Cognitive Science - Kalamazoo Collegegeza.kzoo.edu/~erdi/cogsci/summ.pdf · Cognitive science is a...

Cognitive Science

Peter [email protected]

Henry R. Luce ProfessorCenter for Complex Systems Studies

Kalamazoo Collegehttp://people.kzoo.edu/ perdi/

andInstitue for Particle and Nuclear Physics, Wigner Research Centre, Hungarian Academy

of Sciences, Budapesthttp://cneuro.rmki.kfki.hu/

– Typeset by FoilTEX – 1

Content

• The Brain-Mind Problem: the philosophical approach

• Capsule History of Psychology

• Representation and Computation

• The Cognitive Revolution

• The Physical Symbol Systems Hypothesis

• Neural Networks: Biological and Artificial

• Cognitive Neuroscience: Multiple Levels - Multiple Methods

• Multiple Memory Systems

• Language acquisition, evolution and processing

• Embodied Cognition - Cognitive Robots

• Consciousness:

• Integrating Emotions and Cognitions.


The Brain-Mind Problem

• monism vs. dualism

• reductionism

• emergentism

• functionalism

• downward causation

Monism:is the theory that there isonly one

fundamental kind, categoryof thing or principle.

Dualism:

is the theory that the men-tal and the physical or mindand body or mind and brainare, in some sense, radicallydifferent kinds of thing.(Interactionist dualism fromDescartes to Popper andEccles)


The Brain-Mind Problem: the philosophical approach

ReductionismComplex things can alwaysbe reduced to (explainedby) simpler or more funda-mental things.Ontological reductionism<—> monismDenial of reductionist ideasis holism

Emergentism

The properties of complexsystems are not reducibleto those of their con-stituent elements, thoughthey could not exist with-out them. While many ofthe fundamental propertiesof matter, such as mass, areheld to be merely quanti-tative and additive, emer-gent properties are said tobe qualitative and novel ornon-predictable.


The Brain-Mind Problem

Functionalism

Functionalism is the doctrine

that what makes something a

thought, desire, pain (or any

other type of mental state)

depends not on its internal

constitution, but solely on its

function, or the role it plays,

in the cognitive system of

which it is a part. More pre-

cisely, functionalist theories

take the identity of a men-

tal state to be determined

by its causal relations to sen-

sory stimulations, other men-

tal states, and behavior.

Downward Causation

all processes at the lower level of

a hierarchy are restrained by and

act in conformity to the laws of

the higher level

Specifically: mental agents can

influence the neural functioning

”Two way causation”

The nervous system can be con-

sidered as being open to various

kinds of i nformation, and that

there would be no valid scien-

tific reason to deny the existence

of downward causation, or more

precisely, a two-way causal rela-

tionship between brain and mind

(Szentagothai)


Capsule History of Psychology

• Weber and Fechner

• Helmholtz

• Ebbinghaus

• James, William

• Freud

• Wundt

• Skinner

• Luria

• Vigotsky

• Piaget

• (Gestalt)

• Hebb

• Skinner

.

• a scientific study of human behavior

and mental processes

• first: physicians who started experi-

menting with behavior through scien-

tific methods

• memory experiments

• first experimental laboratory: Leipzig,

Germany

• behaviorism

• psychoanalysis

• constructivism


Representation and Computation

Knowledge Representation

• formal logic

• rules

• concepts

• analogies

• images

• connections

.


The cognitive revolution:a historical perspectiveGeorge A. Miller

Department of Psychology, Princeton University, 1-S-5 Green Hall, Princeton, NJ 08544, USA

Cognitive science is a child of the 1950s, the product of

a time when psychology, anthropology and linguistics

were redefining themselves and computer science and

neuroscience as disciplines were coming into existence.

Psychology could not participate in the cognitive

revolution until it had freed itself from behaviorism,

thus restoring cognition to scientific respectability. By

then, it was becoming clear in several disciplines that

the solution to some of their problems depended cru-

cially on solving problems traditionally allocated to

other disciplines. Collaboration was called for: this is a

personal account of how it came about.

