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JOURNAL OF CLINICAL AND EXPERIMENTAL NEUROPSYCHOLOGY2011, 33 (4), 456–470

A multiperspective approach to the conceptualizationof executive functions

Sonia Packwood1, Helen M. Hodgetts1,2, and Sébastien Tremblay1

1School of Psychology, Université Laval, Quebec, QC, Canada2School of Psychology, Cardiff University, Cardiff, UK

The concept of executive function (EF) is deemed unclear and difficult to operationalize. We use a multiperspectiveapproach to quantify and reduce the current proliferation of EFs. A literature review of 60 studies identified 68 sub-components of EF: Through objective statistical techniques, these terms were reduced to 18 by removing semanticoverlap (using latent semantic analysis) and psychometric overlap (using hierarchical cluster analysis). However,still such a large number of functions lacks parsimony. We therefore revisit the concept of EF and suggest that the

many proposed subcomponents are not functions per se but rather a number of task-specific behaviors.

Keywords: Executive function; Latent semantic analysis; Hierarchical cluster analysis; Neuropsychological assess-ment; Cognitive processes.

Executive functions (EFs) are generally recognized ascognitive control mechanisms that direct and coordinatehuman behavior in an adaptive way when no preestab-lished schema of action is available (e.g., Lezak, 1995;Shallice, 1988). Despite the general acceptance that such

high-level functions play a role in cognition, there isno consensus as to what these functions are, how theymight be organized, or which specific test should beused in the assessment of each one. Abundant research,together with the wide use of EF concepts and execu-tive tests in clinical neuropsychology, has contributed toan extensive list of EFs including such functions as goalformation, planning, set shifting, verbal fluency, and inhi-bition. Indeed the lack of a formal definition of EFs mayhave led to some overlap and redundancy in the numberof terms used, but even attempts to gain a more coher-ent structure through factorial analysis have failed to findany consistency across studies in the type or number of

functions involved (e.g., Fisk & Sharp, 2004; Huizinga,Dolan, & van der Molen, 2006; Miyake et al., 2000).

Helen M. Hodgetts is an honorary research fellow at School of Psychology, Cardiff University. This research was supported by anoperating grant to Sébastien Tremblay and a graduate scholarship to Sonia Packwood from the Natural Sciences and EngineeringResearch Council of Canada (NSERC). Part of this work was presented at the International Congress of Psychology, Berlin, Germany(July, 2008). We would like to thank Daniel Lafond and Jean-François Gagnon for their significant help and suggestions with regard tothe ideas proposed in this paper. We would also like to thank Cindy Chamberland for comments on an earlier draft and to Marie-JoséeCôte for assistance with the analysis.

Address correspondence to Sonia Packwood or Sébastien Tremblay, École de Psychologie, Université Laval, Québec, G1V 0A6,Canada (E-mail: [email protected] or [email protected]).

The diversity of taxonomies and general absence of con-sensus have led to a proliferation of EFs. Since scienceis guided by the principle of parsimony—whereby onlythe minimum of elementary causes is used to explain aphenomenon—it would seem reasonable to seek greater

unity within the field (Banich, 2009; Uttal, 2001). Thecurrent paper uses a multiperspective approach to firstquantify the extent of the proliferation and then to esti-mate the extent to which EF subcomponents overlap bothconceptually and psychometrically. This meta-analysiswill provide an objective portrayal of the current stateof affairs with regard to EF and aims towards a clearerunderstanding of the concept.

Some of the earliest models to incorporate the idea of a higher order management system proposed a unitarymechanism responsible for all processes involving atten-tional control (Baddeley & Hitch, 1974; Grafman, 1989;Norman & Shallice, 1986; Pribram, 1960). However,

this view of a single executive entity—often referredto as the homunculus—has been criticized for lacking

© 2011 Psychology Press, an imprint of the Taylor & Francis Group, an Informa businesshttp://www.psypress.com/jcen DOI: 10.1080/13803395.2010.533157



CONCEPTUALIZATION OF EXECUTIVE FUNCTIONS 457

specificity, leading subsequent research efforts to focuson decomposing the proposed “black box” into moreinformative subcomponents (see Baddeley, 1996; Shallice,2002). Much of this work has relied upon patients withfrontal lobe damage, for whom the higher level controlabilities associated with EF—broadly the coordination,execution, and regulation of behaviors—are noticeably

impaired. That patients deficient in these everyday func-tions tend to have damage to the same area indicatesa common mechanism, but the pattern of performancedeficits between patients and across tasks is not uniformand instead seems to support the existence of multipledissociable executive skills. Despite copious research, thecomplex nature of neuropsychological impairment meansthat it is difficult to determine precisely how many funda-mentally distinct executive abilities there are (see Andrés,2003). Different models have suggested that the func-tions of the frontal lobe can be divided into three (Fuster,1980), four (Baddeley, 1996; Luria, 1973), or five sub-components (Shallice & Burgess, 1991; Stuss & Benson,

1986), and more recent factor analyses have producedsimilarly inconsistent results with models proposing upto six EFs (e.g., Fisk & Sharp, 2004; Floyd, Bergeron, &Hamilton, 2004; Huizinga et al., 2006). Disagreementabout the taxonomies of EF extends beyond just the num-ber, to also the critical roles proposed for each; althoughthere are some commonalities, it seems that there is noone subcomponent that is shared by all models (seeFournier-Vicente, Larigauderie, & Gaonac’h, 2008; Hull,Martin, Beier, Lane, & Hamilton, 2008; Miyake et al.,2000). Jurado and Rosselli (2007) reported a review of 11papers published between 1974 and 2004 that identifiesmore than 30 executive subcomponents. In sum, the cur-

rent fractionation of the central executive is unclear andperhaps of little more help than the original black boxitself (see Banich, 2009; Logan, 2003).

