molloy 1BH6 Madeline Peyroux 5 BH50 Patricia Barber 1BH7 Billie Holiday 4 BH51 Paula Greer 1BH8 Dinah Washington 4 BH52 Red Mitchell 1BH9 Miles Davis 4 BH53 Rod Phasouk 1BH10 Charlie Parker 3 BH54 Rose Colella 1BH11 Count Basie 3 BH55 Stevie Wonder 1BH12 Duke Ellington 3 BH56 Susanna McCorkle 1BH13 Nancy Wilson 3 BH57 Teddy Thomas 1BH14 ng 3 BH58 Tony Bennett 1BH15 Sonny Rollins 3 BH59 Tori Amos 1BH16 Teddy Wilson 3BH17 Bessie Smith 2BH18 Blossom Dearie 2BH19 Dianne Reaves 2BH20 Nina Simone 2BH21 Abbey Lincoln 1BH22 Amy Winehouse 1BH23 Aretha Franklin 1BH24 Astrud Gilberto 1BH25 Ben Webster 1BH26 Cassandra Wilson 1BH27 Charlie Shavers 1BH28 Chet Baker 1BH29 Coleman Hawkins 1BH30 Dee Dee Bridgewater 1BH31 Diana Krall 1BH32 Diana Ross 1BH33 Dizzy Gillespie 1BH34 Earma Thompson 1BH35 Erin McDougald 1BH36 Frank Sinatra 1BH37 Hinda Hoffman 1BH38 Jim Hall 1BH39 Joanna Newsom 1BH40 Joni Mitchell 1BH41 Josh Berman 1BH42 Karen Dalton 1BH43 Kenny Clarke 1BH44 Kim Gordon 1

327 Excerpt: Charles Mingus, Fables of Faubus

Perf./No. Name Freq Perf./No. Name FreqCM1 Paul Chambers 11 CM45 Fred Hopkins 1CM2 Ray Brown 11 CM46 Gabe Noel 1CM3 Oscar Pettiford 7 CM47 Henry Grimes 1CM4 Ron Carter 7 CM48 James Merenda 1CM5 Dannie Richmond 6 CM49 Jimmy Knepper 1CM6 Sam Jones 6 CM50 John Tate 1CM7 Charles Mingus 5 CM51 Jon Dann 1CM8 Eric Dolphy 5 CM52 Karl Seigfried 1CM9 Jimmy Blanton 5 CM53 Kent Kessler 1CM10 Charlie Haden 4 CM54 Larry Gray 1CM11 Dave Holland 4 CM55 Larry Kohut 1CM12 Jimmy Garrison 4 CM56 Lorin Cohen 1CM13 Wilbur Ware 4 CM57 Matt Ulery 1CM14 Dennis Carroll 3 CM58 Mike Holstein 1CM15 Duke Ellington 3 CM59 Nat Hentoff 1CM16 Eddie Gomez 3 CM60 Ornette Coleman 1CM17 Josh Abrams 3 CM61 Patrick Mulcahy 1CM18 Scott LaFaro 3 CM62 Paul Motian 1CM19 Avishai Cohen 2 CM63 Percy Heath 1CM20 Gary Peacock 2 CM64 Reggie Workman 1CM21 Jaco Pastorius 2 CM65 Rodney Whittaker 1CM22 Max Roach 2 CM66 Rufus Reid 1CM23 ng 2 CM67 Scott Colley 1CM24 Richard Davis 2 CM68 Sean Parsons 1CM25 Slam Stewart 2 CM69 Tyler Mitchell 1CM26 Ted Curson 2CM27 Aaron Tully 1CM28 Amalie Smith 1CM29 Ben Street 1CM30 Bob Brookmeyer 1CM31 Bob Moses 1CM32 Booker Irving 1CM33 Brian Doherty 1CM34 Brian Ritter 1CM35 Butch Warren 1CM36 Charlie Parker 1CM37 Chet Baker 1CM38 Chris Potter 1CM39 Christian McBride 1CM40 Clark Sommers 1CM41 Clark Terry 1CM42 Cory Biggerstaff 1CM43 Dan DeLorenzo 1CM44 Dan Friedman 1