Anybody can make history. Only a

great man can write it.

Oscar Wilde’s aphorism is appropriate. At the time, thesuggestion that we were making history would have beenpresumptuous. But anybody can make history; writinghistory is another matter. I know something of thescholarship required and nothing approaching it hasgone into the story I will tell here. But I offer this personalaccount in the hope that it might interest and help the realhistorians of science.

At the time it was happening I did not realize that I was,in fact, a revolutionary, and two different stories becameintertwined in my life. They unfolded concurrently but Iwill tell the psychological story first.

The cognitive revolution in psychology

The cognitive revolution in psychology was a counter-revolution. The first revolution occurred much earlierwhen a group of experimental psychologists, influenced byPavlov and other physiologists, proposed to redefinepsychology as the science of behavior. They argued thatmental events are not publicly observable. The onlyobjective evidence available is, and must be, behavioral.By changing the subject to the study of behavior,psychology could become an objective science based onscientific laws of behavior.

The behavioral revolution transformed experimentalpsychology in the US. Perception became discrimination,memory became learning, language became verbal beha-vior, intelligence became what intelligence tests test. By

the time I went to graduate school at Harvard in the early1940s the transformation was complete. I was educated tostudy behavior and I learned to translate my ideas into thenew jargon of behaviorism. As I was most interested inspeech and hearing, the translation sometimes becametricky. But one’s reputation as a scientist could depend onhow well the trick was played.

In 1951, I published Language and Communication [1],a book that grew out of four years of teaching a course atHarvard entitled ‘The Psychology of Language’. In thepreface, I wrote: ‘The bias is behavioristic – not fanaticallybehavioristic, but certainly tainted by a preference. Theredoes not seem to be a more scientific kind of bias, or, if thereis, it turns out to be behaviorism after all.’ As I read thatbook today it is eclectic, not behavioristic. A few yearslater B.F. Skinner published Verbal Behavior [2], a trulybehavioral treatment of language and communication. BySkinner’s standards, my book had little or nothing to dowith behavior.

In 1951, I apparently still hoped to gain scientificrespectability by swearing allegiance to behaviorism. Fiveyears later, inspired by such colleagues as Noam Chomskyand Jerry Bruner, I had stopped pretending to be abehaviorist. So I date the cognitive revolution in psychol-ogy to those years in the early 1950s.

Limitations of information theory

During those years I personally became frustrated in myattempts to apply Claude Shannon’s theory of informationto psychology. After some initial success I was unable toextend it beyond Shannon’s own analysis of lettersequences in written texts. The Markov processes onwhich Shannon’s analysis of language was based had thevirtue of being compatible with the stimulus–responseanalysis favored by behaviorists. But informationmeasurement is based on probabilities and increasinglythe probabilities seemed more interesting that theirlogarithmic values, and neither the probabilities northeir logarithms shed much light on the psychologicalprocesses that were responsible for them.

I was therefore ready for Chomsky’s alternative toMarkov processes. Once I understood that Shannon’sMarkov processes could not converge on natural language,I began to accept syntactic theory as a better account of thecognitive processes responsible for the structural aspectsof human language. The grammatical rules that governphrases and sentences are not behavior. They areCorresponding author: George A. Miller ([email protected]).

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mentalistic hypotheses about the cognitive processesresponsible for the verbal behaviors we observe.

The end of behaviorism

Behaviorism was an exciting adventure for experimentalpsychology but by the mid-1950s it had become apparentthat it could not succeed. As Chomsky remarked, definingpsychology as the science of behavior was like definingphysics as the science of meter reading. If scientificpsychology were to succeed, mentalistic concepts wouldhave to integrate and explain the behavioral data. We werestill reluctant to use such terms as ‘mentalism’ to describewhat was needed, so we talked about cognition instead.