As well as inconsistency regarding the core structure of the central executive, the numerous terms usedto describeoften seemingly similar functions further obfuscate theconcept of EF. In a large number of studies over the lasttwo decades or so, factorial analysis has been a privilegedtool in the attempt to gain a more coherent structure of EF; however, due to different researchers’ opinions on theprocesses underlying performance on different tasks, theresults of such studies may even compound the problemof proliferation by providing labels for factors that vary

from one author to another (Séguin & Zelazo, 2005). Forexample, it is difficult to see how the factor of “visual pro-cessing” in one factor analysis (Floyd et al., 2004) couldbe considered conceptually distinct from that of “visu-ospatial storage-and-processing coordination” in another(Fournier-Vicente et al., 2008). Until researchers becomemore uniform in their terminology, it will be difficult tocompare between studies and to identify core, separableunderlying functions.

The problem of proliferation is further exacerbatedby the variety of tasks available to measure differentfacets of EF. With no formal definitions, clinicians tendto use their own labels to express the functions that a

neuropsychological task measures (Royall et al., 2002);furthermore, with the development of various new tasks

there follows an increasing number of terms to describethe potential processes tapped by each. Generally, anytest sensitive to frontal lobe damage is deemed to assess“executive” processes, and some of those more com-monly used include the Tower of Hanoi (TOH) orTower of London (TOL), verbal fluency, Stroop, andthe Wisconsin Card Sorting task (WCST). A problem of

task impurity means that each task may assess a num-ber of different executive subcomponents and/or non-EFcognitive processes as well; as such, it can be very dif-ficult to know how impairment on a particular taskshould be interpreted. For example, verbal fluency canbe described as a measure of fluency capacities (Baldo,Shimamura, Delis, Kramer, & Kaplan, 2001), memoryretrieval (Rosen & Engle, 1997), set shifting (Troyer,Moscovitch, & Winocur, 1997), or inhibition (Brosnanet al., 2002). Similarly, the Stroop task is mostly con-sidered a measure of inhibitory function (Miyake et al.,2000) but is also used to evaluate working memory (Kane& Engle, 2003), cognitive flexibility (Zalonis et al., 2009),

impulse control (Peterson et al., 1999), selective atten-tion (Melcher & Gruber, 2006), concentration (Van Diest,Stegen, Van de Woestijne, Schippers, & Van den Bergh,2000), and so on. Undoubtedly the range of tasks avail-able, as well as the lack of specificity regarding what eachtask does and does not measure, has contributed to themultiplicity of terms.

The ambiguity surrounding the concept of EF is aproblem for clinical diagnosis such that the more tax-onomies we have, the less clear the executive profile isfor each given disorder. For example, while theories sug-gest that EFs are at the heart of the difficulties associatedwith attention-deficit/hyperactivity disorder (ADHD),

five recent studies show inconsistent executive profilesespecially with regard to inhibition, verbal fluency, andplanning deficits (e.g., Boonstra, Oosterlaan, Sergeant,& Buitelaar, 2005; Geurts, Verté, Oosterlaan, Roeyers, &Sergeant, 2005; Marzocchi et al., 2008; Pasini, Paloscia,Alessandrelli, Porfirio, & Curatolo, 2007; Willcutt, Doyle,Nigg, Faraone, & Pennington, 2005). Given the plethoraof executive tasks, it is perhaps not surprising that apatient’s performance can differ between various tasksthat purport to measure the same thing and also betweenpatients who have similar diagnoses (Andrés, 2003).Greater unity in the field of EFs will be critical tounderstanding the underlying basis of neuropsycholog-

ical impairment. Neuropsychologists should be able todiscriminate between each EF (deficits of planning, inhi-bition, fluency, etc) in order to provide accurate diagnosisand treatment. A major consequence of the wide varietyof subcomponents of EF is that the profile of neu-ropathology is hard to determine with certainty, and sodiagnoses and treatment may lack specificity and unifor-mity between clinicians.

The current paper aims to quantify the extent of theproliferation that we are faced with and to seek somecoherence within the literature with regard to definitionsand the tasks used to measure each EF. Our multiper-spective approach uses three phases: a targeted review

of the literature, latent semantic analysis (LSA), andhierarchical cluster analysis (HCA). The literature review,



458 PACKWOOD, HODGETTS, TREMBLAY

for which we select a large number of well-cited articles,will reveal an estimate of the number of tasks and thenumber of different terms used in the field. LSA is a novelapplication of an approach borrowed from social sciencesand the study of language: Given that there may be over-lap and redundancy with regard to some terms obtainedin the review, it provides a first filter to guard against an

overestimation by combining semantically similar items.HCA will then be used to reduce the number of EFsstill further: Those individual subcomponents that aremeasured using the same types of tasks will be groupedtogether, since these are likely to involve the same mecha-nisms and thus represent the same underlying function.HCA is a well-established method of data explorationused in various fields such as social sciences, archeol-ogy, and biology to cluster together common themes, but,to our knowledge, it has never before been applied tothe concept of EF. Our novel three-phase methodologywill provide an objective estimate of the multiplicity of terms and employs specific techniques to identify com-

monalities between studies and thus reduce the numberof terms used.

GENERAL METHOD

Literature review

The purpose of this systematic review was to synthesizeprior research regarding the number of EFs in currentuse, as well as the executive tasks frequently used intheir assessment. To this end, we performed a search onthe Web of Science database, one of the largest available

databases, for published papers from 1970 up until 2007with the following terms: “executive function,” “executivefunctioning,” or “frontal function.” This returned 1,443articles, but in order to ensure high quality we selected60 highly cited articles for this targeted review. Studieswere identified that focused on the assessment of at leastone EF with specific executive tasks, and which had beenfrequently cited given the time since publication (rangeof citations was between 10 and 1,235). Of course olderpapers had a greater opportunity for more citations toaccumulate, but we considered a minimum of 10 citationsto be an appropriate threshold so that recently publishedarticles were not biased against. The 60 papers selected

involved a broad range of studies, including those usingchildren, adults, and elderly people, and could be con-sidered representative of the literature on how EFs aregenerally conceptualized and measured. Each differentEF mentioned in these articles was recorded, as well asthe task(s) used to measure them (see Table 1).