328 Excerpt: Thelonious Monk, ‘Round Midnight

Perf./No. Name Freq Perf./No. Name FreqTM1 Art Tatum 9 TM45 Eddie Harris 1TM2 John Coltrane 9 TM46 Erroll Garner 1TM3 Charlie Rouse 8 TM47 Fats Waller 1TM4 Duke Ellington 6 TM48 George Gershwin 1TM5 Ron Perrillo 6 TM49 Geri Allen 1TM6 Chick Corea 5 TM50 Hermeto Pascoal 1TM7 Miles Davis 5 TM51 Horace Parlan 1TM8 Bud Powell 4 TM52 Horace Silver 1TM9 Charles Mingus 4 TM53 Irene Schweitzer 1TM10 Johnny Griffin 4 TM54 Jacky Terrasson 1TM11 Thelonious Monk 4 TM55 Jason Moran 1TM12 Bob Dogan 3 TM56 Jeff Parker 1TM13 Brad Mehldau 3 TM57 Joan Hickey 1TM14 James P. Johnson 3 TM58 Jodie Christian 1TM15 Steve Lacy 3 TM59 Joe Pass 1TM16 Alex Von Schlippenbach 2 TM60 John Ore 1TM17 Bill Frisell 2 TM61 Kenny Barron 1TM18 Cecil Taylor 2 TM62 Lin Halliday 1TM19 Dexter Gordon 2 TM63 Mal Waldron 1TM20 Herbie Nichols 2 TM64 Matt Shipp 1TM21 Jaki Byard 2 TM65 Kathy Kelly 1TM22 Keith Jarrett 2 TM66 Morton Feldman 1TM23 Marcus Roberts 2 TM67 ng 1TM24 Misha Mengelberg 2 TM68 Paul Bley 1TM25 Oscar Peterson 2 TM69 Paul Giallorenzo 1TM26 Phineas Newborn 2 TM70 Pierre Walker 1TM27 Rob Clearfield 2 TM71 Red Garland 1TM28 Roy Haynes 2 TM72 Ruben Gonzalez 1TM29 Wynton Kelly 2 TM73 Sergei Prokofiev 1TM30 Aaron Goldberg 1 TM74 Shadow Wilson 1TM31 Andrew Hill 1 TM75 Stan Tracey 1TM32 Anthony Braxton 1 TM76 Stefon Harris 1TM33 Anthony Coleman 1 TM77 Steve Million 1TM34 Anton Denner 1 TM78 Willie "The Lion" Smith 1TM35 Barry Harris 1TM36 Benny Green 1TM37 Bobby Broom 1TM38 Bobby McFerrin 1TM39 Brian O'Hern 1TM40 Charlie Parker 1TM41 Coleman Hawkins 1TM42 Cyrus Chestnut 1TM43 Danilo Perez 1TM44 Doug Hayes 1

329 Excerpt: West Montgomery, Four on Six

Perf./No. Name Freq Perf./No. Name FreqWM1 Grant Green 18 WM45 Matt Schneider 1WM2 Bobby Broom 11 WM46 Maz Roach 1WM3 Jim Hall 9 WM47 Mel Rhyne 1WM4 Charlie Christian 8 WM48 Milt Jackson 1WM5 Jeff Parker 7 WM49 Pat Metheny 1WM6 George Benson 6 WM50 Pat Fleming 1WM7 Wes Montgomery 6 WM51 Peter Bernstein 1WM8 Kenny Burrell 5 WM52 Philly Joe Jones 1WM9 Pat Martino 5 WM53 Sam Macy 1WM10 Dave Miller 4 WM54 Scott Hesse 1WM11 ng 4 WM55 Sonny Rollins 1WM12 Barney Kessel 3 WM56 Tal Farlow 1WM13 Jimmy Cobb 3 WM57 The Beatles 1WM14 Joe Pass 3 WM58 Tim Haden 1WM15 John Coltrane 3 WM59 Tom Allen 1WM16 Wynton Kelly 3 WM60 Tommy Flanagan 1WM17 Alejandro Urzagaste 2 WM61 Von Freeman 1WM18 Andy Brown 2WM19 Dan Friedman 2WM20 Herb Ellis 2WM21 John Scofield 2WM22 Johnny Griffin 2WM23 Kyle Asche 2WM24 Mike Allemana 2WM25 Miles Davis 2WM26 Russel Malone 2WM27 Anthony Bracco 1WM28 Bill Evans 1WM29 Billy Bauer 1WM30 Bob Palmieri 1WM31 Charlie Parker 1WM32 Chick Corea 1WM33 Dan Effland 1WM34 David Baker 1WM35 Elvin Jones 1WM36 Fred Lonberg-Holm 1WM37 Freddie Hubbard 1WM38 Henry Johnson 1WM39 Jean "Django" Reinhardt 1WM40 Jimmy Raney 1WM41 John McLean 1WM42 John Smillie 1WM43 John Zilesko 1WM44 Kurt Rosenwinkel 1