Whatever we called it, the cognitive counter-revolutionin psychology brought the mind back into experimentalpsychology. I think it is important to remember that themind had never disappeared from social or clinicalpsychology. It was only experimentalists in the US whoreally believed that behaviorism would work. In myown case, when I became dissatisfied at Harvard betweenB.F. Skinner’s strict behaviorism and S.S. Stevens’psychophysics, I turned to Jerry Bruner’s social psychol-ogy, and in 1960 that led to the creation at Harvard of theCenter for Cognitive Studies. Bruner’s group at BowStreet had been calling themselves the ‘Cognition Project’for some time, so we simply changed it from a project to acenter. Bruner obtained a grant from the CarnegieCorporation of New York and Dean Bundy gave us spaceto house the enterprise. We assembled a group of brightyoung graduates and a few senior scholars who shared ourinterests. Peter Wason, Nelson Goodman and NoamChomsky had the most influence on my thinking at thattime.

Behaviorism flourished primarily in the US and thiscognitive revolution in psychology re-opened communi-cation with some distinguished psychologists abroad. InCambridge, UK, Sir Frederic Bartlett’s work on memoryand thinking had remained unaffected by behaviorism. InGeneva, Jean Piaget’s insights into the minds of childrenhad inspired a small army of followers. And in Moscow,A.R. Luria was one of the first to see the brain and mind asa whole. None of these three spent time at the Center butwe knew their work well. Whenever we doubted ourselveswe thought of such people and took courage from theiraccomplishments.

I’m happy to say the Harvard Center for CognitiveStudies was a success. The bright young graduates grewup to become important psychologists unafraid of wordslike mind and expectation and perception and memory. Sothat was how I experienced the cognitive revolution inpsychology.

The cognitive revolution and cognitive science

While experimental psychologists were rethinking thedefinition of psychology, other important developmentswere occurring elsewhere. Norbert Wiener’s cyberneticswas gaining popularity, Marvin Minsky and JohnMcCarthy were inventing artificial intelligence, andAlan Newell and Herb Simon were using computers tosimulate cognitive processes. Finally, Chomsky wassingle-handedly redefining linguistics.

In the Historical Addendum to Newell and Simon’sHuman Problem Solving [3] they say: ‘1956 could be takenas the critical year for the development of informationprocessing psychology’ (p. 878). This is not difficult tojustify. 1956 was the year that McCarthy, Minsky,Shannon and Nat Rochester held a conference on artificialintelligence at Dartmouth that was attended by nearlyeveryone working in the field at that time. In 1956Shannon and McCarthy edited Automata Studies [4],and Minsky circulated a technical report that, after manyrevisions, and 5 years later, became his influential article,‘Steps toward artificial intelligence’ [5].

It was also in 1956 that Jerry Bruner, Jackie Goodenoughand George Austin published A Study of Thinking [6],which took seriously the notion of cognitive strategies. In1956 signal-detection theory was applied to perception byTanner, Swets, Birdsall and others at Michigan. Ipublished an article entitled ‘The magical number seven,plus or minus two’ [7] describing some limits on our humancapacity to process information. In 1956 Ward Goodenoughand Floyd Lounsbury published several articles oncomponential analysis that became models for cognitiveanthropology, and J.B. Carroll edited a collection ofpapers by Benjamin Lee Whorf on the effects of languageon thought.

In short, 1956 was a good year for those interested intheories of the mind, but it was only slightly better thanthe years just preceding and following. Many were ridingthe waves that began during World War II: those of servotheory, information theory, signal-detection theory, com-puter theory and computers themselves.

Moment of conception

Newell and Simon were right to put a finger on 1956, whichwas not only crucial in their own development but for all ofus. Indeed, I can narrow it down even further. I date themoment of conception of cognitive science as 11 September,1956, the second day of a symposium organized by the‘Special Interest Group in Information Theory’ at theMassachusetts Institute of Technology [8]. At the time, ofcourse, no one realized that something special hadhappened so no one thought that it needed a name; thatcame much later.