Latent semantic analysis

LSA was used as a first method to estimate the strength of the semantic link between the different definitions of EFsidentified in the review. LSA is a mathematical/statistical

technique for extracting and representing words andpassages similar in meaning by analyzing large bodies

of text (Landauer & Dumais, 1997; Landauer, Foltz, &Laham, 1998). It takes into account the redundancybetween definitions associated with each subcomponentand enables the grouping of those that are similarlydefined. The computer program uses singular valuedecomposition, a general form of factor analysis, to con-dense a very large matrix of word-by-context data accord-

ing to 300–500 dimensions. These dimensions representhow often a word occurs within a document (defined atthe sentence level, the paragraph level, or in larger sec-tions of texts), and each word, sentence, or text becomesa weighted vector. LSA takes into account the trackingof words that are semantically similar, but may not berelated morphologically—for example, the word mouse

has a higher LSA score when compared to cat thanwhen compared to either dog or house. Those items thatare strongly connected are grouped together to avoidan overestimation of the proliferation, thus leaving onlythose terms that are considered conceptually distinct. Thesimilarity between the resulting vectors for words and

contexts, as measured by the cosine of their containedangle, has shown to closely mimic human judgments of meaning similarity. For example, after practicing withabout 2,000 pages of English text, the program scoredas well as the average test-takers of the synonym por-tion of the Educational Testing Service’s Test of Englishas a Foreign Language (TOEFL; Landauer & Dumais,1997), and after training on an introductory psychologytextbook it achieved a passing score on a multiple-choiceexam (Landauer et al., 1998).

In order to be entered into the program, each termneeded a definition. Given that a number of terms werenot specifically defined by the authors, the missing defini-

tions were replaced by those offered by the online Websterdictionary (Parker, 2009), recognized as one of the widestdictionaries of modern language usage. This correspondsto the equivalent of 500 encyclopedias and was thuschosen for its impressive bank of available terminolo-gies. Examples of definitions provided by the dictionaryinclude perseveration as the inability to switch; creativity

as constructing a novelty without constituent compo-nents; abstraction as the process of formulating generalconcepts by abstracting common properties of instances;and problem-solving as a learning situation involvingmore than one alternative from which a selection is made.

A matrix comparison was used to compare the similar-

ity of multiple definitions within a particular LSA space,where the LSA space is defined as “a semantic space rep-resenting a mathematical representation of a large bodyof text” (Landauer et al., 1998). This space contained thetext from three college level psychology textbooks witheach paragraph used as a document, totaling 13,902 doc-uments and 30,119 unique terms. Each definition wascompared to all other definitions. The LSA system com-puted a similarity score between –1 and 1 for each sub-mitted definition compared to all submitted definitions.Identical passages in meaning were given cosines of 1,unrelated ones, 0, and those of opposite meaning, –1.Definitions of subcomponents of EF that were strongly

semantically connected (i.e., cosine ≥ .5) were groupedtogether.




TABLE 1

References of the literature review

See the Appendix for the full references.

raeY srohtuA

6991.A,ovreS&,.J,ikkliV,.K,alohA

2002.P,nosrednA

8991.V,nosrednA

Anderson, V., Anderson, P., Northam, E.,

Jacobs, R., & Catroppa, C.

2001

Austin, M. P., Mitchell, P., Wilhelm, K.,

Parker, G., Hickie, I., Brodaty, H., et al.

1999

Baddeley, A., DellaSala, S., Papagno, C., & Spinnler, H. 1997

Barnett, R., Maruff, P., Vance, A., Luk, E. S. L.,

Costin, J., Wood, C., & Pantelis, C.

2001

Belleville, S., Rouleau, N., & Van der Linden, M. 2006

4002.G,nilhoB&,.C.K,ikcorB

Brosnan, M., Demetre, J., Hamill, S., Robson, K.,

Shepherd, H., & Cody, G.

2002

0002.A.M,zczsuL&.J,nayrB

Burgess, P. W., Alderman, N., Evans, J.,

Emslie, H., & Wilson, B. A.

1998

6991.T,ecillahS&,.W.P,ssegruB

Busch, R. M., McBride, A., Curtiss, G., &

Vanderploeg, R. D.

2005

9991.S.S.P,neerG&,.S,nonnahC

Coppin, A. K., Shumway-Cook, A., Saczynski, J. S.,

Patel, K. V., Ble, A., Ferrucci, L., & Guralnik, J. M.

2006

De Luca, C. R., Wood, S. J., Anderson, V.,

Buchanan, J. A., Proffitt, T. M., Mahony, K., &

Pantelis, C.

2003

Denney, D. R., Sworowski, L. A., & Lynch, S. G. 2005

Doyle, A. E., Wilens, T. E., Kwon, A.,

Seidman, L. J., Faraone, S. V., Fried, R., et al.

2005

Duncan, J., Johnson, R., Swales, M., & Freer, C. 1997

Espy, K. A., Kaufmann, P. M., McDiarmid, M. D., &

Glisky, M. L.

1999

5002.F,éppaH&,.N,rehsiF

Foong, J., Rozewicz, L., Quaghebeur, G.,

Davie, C. A., Kartsounis, L. D., Thompson, A. J.,

et al.

1997

Fossati, P., Amar, G., Raoux, N., Ergis, A. M.,

& Allilaire, J. F.

1999

Fucetola, R., Seidman, L. J., Kremen, W. S.,

Faraone, S. V., Goldstein, J. M., & Tsuang, M. T.

2000

Garavan, H., Ross, T. J., Li, S. J., & Stein, E. A. 2000

Geurts, H. M., Verté, S., Oosterlaan, J.,

Roeyers, H., Hartman, C. A., Mulder, E. J., et al.

2004

Greene, J. D. W., Hodges, J. R., & Baddeley, A. D. 1995

Hughes, C., Leboyer, M., & Bouvard, M. 1997

Hutton, S. B., Puri, B. K., Duncan, L. J.,

Robbins, T. W., Barnes, T. R. E., & Joyce, E. M.

1998

raeY srohtuA

Kempton, S., Vance, A., Maruff, P., Luk, E.,

Costin, J., & Pantelis, C.