330 Excerpt: Charlie Parker, Now’s the Time

Perf./No. Name Freq Perf./No. Name FreqCP1 Dizzy Gillespie 17 CP45 Jodie Christian 1CP2 Sonny Stitt 15 CP46 Joe Henderson 1CP3 Charlie Parker 8 CP47 John Crawford 1CP4 Max Roach 8 CP48 Keefe Jackson 1CP5 Bud Powell 6 CP49 Keith Jarrett 1CP6 Miles Davis 6 CP50 Kenny Clarke 1CP7 Ornette Coleman 5 CP51 Kids at Manhattan School of Music 1CP8 Cannonball Adderley 4 CP52 King Pleasure 1CP9 Lester Young 4 CP53 Lee Konitz 1CP10 Thelonious Monk 4 CP54 Lee Morgan 1CP11 Caroline Davis 3 CP55 Lennie Tristano 1CP12 Charles McPherson 3 CP56 Louis Armstrong 1CP13 Greg Ward 3 CP57 Mike LeBrun 1CP14 Art Pepper 2 CP58 Oliver Nelson 1CP15 Barry Harris 2 CP59 Oscar Pettiford 1CP16 Jackie McLean 2 CP60 Paquito D'Rivera 1CP17 Johnny Hodges 2 CP61 Pat Mallinger 1CP18 Kenny Garrett 2 CP62 Ray Brown 1CP19 Lou Donaldson 2 CP63 Rob Clearfield 1CP20 Mike Smith 2 CP64 Sam "Lightnin" Hopkins 1CP21 ng 2 CP65 Sonny Rollins 1CP22 Paul Chambers 2 CP66 Teddy Kotick 1CP23 Phil Woods 2 CP67 Wynton Marsalis 1CP24 Taku Akiyami 2CP25 Tommy Potter 2CP26 Von Freeman 2CP27 Al Haig 1CP28 Art Blakey 1CP29 Benny Golson 1CP30 Bobby Broom 1CP31 Charles Mingus 1CP32 Chris McBride 1CP33 Chris Potter 1CP34 Coleman Hawkins 1CP35 Count Basie 1CP36 Dave Douglas 1CP37 Dean Benedetti 1CP38 Dennis Carroll 1CP39 Dexter Gordon 1CP40 Dick Oatts 1CP41 Jake Vinsel 1CP42 James Moody 1CP43 Jimmy Ford 1CP44 Jimmy Hamilton 1

331 Excerpt: Jaco Pastorius, Continuum

Perf./No. Name Freq Perf./No. Name FreqJP1 Joe Zawinul 14 JP45 Jean-Luc Ponty 1JP2 Wayne Shorter 14 JP46 Jeff Berlin 1JP3 Herbie Hancock 8 JP47 Jimi Hendrix 1JP4 Pat Metheny 8 JP48 John Scofield 1JP5 Chick Corea 5 JP49 Joni Mitchell 1JP6 John Patitucci 5 JP50 Josh Ramos 1JP7 Miles Davis 5 JP51 Kelly Sill 1JP8 Jaco Pastorius 4 JP52 Larry Kohut 1JP9 Marcus Miller 4 JP53 Lorin Cohen 1JP10 ng 4 JP54 Mark Egan 1JP11 Ron Perrillo 4 JP55 Mat Lux 1JP12 Victor Wooten 4 JP56 Matt Garrison 1JP13 Bob Moses 3 JP57 Mozart 1JP14 Bryan Doherty 3 JP58 Nick West 1JP15 Charles Mingus 3 JP59 Oteil Burbridge 1JP16 Dave Holland 3 JP60 Richard Winkelmann 1JP17 Dennis Carroll 3 JP61 Rufus Reid 1JP18 Stanley Clarke 3 JP62 Smokin' Joe 1JP19 Charlie Parker 2 JP63 Steve Vai 1JP20 Jimmy Haslip 2 JP64 Tim Haden 1JP21 Josh Shapiro 2 JP65 Tim Ipsen 1JP22 Michael Brecker 2 JP66 Tim Lincoln 1JP23 Patrick Mulcahy 2 JP67 Tim Seisser 1JP24 Richard Bona 2JP25 Steve Swallow 2JP26 Aaron Tully 1JP27 Airto Moreira 1JP28 Al Di Meola 1JP29 Amalie Smith 1JP30 Billy Dickens 1JP31 Christian McBride 1JP32 Clark Sommers 1JP33 Connie Grauer 1JP34 Drew Gress 1JP35 Duane Stuermer 1JP36 Eberhard Weber 1JP37 Garrett McGinn 1JP38 Hermeto Pascoal 1JP39 Ira Sullivan 1JP40 Jack DeJohnette 1JP41 Jan Garbarek 1JP42 Jan Hammer 1JP43 Janek Gwizdala 1JP44 Jason Steele 1