The chairman of the organizing committee was PeterElias, who had only recently arrived at MIT from a JuniorFellowship at Harvard. The first day, 10 September, wasdevoted to coding theory, but it is the second day of thesymposium that I take to be the moment of conception forcognitive science. The morning began with a paper byNewell and Simon on their ‘logic machine’. The secondpaper was from IBM: Nat Rochester and collaborators hadused the largest computer then available (an IBM 704 witha 2048-word core memory) to test Donald Hebb’s neuro-psychological theory of cell assemblies. Victor Yngve thengave a talk on the statistical analysis of gaps and itsrelation to syntax.

Noam Chomsky’s contribution used information theoryas a foil for a public exposition of transformationalgenerative grammar. Elias commented that other linguistshad told him that language has all the precision ofmathematics but Chomsky was the first linguist to back

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up the claim. His 1956 paper contained the ideas that heexpanded a year later in his monograph, SyntacticStructures [9], which initiated a cognitive revolution intheoretical linguistics.

To complete the second day, G.C. Szikali described someexperiments on the speed of perceptual recognition, Italked about how we avoid the bottleneck created by ourlimited short-term memory, and Swets and Birdsallexplained the significance of signal-detection theory forperceptual recognition. The symposium concluded on thefollowing day.

I left the symposium with a conviction, more intuitivethan rational, that experimental psychology, theoreticallinguistics, and the computer simulation of cognitiveprocesses were all pieces from a larger whole and thatthe future would see a progressive elaboration andcoordination of their shared concerns.

The birth of cognitive science

By 1960 it was clear that something interdisciplinary washappening. At Harvard we called it cognitive studies, atCarnegie-Mellon they called in information-processingpsychology, and at La Jolla they called it cognitive science.What you called it didn’t really matter until 1976, whenthe Alfred P. Sloan Foundation became interested.

The Sloan Foundation had just completed a highlysuccessful program of support for a new field called‘neuroscience’ and two vice-presidents of the Foundation,Steve White and Al Singer, were thinking that the nextstep would be to bridge the gap between brain and mind.They needed some way to refer to this next step and theyselected ‘cognitive science.’ They created a Sloan SpecialProgram in Cognitive Science in order to explore thepossibilities.

I learned of the Foundation’s interest in 1977 fromKenneth A. Klivington, who was on the staff at theFoundation. My recollection is that Ken had talked toMarvin Minsky and others at MIT and was considering arecommendation that the Foundation invest in artificialintelligence. Shamelessly, I argued that in that case theFoundation’s money would be spent buying computers. Iclaimed that AI was merely part of a much largermovement. At that time the Sloan Foundation wassensitive to the charge that it had become part of theMIT endowment, so my lobbying for a broader constitu-ency was well received.

Interdisciplinary activities

I argued that at least six disciplines were involved:psychology, linguistics, neuroscience, computer science,anthropology and philosophy. I saw psychology, linguisticsand computer science as central, the other three asperipheral. These fields represented, and still represent,an institutionally convenient but intellectually awkwarddivision. Each, by historical accident, had inherited aparticular way of looking at cognition and each hadprogressed far enough to recognize that the solution tosome of its problems depended crucially on the solution ofproblems traditionally allocated to other disciplines.

The Sloan Foundation accepted my argument and acommittee of people from the several fields was assembled

to summarize the state of cognitive science in 1978, and towrite a report recommending appropriate action. Thecommittee met once, in Kansas City. It quickly becameapparent that everyone knew his own field and had heardof two or three interesting findings in other fields. Afterhours of discussion, experts in discipline X grew unwillingto make any judgments about discipline Y, and so forth. Inthe end, they did what they were competent to do: eachsummarized his or her own field and the editors – SamuelJay Keyser, Edward Walker and myself – patched togethera report (Keyser, S.J., Miller, G.A., and Walker, E.,Cognitive Science in 1978. An unpublished report sub-mitted to the Alfred P. Sloan Foundation, New York).

Our report had one figure, which is reproduced here(Fig. 1). The six fields are connected in a hexagon. Eachline in the figure represented an area of interdisciplinaryinquiry that was well defined in 1978 and that involved thetools of the two disciplines it linked together. Thus,cybernetics used concepts developed by computer scienceto model brain functions elucidated in neuroscience.Similarly, computer science and linguistics were alreadylinked through computational linguistics. Linguistics andpsychology are linked by psycholinguistics, anthropologyand neuroscience were linked by studies of the evolution ofthe brain, and so on. Today, I believe, all fifteen possiblelinks could be instantiated with respectable research, andthe eleven links we saw as existing in 1978 have beengreatly strengthened.