1999

4002.D.P,ozaleZ&,.A,rreK

Klenberg, L., Korkman, M., & Lahti-Nuuttila, P. 2001

Klimkeit, E. I., Mattingley, J. B., Sheppard, D. M.,

Farrow, M., & Bradshaw, J. L.

2004

5991.S.M,treblA&,.G,ehcelf aL

6991.J,otheL

Lehto, J. E., Juujärvi, P., Kooistra, L., &

Pulkkinen, L.

2003

Levin, H. S., Culhane, K. A., Hartmann, J.,

Evankovich, K., Mattson, A. J., Harward, H.,

et al.

1991

Lopez, B. R., Lincoln, A. J., Ozonoff, S., & Lai, Z. 2005

Lovejoy, D. W., Ball, J. D., Keats, M., Stutts, M. L.,Spain, E. H., Janda, L., & Janusz, J.

1999

McPherson, S., Fairbanks, L., Tiken, S.,

Cummings, J. L., & Back-Madruga, C.

2002

Mattson, S. N., Goodman, A. M., Caine, C.,

Delis, D. C., & Riley, E. P.

1999

Miyake, A., Friedman, N. P., Emerson, M. J.,

Witzki, A. H., Howerter, A., & Wager, T. D.

2000

Miyake, A., Friedman, N. P., Rettinger, D. A.,

Shah, P., & Hegarty, P.

2001

Moritz, S., Birkner, C., Kloss, M., Jahn, H.,

Hand, I., Haasen, C., & Krausz, M.

2002

Murphy, K. R., Barkley, R. A., & Bush, T. 2001

Nigg, J. T., Stavro, G., Ettenhofer, M.,

Hambrick, D. Z., Miller, T., & Henderson, J. M.

2005

9991.J,nesneJ&,.S,f f onozO

4791.E,terreP

Purcell, R., Maruff, P., Kyrios, M., & Pantelis, C. 1998

Robbins, T. W., James, M., Owen, A. M.,

Sahakian, B. J., Lawrence, A. D., McInnes, L., &

Rabbitt, P. M. A.

1998

Rowe, A. D., Bullock, P. R., Polkey, C. E., &

Morris, R. G.

2001

Salthouse, T. A., Atkinson, T. M., & Berish, D. E. 2003Scheres, A., Oosterlaan, J., Geurts, H.,

Morein-Zamir, S., Meiran, N., Schut, H., et al.

2004

5002.R.R,legnE&,.C,nilhceohcS

Sergeant, J. A., Geurts, H., & Oosterlaan, J. 2002

2891.T,ecillahS

1991.P,ssegruB&,.T,ecillahS

Welsh, M. C., Pennington, B. F., & Groisser, D. B. 1991

Willcutt, E. G., Doyle, A. E., Nigg, J. T.,

Faraone, S. V., & Pennington, B. F.

2005




Hierarchical cluster analysis

HCA is a statistical procedure that tries to identify clus-ters of relatively homogeneous variables based on selectedcharacteristics, in this case identifying the subcompo-nents of EF according to the neuropsychological taskswith which they are measured. The analysis was per-

formed with SPSS Version 16.0 using an agglomerativealgorithm that starts with each variable in a separatecluster and combines them until there is finally onlyone (Finch, 2005). A binary measure of pattern dis-tance was used on the dichotomous data, whereby everyvariable is compared with every other in order to deter-mine distance based on response pattern similarity. Thosewith the smallest cluster-to-cluster distance at each stagewere combined. Items were clustered according to Ward’smethod because it minimizes the variance between clus-ters at each step ensuring that they are as distinct aspossible (see Ward, 1963).

HCA is useful to establish a pattern regarding which

tasks are used more often than others when measuringparticular EFs and to determine the level of agreementbetween authors. We postulate that two subcomponentsconsistently measured with the same tasks would notbe functionally distinct and would therefore meet in thesame cluster of the analysis. These two subcomponentswould require similar cognitive mechanisms and thus rep-resent the same underlying function. In other words, byestablishing the similarity between the different EFs interms of the tests with which they are measured, the HCAhelps to estimate the proximity between each EF, cluster-ing together those that are closest and thus reducing theoverall number of individual functions. It tries to deter-

mine a set of tasks that would allow us to distinguish onecluster of EFs from another and helps to find a recurringpattern of tasks that would be used mostly to measure aparticular group of subcomponents.

RESULTS

In our review of 60 of the most frequently cited studies,68 different terms for EFs were identified as well as 98tasks used to assess them. We extracted only the preciseterms used by authors, and in some cases it seemed that anumber of different labels were being used for what might

essentially be a single EF—for example, inhibition, inter-ference control, mental control, and control of responseall appear to be very similar. LSA is a way to objectivelyquantify this overlap, reducing the number of terms toonly those that are conceptually distinct.

The result of the LSA suggests the presence of 50EFs rather than 68. Eighteen terms were thus includedwith one of the 50 remaining EFs because they werestrongly semantically connected (cosine ≥ .5). For exam-ple, the terms set shifting, selective attention, attentionshifting, attentional control, and cue-directed attentionwere reduced simply to set shifting. In each case, the mostcommon term was used, and the others were absorbed

by it. Figure 1 represents the five most common terms inrectangles: Planning was assessed in 48% of the studies,

working memory in 42%, set shifting in 32%, inhibitionin 42%, and fluency in 27%. Those taxonomies subsumedby these umbrella terms are connected by solid lines(cosine≥ .5). Items linked by dotted lines are less stronglyassociated (cosine .3–.5), and the 12 subcomponents rep-resented by numbered circles are those that the LSA didnot link semantically to any other term (cosine < .3).

This final grouping constitutes a better estimate of theproliferation of EFs because it takes into account asso-ciations between their definitions. This analysis allows usnot only to determine the EFs that should be groupedtogether but also to estimate the proximity between eachof them. Table 2 provides a summary of the resultsregarding which 50 components were retained, and which18 were sufficiently semantically related to be subsumedby a more common term.