332 Excerpt: Max Roach, Freedom Day

Perf./No. Name Freq Perf./No. Name FreqMR1 Art Blakey 13 MR45 Keith Hall 1MR2 Max Roach 13 MR46 Kenny Clarke 1MR3 Elvin Jones 11 MR47 Louis Hayes 1MR4 Philly Joe Jones 11 MR48 Marshall Thompson 1MR5 Tony Williams 10 MR49 Matt Wilson 1MR6 Buddy Rich 6 MR50 Max Krukoff 1MR7 George Fludas 5 MR51 Michael Zerang 1MR8 Mikel Avery 4 MR52 Miles Davis 1MR9 ng 4 MR53 Milford Graves 1MR10 Roy Haynes 4 MR54 Nasheet Waits 1MR11 Billy Higgins 3 MR55 Otis Ray Appleton 1MR12 Joel Spencer 3 MR56 Quin Kirchner 1MR13 Billy Cobham 2 MR57 Rashied Ali 1MR14 Brian Blade 2 MR58 Sharif Zaben 1MR15 Charlie Parker 2 MR59 Simon Lott 1MR16 Clifford Brown 2 MR60 Sonny Murray 1MR17 Frank Rosaly 2 MR61 Sonny Rollins 1MR18 Freddie Hubbard 2 MR62 Vance Okraszweski 1MR19 Gene Krupa 2 MR63 Victor Lewis 1MR20 Jack DeJohnette 2 MR64 Walter Perkins 1MR21 Ted Sirota 2 MR65 Wynton Marsalis 1MR22 Tim Daisy 2MR23 Wayne Shorter 2MR24 Ed Breazeale 2MR25 Art Taylor 2MR26 Alan Dawson 1MR27 Booker Little 1MR28 Branford Marsalis 1MR29 Brian Ritter 1MR30 Carl Allen 1MR31 Cecil Taylor 1MR32 Charles Rumback 1MR33 Curtis Fuller 1MR34 Dana Hall 1MR35 Dennis Carroll 1MR36 Ed Blackwell 1MR37 Every Drummer that came after him 1MR38 Gary Shandling 1MR39 Gerrick King 1MR40 Jazz Messengers 1MR41 Jim Black 1MR42 Jimmy Cobb 1MR43 John Smillie 1MR44 Jon Wert 1

333 Excerpt: Sonny Rollins, Without a Song

Perf./No. Name Freq Perf./No. Name FreqSR1 Jim Hall 11 SR45 Eddie Bayard 1SR2 John Coltrane 11 SR46 Ella Fitzgerald 1SR3 Bobby Broom 8 SR47 Elvin Jones 1SR4 Coleman Hawkins 7 SR48 Franz Jackson 1SR5 Hank Mobley 5 SR49 Gato Barbieri 1SR6 Bob Cranshaw 4 SR50 Gene Ammons 1SR7 Joe Lovano 4 SR51 Geof Bradfield 1SR8 ng 4 SR52 Greg Cohen 1SR9 Sonny Stitt 4 SR53 Greg Ward 1SR10 Stan Getz 4 SR54 Gunther Schuller 1SR11 Charlie Parker 3 SR55 Horace Silver 1SR12 Dexter Gordon 3 SR56 Jeff Parker 1SR13 Lester Young 3 SR57 Jerry Bergonzi 1SR14 Miles Davis 3 SR58 Joe Henderson 1SR15 Ron Dewar 3 SR59 Johnny Griffin 1SR16 Sonny Rollins 3 SR60 Joshua Redman 1SR17 Branford Marsalis 2 SR61 Kobie Watkins 1SR18 Dennis Carroll 2 SR62 Matt Schneider 1SR19 Eddie Harris 2 SR63 Max Roach 1SR20 Harold Land 2 SR64 Michael Brecker 1SR21 Keefe Jackson 2 SR65 Mike LeBrun 1SR22 Lee Konitz 2 SR66 Ornette Coleman 1SR23 Lin Halliday 2 SR67 Pat Mallinger 1SR24 Scott Burns 2 SR68 Paul Chambers 1SR25 Tim Haldeman 2 SR69 Pete La Roca 1SR26 Wayne Shorter 2 SR70 Philly Joe Jones 1SR27 Aaron Krueger 1 SR71 Ralph Bowen 1SR28 Art Farmer 1 SR72 Ray Brown 1SR29 Ben Riley 1 SR73 Rob Haight 1SR30 Ben Webster 1 SR74 Sam Jones 1SR31 Bill Stewart 1 SR75 Sam Macy 1SR32 Bob Mintzer 1 SR76 Steve Grossman 1SR33 Bob Perna 1 SR77 Steve Swallow 1SR34 Brian Ritter 1 SR78 Von Freeman 1SR35 Cannonball Adderley 1 SR79 Wes Montgomery 1SR36 Charles Lloyd 1SR37 Charlie Persip 1SR38 Chris McBride 1SR39 Clifford Brown 1SR40 Dave Rempis 1SR41 David Murray 1SR42 Dewey Redman 1SR43 Don Byas 1SR44 Doug Stone 1