The report was submitted, reviewed by anothercommittee of experts, and accepted by the Sloan Foun-dation. The program that was initiated provided grants toseveral universities with the condition that the funds beused to promote communication between disciplines. Oneof the smaller grants went to Michael Gazzaniga, then atthe Cornell Medical School, and enabled him to initiatewhat has since become cognitive neuroscience. As aconsequence of the Sloan program, many scholars becamefamiliar with and tolerant of work in other disciplines. Forseveral years, interdisciplinary seminars, colloquia andsymposia flourished.

Fig. 1. Cognitive science in 1978. Each line joining two disciplines represents inter-

disciplinary inquiry that already existed in 1978.

TRENDS in Cognitive Sciences

Psychology

Computerscience

Linguistics

Anthropology

Neuroscience

Philosophy

Review TRENDS in Cognitive Sciences Vol.7 No.3 March 2003 143


Cognitive sciences today

Unfortunately, the Alfred P. Sloan Foundation did notfollow up this initiative, but the interactions stimulated inthe early 1980s have left their mark. Some veterans ofthose days question whether the program was successful,and whether there really is something now that we can call‘cognitive science’. For myself, I prefer to speak of thecognitive sciences, in the plural. But the original dream ofa unified science that would discover the representationaland computational capacities of the human mind and theirstructural and functional realization in the human brainstill has an appeal that I cannot resist.

References

1 Miller, G.A. (1951) Language and Communication, McGraw-Hill2 Skinner, B.F. (1957) Verbal Behavior, Appleton-Century-Crofts3 Newell,A.andSimon,H.A. (1972)HumanProblemSolving,Prentice-Hall4 Shannon, C.E., McCarthy, J. eds (1956) Automata Studies, Annals of

Mathematics Studies (Vol. 34) Princeton University Press5 Minsky, M. (1961) Steps toward artificial intelligence. Proc. IRE 49,

8–296 Bruner, J.S. et al. (1956) A Study of Thinking, John Wiley7 Miller, G.A. (1956) The magical number seven, plus or minus two.

Psychol. Rev. 63, 81–978 Elias, P. et al. (1956) Information theory. IRE Trans. Information

Theory, IT-2(3)9 Chomsky, N. (1957) Syntactic Structures, Mouton

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PO Box 800, Oxford, UK OX5 1DX.

Review TRENDS in Cognitive Sciences Vol.7 No.3 March 2003144


The Physical Symbol Systems Hypothesis

Formal logic: the symbols are words like ”and”, ”or”, ”not”, ”for all x” and so on. The expressions are

statements in formal logic which can be true or false. The processes are the rules of logical deduction.

Algebra: the symbols are ”+”, ”x”, ”y”, ”1”, ”2”, ”3”, etc. The expressions are equations. The processes

are the rules of algebra, that allow one to manipulate a mathematical expression and retain its truth.

A digital computer: the symbols are zeros and ones of computer memory, the processes are the

operations of the CPU that change memory.

Chess: the symbols are the pieces, the processes are the legal chess moves, the expressions are the

positions of all the pieces on the board.


The Physical Symbol Systems Hypothesis

Newell and Simon:

”A physical symbol system has the necessary and sufficient means for intelligent action.”

A physical symbol system ”consists of a set of entities, called symbols, which are physical

patterns that can occur as components of another type of entity called an expression (or symbol

structure). Thus, a symbol structure is composed of a number of instances (or tokens) of symbols

related in some physical way (such as one token being next to another). At any instant of time the

system will contain a collection of these symbol structures. Besides these structures, the system

also contains a collection of processes that operate on expressions to produce other expressions:

processes of creation, modification, reproduction and destruction. A physical symbol system is a

machine that produces through time an evolving collection of symbol structures. Such a system

exists in a world of objects wider than just these symbolic expressions themselves.”