An overlap remains, however, especially with regard tothe tasks used to measure EFs. If the same subcompo-nents are consistently measured by the same sets of tasks,then we might assume that these are not separate func-

tions after all, but rather individual labels provided byvarious researchers for the same EF. HCA was employedto reduce the number of EFs further, by progressivelygrouping together subcomponents according to similar-ity in the tasks with which they are measured. Unlikefactor analysis that looks at individual differences intask performance, this novel approach is based uponthe similarities/differences in authors’ opinions regard-ing what is thought to be measured by a particular task.It assesses whether a given task is used more frequentlythan another to measure a specific function, and so a pat-tern emerges regarding which tasks are associated withparticular EFs. Thus if Task A is most frequently used

to measure Function X but is also used to measureFunctions Y and Z, we might infer that these func-tions must also be associated—for example, accordingto the literature review the Tower of Hanoi is mainlyused to measure planning, but can also be used tomeasure organization and problem solving; therefore allthree functions are associated and are combined in thesame cluster.

Not all the data could be entered into the cluster analy-sis because 18 of the 50 EFs were idiosyncratic and listedonly once; that is, they were not measured by a test com-mon to any other EF and so could not be assigned to acluster. Had these 18 EFs been included, the number of

separate clusters would have been artificially increased.Table 2 indicates which EFs were eliminated at this stage(e.g., intentionality, creativity, complex integration, self-regulation), leaving just 32 EFs to be entered into theHCA. Figure 2 shows the full dendrogram from the anal-ysis. Items are combined at each level until the clustersare increasingly coarse-grained, and there is finally onlyone. If we look just at the first level of clustering, whichis the most conservative, the 32 EFs have been reduced to18. Of course, we could look at later stages of the anal-ysis to reduce the number of terms still further, but weprefer to remain cautious rather than to suggest combin-ing items at too high a level. The tasks used to measure

the subcomponents of each of these clusters are shownin Table 3. From an initial set of 68 EFs we are left




working

memory

goal management

concentration

carry out asequence of actions

activationand monitoring

goals

goalsetting

develop aplan

execute aplan

centralexecutive

controllingof actions

temporal coding

planning

efficiency to retrieve

words from memory

motor planning

executivemotor skills

1dividedattention

sequencing

executive

memory

2

STMcapacity

selective attention

shiftof attentioninhibition

set shifting

self-monitoring

self-generativebehavior

6

3

5

cognitiveflexibility

informationprocessing

perseveration

organization

self-regulation

resist todistraction

complexintegration

4

sustained

attention/vigilance

attentionnal control

cue-directedattention

interferencecontrol

responsesuppression

responsegeneration

responsemodulation

fluency

control of response

10

7

programming

impulsivity

wordgeneration

use of strategies

8

9

11

attentionnal setformation

maintainset

mental control

verbal efficiencyspontaneous

verbal formation

12

reasoningabstraction

conceptformation

conceptuali-zation

flexibility of thinking

1. Workload

2. Creativity

problemsolving

3. Theoryofmind 9. Discoveringchangesinrules

4. Visual search

5. Time sharing

6. Intentionality 12. Initiation

7. Generation of strategy

8. Proneness to interference

10. Affective decision making

11. Verification of hypothesis

Figure 1. Strength of the semantic link between definitions of executive functions (EFs) according to the latent semantic analysis (LSA).EFs represented in rectangles are the most frequently postulated in the literature review. EFs are connected by a full line (cosine of .5 ormore), connected by a dotted line (cosine between .3 and .5), or are unconnected (cosine less than .3). Note: The 12 items in the legendcorrespond to the 12 items associated with the 12 circles. To view a color version of this figure, please see the online issue of the Journal.

with 18; this is quite a considerable reduction, althoughsuch a large number of functions is still lacking inparsimony.

DISCUSSION

The concept of EF has been criticized for its lack of clar-ity and profusion of terms (e.g., Andrés, 2003; Jurado &Rosselli, 2007; Miyake et al. 2000), andin the current arti-cle we aimed to both quantify and reduce the proposednumber of executive subcomponents. A multiperspectivemethodology was used that incorporated three filters:

a targeted review of the literature incorporating highlycited EF articles, LSA, and HCA. The literature review

revealed 68 different terms and a set of 98 executivetasks. The sheer number of tasks and labels to describeEFs clearly shows the inconsistency in the literature andillustrates the need for a more coherent approach. Thenumber of terms was reduced to 50 with LSA and to18 following the HCA (or 36 if we consider the idiosyn-cratic terms that were eliminated from this analysis), buteven after three filters this still seems too large a num-ber to suggest that the problem has been adequatelyresolved. There are too many abilities, definitions, andtasks to provide any meaningful taxonomy; continuing inthis manner will not be helpful in clarifying the notion of EF, and as such we suggest that the concept is revisited.

The results of the literature review demonstrate theextent of the problem with regard to EF; since clinicians




TABLE 2

Results of the LSA: EFs retained and EFs eliminated after the analysis

EFs retained after the LSA EFs eliminated after the LSA (and terms subsumed under)

1. planning 1. goal management (planning)2. inhibition 2. interference control (inhibition)3. working memory (WM) 3. control of response (inhibition)

4. set shifting 4. mental control (inhibition)5. fluency 5. efficiency to retrieve words from memory (WM)6. cognitive flexibility 6. temporal coding (WM)7. impulsivity 7. selective attention (set shifting)8. sustained attention 8. shift of attention (set shifting)9. goal setting 9. attentional control (set shifting)

10. perseveration 10. cue-directed attention (set shifting)11. organization 11. attentional set formation (maintain set)12. concept formation 12. response modulation (fluency)13. initiation 13. response generation (fluency)14. problem solving 14. response suppression (fluency)15. generation of strategy 15. executive motor skills (motor planning)16. executive memory 16. self-monitoring (self generative behavior)17. resist to distraction 17. spontaneous verbal formation (verbal efficiency)18. sequencing 18. carry out a sequence of actions (concentration)19. reasoning20. maintain set21. information processing22.a divided attention23. use of strategies24. conceptualization25.a short-term memory26. visual search27. central executive28.a proneness to interference29.a motor planning30. develop a plan31. execute a plan32.a verification of hypothesis

33.a discovering changes in categorizing rules34.a word generation35. self-generative behavior36. controlling of actions37. verbal efficiency38.a intentionality39.a programming40.a flexibility of thinking41.a creativity42. concentration43.a complex integration44.a activation and monitoring goals45.a time sharing46.a decision making

47.a

workload48.a theory of mind49.a self-regulation50. abstraction

Note. LSA = latent semantic analysis. EF = executive function. A total of 50 EFs were retained after the LSA,and 18 EFs were eliminated. aThe executive functions that were excluded from the hierarchical cluster analysis(HCA).

often use their own terminologies based on what theyconsider a task to measure, semantically overlappingand superfluous labels are increasingly created. LSA andHCA are two novel approaches in the area of EF—one

based on semantics and the other on psychometrics—that have proven useful in our attempt to gain more

coherence amongst the multiplicity of terms. LSA andHCA combine those subcomponents that do not sharethe same label but that are defined and/or measured inthe same way across different authors. LSA provides an

innovative method to reduce the number of EFs, allowingus to quantify the overlap at the abstract level by offering




Rescaled Distance Cluster Combine

Executive Functions

0 5 10 15 20 25

Working Memory

Concentration

Strategy Generation

ConceptualizationMaintain Set

Executive Memory

Goal Setting

Central Executive

Impulsivity

Initiation

Inhibition

Visual Search

Sustained Attention

Resist Distraction

Problem Solving

Controlling Actions

Develop Plan

Execute Plan

Planningg

Organization

Use Strategies

Self Monitoring

Set Shifting

Cognitive Flexibility

Concept Formation

Abstraction

Fluency

Verbal Efficiency

Sequencing

Information Processing

PerseverationReasoningReasoning

Figure 2. Dendrogram of the cluster analysis using Ward’s method. From the left side of the figure, the first nodes represent the18 clusters of the hierarchical cluster analysis (HCA) reduced from the initial set of 32 executive functions (EFs).

a grouping of EFs based on definitions. Although intu-itively we may consider that two descriptions in the liter-ature are equivalent and that one can be subsumed by theother, this method allows us to make that decision objec-tively. We identified which of any corresponding termswas the most frequently used by authors and now makethe recommendation that these most common terminolo-

gies become more uniform in the field of EF (see Table 2for the most frequent terms highlighted in our review andthose more idiosyncratic terms that could be subsumed).A greater transparency in the labeling of subcomponentswill better facilitate comparison between studies in thisconceptually complex area.

HCA reduced the number of functions still furtherby grouping together those that were strongly associ-ated in terms of the tasks used. If two subcomponentsare consistently measured by the same types of task,we might infer that different labels have simply evolvedfor the same EF; after all, it would seem reasonableto assume that those measured by the same tasks rely

upon the same mechanisms and therefore tap a common

underlying function. Based on semantic and psychome-tric overlap we have reduced the number of terms to18, or 36 if we also consider those idiosyncratic termsthat were excluded from the HCA. However, to regardeach of these as separate functions would be unparsimo-nious given their number and also the lack of any clearoperationalization: Those terms deemed separate by the

LSA and HCA are not necessarily completely indepen-dent of one another, just associated to a lesser degree thanthose terms that the analyses combined together. Sincethe 18 (or 36) EFs derived from the analyses cannot besaid to each represent a clear functional subcomponent,it would be difficult to incorporate these into a mean-ingful or parsimonious theory of executive functioning.We therefore consider another viewpoint whereby the dif-ferent subcomponents identified do not reflect separatefunctions per se, but rather task-specific manifestationsof behaviors. Such an explanation allows for the manyvariations in executive task performance highlighted inour review, but without the need to suggest that these are

each functionally distinct components.




TABLE 3

EFs and executive tasks included in each of the 18 clusters according to the HCA

1.1 Working memory/efficiency to retrieve words from memory/temporal coding1.2 Concentration/carry out a sequence of actions

Stroop, SOPT , verbal and nonverbal fluency tasks, WCST, TOL, TMT, Delis sorting test, spatial span, digit span, CVLT,cognitive estimates test, auditory attention and response set, Porteus mazes, delayed alternation, visual search test, A not Btask, self-ordered searching task.

2.1 Generation of strategy2.2 Conceptualization2.3 Maintain set/attentional set formation

Verbal and nonverbal fluency tasks, WCST , SOPT, similarities subtest, visual discrimination task.3.1 Executive memory

Verbal and non verbal fluency tasks, CVLT, MCST.4.1 Goal setting4.2 Central executive

TOL, spatial learning task, verbal and nonverbal fluency tasks, cognitive estimates test.5.1 Impulsivity

Verbal and nonverbal fluency tasks, Stroop, TOL, WCST, selective reaching task, spatial puzzle, statue.6.1 Initiation

Verbal and nonverbal fluency tasks, COWAT, Delis sorting test, cognitive effort test, Hayling, cognitive estimates test, spatialpuzzle, list learning task.

7.1 Inhibition/interference control /control of response/mental control7.2 Visual search7.3 Sustained attention or vigilance

Matching familiar figures, selective reaching task , CPT , verbal and nonverbal fluency tasks, Stroop, WCST, TOL, SOPT, TMT,six element test, go/no go task, digit span, Hayling, arithmetic subtest, cognitive estimates test, statue, change task, A not Btask, group-embedded figures test.

8.1 Resist to distractionStroop, digit span, arithmetic subtest.

9.1 Problem solving9.2 Controlling of actions

WCST , TOL, TOH, 20 questions, delayed alternation.10.1 Develop a plan10.2 Execute a plan10.3 Planning/goal management

TOL, TOH , WCST, TMT, cognitive effort test, selective reaching task, CFR, Porteus mazes.