Neural Networks: Biological and Artificial


• McCulloch-Pitts model

• Hebb’s learning rule

• Perceptron: learning machine

• Multi-layer Perceptron

• The Connectionist Movement: Parallel

Distributed Processing

• Back to Neurobiology!

The network on the left is a simple feed forward networkof the kind we have already met. The right hand networkhas an additional connection from the hidden unit to itself.What difference could this seemingly small change to thenetwork make?Each time a pattern is presented, the unit computes itsactivation just as in a feed forward network. However itsnet input now contains a term which reflects the state of thenetwork (the hidden unit activation) before the pattern wasseen. When we present subsequent patterns, the hiddenand output units’ states will be a function of everythingthe network has seen so far.


Cognitive Neuroscience: Multiple Levels - Multiple Methods

• Phrenology

• Localizationist view

• Aggregate field view

• Neuron doctrine

• Combining neuroscience and cognitive

science: brain mapping


Cognitive Neuroscience: Multiple Levels - Multiple Methods

Experimental methods and

disciplines

• Anatomy: discover the structureof the brain

• Electrophysiology

• Single cell recording• Electroencephalography

(EEG)

• Psychological experiments onpeople with brain damage, e.g.HM

• Brain imaging

Brain imaging

• EEG and MEG

• CT or CAT: Computerized axial to-

mography

• PET : Positron emission tomography

• fMRI: Functional magnetic resonance

imaging


Multiple Memory Systems


Minireview

Memory systems of the brain: A brief history and current perspective

Larry R. Squire*

Veterans Affairs Healthcare System, San Diego, CA 92161, USA

Departments of Psychiatry, Neurosciences, and Psychology, University of California, San Diego, La Jolla, CA 92093, USA

Received 23 April 2004; accepted 14 June 2004

Available online 4 August 2004

Abstract

The idea that memory is composed of distinct systems has a long history but became a topic of experimental inquiry only after

the middle of the 20th century. Beginning about 1980, evidence from normal subjects, amnesic patients, and experimental animals

converged on the view that a fundamental distinction could be drawn between a kind of memory that is accessible to conscious rec-

ollection and another kind that is not. Subsequent work shifted thinking beyond dichotomies to a view, grounded in biology, that

memory is composed of multiple separate systems supported, for example, by the hippocampus and related structures, the amyg-

dala, the neostriatum, and the cerebellum. This article traces the development of these ideas and provides a current perspective on

how these brain systems operate to support behavior.

Published by Elsevier Inc.

Keywords: Declarative; Nondeclarative; Priming; Episodic; Semantic; Procedural; Habit; Conditioning

The idea that memory is not a single faculty of the

mind is not itself new. One can find expressions of this

idea in the writings of psychologists and philosophers

more than a century ago. For example, it is often noted

that Maine de Biran wrote in 1804 about mechanical

memory, sensitive memory, and representative memory(Maine de Biran, 1804/1929), and that William James

(1890) wrote separate chapters on memory and habit

in his Principles of Psychology. Another percipient wri-

ter was Bergson (1910). Focusing on habits, he wrote:

[It is] a memory profoundly different . . . always bentupon action, seated in the present and looking only tothe future. . . In truth it no longer represents our pastto us, it acts it; and if it still deserves the name of mem-ory, it is not because it conserves bygone images, butbecause it prolongs their useful effect into the presentmoment. (p. 93).

One can identify other antecedents as well. McDou-

gall (1923) distinguished between explicit and implicit

recognition, and Tolman (1948) wrote at length on the

proposition that there is more than one kind of learning.

Most often in the earlier literature, one finds this idea

expressed as a dichotomy between two kinds of mem-ory. Thus, Ryle (1949) distinguished between knowing

how and knowing that, and Bruner (1969) contrasted

memory without record and memory with record. In

the 1970s a similar distinction was discussed in the arti-

ficial intelligence literature as procedural and declarative

knowledge (Winograd, 1975).