11.1 OrganizationTOL, COWAT, TOH, SOPT, six element test, CVLT, CFR, Porteus mazes.

12.1 Use of strategies12.2 Self-generative behavior/self-monitoring12.3 Set shifting/selective attention/shift of attention/attentional control/cue-directed attention

Verbal and nonverbal fluency tasks, WCST , COWAT , TMT , self-ordered searching task , Stroop, digit span, selective reachingtask, auditory attention and response set, MCST, CNT, ID/ED, visual search test.

13.1 Cognitive flexibilityVerbal and nonverbal fluency tasks, Stroop, WCST, COWAT, TMT, Delis sorting test, digit span, CNT, ID /ED, change task,

group-embedded figures test.14.1 Concept formation

WCST, COWAT, CVLT, similarities subtest, California word context, 20 questions.15.1 Abstraction

WCST, COWAT, Delis sorting test, 20 questions, MCST.

16.1 Fluency/response suppression/response modulation/response generation/control of response16.2 Verbal efficiency/spontaneous verbal formation

Stroop, COWAT , WCST, TMT, object usage test, go/no go task, Hayling.17.1 Sequencing17.2 Information processing

COWAT , SOPT , TMT , spatial span.18.1 Perseveration18.2 Reasoning

WCST , TMT , verbal and nonverbal fluency tasks, COWAT, list learning task, California word context.

Note. Executive tasks in italics are most frequently postulated to measure the EFs that are within that cluster. EF = execu-tive function. HCA = hierarchical cluster analysis. SOPT = Self-Ordered Pointing Task; WCST = Wisconsin Card Sorting Test;TOL = Tower of London; TMT = Trail Making Test; CVLT = California Verbal Learning Test; MCST = Modified CardSorting Test; COWAT = Controlled Oral Word Association Test; CPT = Continuous Performance Test; TOH = Tower of Hanoi;CNT = Contingency Naming Task; ID/ED = Intradimensional/Extradimensional; CFR = Complex Figure of Rey.




Although EFs are generally considered the managersof all decision-making processes, some models in cogni-tive psychology suggest that fundamental processes likememory and attention instead govern the choice of whichbehavior to adopt in a given situation (see Goldman-Rakic, 1987; Kimberg & Farah, 1993). If complex deci-sion making is controlled by just a few key functions,

then the numerous EFs that clinicians refer to might sim-ply be different behaviors that result from the interactionbetween these fundamental processes when performingdifferent tasks. Similar tasks or situations would generatesimilar behaviors because the decision-making processthat one goes through in each case would be almost thesame; for example, tasks that require the inhibition of a usual behavior are grouped together in the HCA, butinhibition is not necessarily a function, it could simplybe the manifestation of the interaction between key pro-cesses that are required to make a decision (see MacLeod,Dodd, Sheard, Wilson, & Bibi, 2003). Evidence from neu-roimaging suggests that such key functions (e.g., memory

and attention) could be located in the cortex and thatalthough specific areas might be responsible for a spe-cific type of information processing (e.g., either visualor auditory), it is the interaction of multiple parts of the cortex that are necessary to make a complex deci-sion or to solve a novel problem (see Smith & Jonides,1999). Undoubtedly if we consider the numerous sub-components as examples of executive behaviors ratherthan as executive functions, then this would represent amore parsimonious approach.

Neuroimaging studies indicate that the activity of many neurons in the prefrontal cortex—the centre of executive functioning—is task dependent (Asaad, Rainer,

& Miller, 2000); however, although a particular task mayactivate a new set of neurons, one should not make theerroneous assumption that this is then tapping into a newfunction. In basic research rather than clinical studies, theway to operationalize concepts is quite different: Two dif-ferent tasks or paradigms might activate different sets of neurons but can still be considered to elicit the same cog-nitive function. For example, the attentional blink andthe psychological refractory period (PRP) are two differ-ent paradigms, but both are assumed to perform similarcognitive operations (Jolicoeur, Dell’Acqua, & Crebolder,1998). That is, it is clear from other areas of psychol-ogy that a number of similar but distinct paradigms can

converge upon the same fundamental cognitive function.Research efforts to identify and characterize variousisolated functions only serve to further obfuscate the con-cept of EF; rather, EF could be defined as a systemresponsible for the acquisition of task context and theimplementation of rules used to guide behavior, regard-less of the specific behaviors or response required bya given task (e.g., inhibition of a prepotent response,set shifting). Such a definition would shift our opera-tionalization of EF closer to the concept of the g factorof intelligence, for which the same kind of debate hasoccurred (see Duncan, 2010; Stiers, Mennes, & Sunaert,2010). As Duncan and Owen (2000) state in their review,

although there must be an increase in the local special-ization of a function as the scale of analysis approaches

the single neuron, many of the same specific regions of the frontal lobe are activated by multiple kinds of cog-nitive demands. Like the g factor, the concept of EFhas increased in interest due to its predictive successfor many real-world activities, yet by concentrating ourwork on isolated operations such as response inhibition,we may have lost sight of the key reason why the con-

cept of EF was originally developed: to illustrate howhumans address adaptive behavior. Such adaptive behav-ior usually implies complex sequential programming of goal-directed behaviors, and thus it seems illogical toreduce this holistic concept to a multitude of isolatedfunctions. Indeed, it is unlikely that deficits of com-plex behaviors can be captured by isolating operations(e.g., response inhibition), when many different kinds of problem-solving situations require complex and multi-component behavior (Duncan, 2010).

Carpenter, Just, and Reichle (2000) also subscribeto this view, proposing that cognitive processes arisefrom networks across multiple cortical sites with inter-

active and overlapping functions; as such, each possibleinteraction could generate a different pattern of actionsor behaviors depending on the task in hand, the spe-cific situation, or even the cognitive skills that a personhas developed within a particular culture: “the varietyand generativity of human cognition, like the variationobserved in other complex adaptive systems, arises fromthe combinatorics of simpler elements” (Carpenter et al.,2000, p. 197). Thus the interaction of a number of fac-tors can give rise to a multitude of possible behaviors,and this explanation may go some way towards under-standing why we can observe so many executive deficitsyet the existence of such a large number of separate, spe-

cialized executive functions seems implausible (see Uttal,2001).A good example of a more parsimonious approach

is the model of Shallice and Burgess (1996): Instead of describing executive functioning with a multitude of sub-components, the authors describe three general stagesthat one must go through when facing a new or com-plex situation (construction of temporary new schema;implementation of temporary new schema; assessmentand verification). The output of the model is a behav-ior that emerges from these three general principles. If we consider all the EFs identified in the literature reviewas being examples of different outputs—or as behaviors

deriving from a few basic underlying functions—then thismodel would seem to be in keeping with our viewpoint of a more parsimonious representation. One further modelto provide an explanation as to how executive control canoperate as a unitary function without resorting to a mul-titude of separate EFs is the cascade model, based onfunctional imaging studies (see Koechlin & Summerfield,2007). According to this model, executive functioningdoes not comprise individual subcomponents but ratherchosen actions are dependent upon different contexts;thus, there are as many possible actions as there arecontexts in which an individual can be placed (or, torelate to our current argument, there may be as many

possible executive behaviors as there are different exec-utive tasks to perform). The anterior portion of the




prefrontal cortex is thought to support the selection of actions in multitasking contexts, whereby the individualmust keep in mind relevant information for a future taskwhilst currently engaging in another. This component isreferred to in the model as branching control and mayaccount for a number of proposed EFs across differ-ent contexts that require the maintenance and constant

updating of information whilst performing multiple tasks(e.g., planning, inhibition, working memory, cognitiveflexibility).

Limitations and future directions

The novel approaches of LSA and HCA offer a usefulperspective within the complex field of EF, but are not of course without their limitations. The LSA is constrainedby the specific semantic space available—in the case of psychology, the only semantic space available was thatof college-level text books. Ideally, an LSA space relat-

ing more specifically to EF could be useful, but given thatthe definitions used were fairly simple, we believe that thiswas adequate for our analysis to give an initial idea of thesemantic overlap. The output does show some surprises,however; for example, some EFs that one may consider tobe highly related were not combined in the analysis. Thisis because the semantic link between two EFs stronglydepends on the specific definition used, and so it is pos-sible that some associations may have been missed, thusslightly underestimating the number of terms that couldotherwise have been combined. We acknowledge that thisis a limitation of this analysis; however, we felt that usingthis approach based on objective definitions was better

than adopting more lenient criteria whereby more itemscould have been combined on the basis of author opin-ion. A further point is that we used a high cutoff pointin order to ensure that the EFs grouped together afterthe LSA were exclusively those that were strongly seman-tically linked (cosine ≥ .5). Thus, EFs like attentionalcontrol and interference control that were reasonably wellassociated (cosine= .26) were not grouped together in theanalysis. Our conservative criteria for objectively combin-ing items would explain why some seemingly similar EFsare not related in this analysis, which could be consideredan initial first step at reducing the number of terms.

The HCA allowed us to determine the extent of agree-

ment between authors regarding which EF a specific taskis thought to measure. For some tasks there appears tobe a high level of agreement, such as the ContinuousPerformance Test (CPT), which features in only one clus-ter (i.e., inhibition, visual search, and sustained attention)and so is closely tied to the measurement of one specificfunction. On the other hand, the WCST appears in twothirds of clusters in the HCA (12 of 18), thus illustrat-ing problems we have in the current EF literature: (a) thelack of agreement between authors concerning what EFis measured by a task, and/or (b) the use of this task toassess many EFs at a time. Clearly, these problems makeany performance deficit on such a task difficult to inter-

pret, and the HCA highlights which tasks may be mostproblematic in this regard.

One might argue that some of the tasks included in theHCA were not originally developed or validated as “exec-utive measures”—for example, tasks like Stroop and TrailB were first recognized as sensitive measures to differenti-ate between patients with or without brain injury (Davids,Goldenberg, & Laufer, 1957; Houston, 1969). However,they are now commonly used in the field by researchers

and neuropsychologists alike (see Miyake et al., 2000),and patients’ performance on these tasks is used to makeclinical diagnoses regarding their capacity for executivefunctioning and independent living. Although these tasksmight not have been developed directly from the currentexecutive semantic space, they are certainly widely used torefer to this concept. Since our paper is a representationof the measures and terms currently used in the litera-ture, these tasks are still deemed relevant in our presentconceptualization of EF.

The 60 papers used in the literature review were fre-quently cited so give a good indication of the current stateof play in the cognitive and neuropsychological literature

with regard to the conceptualization and measurement of EFs. As a future avenue of research it would perhaps beinteresting to compare this review to one that includesarticles from another research area such as neuroimaging,as this would allow us to compare the extent to which theoperationalization of EFs is comparable across differentdomains.

In the current paper our contribution is threefold: Wehave quantified the extent of the problem with regard toEF; we demonstrate the use of novel methods to objec-tively reduce the number of terms based on semanticand psychometric overlap; and we have suggested a needto revisit the concept of EF. Although researchers may

agree that the concept of EF is difficult to operational-ize, this paper is critical in highlighting just how difficultthis problem has become. If researchers continue in thesame manner—creating new executive tasks and usingnew labels for functions that these tasks may measure—itwill eventually become impossible to make any mean-ingful comparisons between studies and to understandone clinician’s diagnosis relative to another’s. One of themain goals of this study was to stress the theoretical andpsychometric inconsistency in this field. It is importantfor both cognitive psychology and clinical practice thatwe make a concerted effort now to move towards greatercoherence and uniformity regarding these constructs. We

recommend that more standardized rather than idiosyn-cratic terms be established within the literature, perhapsadopting those most frequent terms highlighted by theLSA. Furthermore, this paper emphasizes the need torevise our view of EFs so that content is not con-founded by form; that various types of behaviors/deficitsexhibited through the performance measures of differentexecutive tasks are just that—the expression of differentexecutive behaviors—and not necessarily a reflection of multiple separate executive “functions.”

Original manuscript received 9 November 2009

Revised manuscript accepted 10 September 2010First published online 24 January 2011




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