Yet these writings did not lead to a single view of the

matter. What was finally needed was not philosophicaldiscourse or psychological intuition but experimental in-

quiry into how the brain actually stores information.

The modern experimental era arguably began when Mil-

ner (1962) demonstrated that a hand–eye coordination

skill (mirror drawing) could be learned over a period

of days by the severely amnesic patient H.M. in the ab-

sence of any memory of having practiced the task be-

fore. While this finding showed that memory was not

www.elsevier.com/locate/ynlme

Neurobiology of Learning and Memory 82 (2004) 171–177

1074-7427/$ - see front matter. Published by Elsevier Inc.

doi:10.1016/j.nlm.2004.06.005

* Fax: 1-858-552-7457.

E-mail address: [email protected].

unitary, discussions at the time tended to set aside motor

skills as a special case that represented a less cognitive

form of memory. The idea was that all the rest of mem-

ory is of one piece and that all the rest of memory is im-

paired in patients like H.M.

Subsequently, and into the 1970s, there were twostrands of work that moved the focus beyond motor

skills. One strand came from studies of experimental

animals, and the other strand came from work with

amnesic patients. In the case of animal studies, distinc-

tions were drawn between recognition and associative

memory (Gaffan, 1974), contextual retrieval and habit

(Hirsh, 1974), and taxon and locale memory (O�Keefe

& Nadel, 1978). These proposals had in common theidea that the hippocampus, and perhaps related struc-

tures, was involved in one particular type of memory.

Yet, the proposals also differed from each other and

their prescience was not widely appreciated, in part be-

cause at the time the findings in experimental animals

did not conform well to the findings from human mem-

ory and amnesia. Indeed, one of the reasons that it took

a long time to develop an animal model of human amne-sia was that it was not understood what needed to be

modeled. That is, the description of human amnesia

was itself incomplete, and many tasks given to experi-

mental animals were in fact tasks that animals could

succeed at just as patients succeeded at motor skills.

However, after an animal model of human amnesia

was established in the nonhuman primate (Mishkin,

1982; Squire & Zola-Morgan, 1983), a useful correspon-dence was established between the findings for experi-

mental animals and humans, and since that time work

with experimental animals has been invaluable for

understanding the memory systems of the brain.

The second strand of relevant work in the 1960s and

1970s came from demonstrations of unexpectedly good

learning and retention by amnesic patients on tasks

other than motor skill tasks (Milner, Corkin, & Teuber,1968; Warrington & Weiskrantz, 1968). However, there

were two reasons why these findings, and others that fol-

lowed in the subsequent few years, did not lead to pro-

posals of multiple memory systems. First, even when the

performance of amnesic patients was good, it sometimes

fell short of normal levels. Second, in those cases when

amnesic patients did perform normally, or near nor-

mally, a dominant view was that amnesia was thereforea retrieval deficit (Warrington & Weiskrantz, 1970,

1978; Weiskrantz, 1978).

Quite apart from issues of interpretation, it took

some time to appreciate the crucial role of test instruc-

tions in determining whether amnesic patients per-

formed as well as controls. For example, amnesic

patients often performed well when they were given

three-letter word stems as cues for previously presentedwords, a demonstration of what would later be termed

priming. Only later was it appreciated that normal per-

formance by amnesic patients depended on a nonme-

mory kind of instruction (Use this word stem to form

the first word that comes to mind). With conventional

memory instructions (Use this word stem as a cue to re-

trieve a previously presented word), controls performed

better than amnesic patients (Graf, Squire, & Mandler,1984).

The fact that amnesic patients do perform normally

when these tests are structured appropriately (for an

early example, see Jacoby &Witherspoon, 1982) showed

that the phenomenon of priming is a distinct form of

memory, separate from what is impaired in amnesia

(Schacter & Buckner, 1998; Tulving & Schacter, 1990).

Evidence for the special status of priming also camefrom studies of normal subjects (Tulving, Schacter, &

Stark, 1982). These authors wrote ‘‘. . . we are tempted

to think that [these priming effects] reflect the operation

of some other, as yet little understood, memory system.’’

(p. 341). Perhaps the most compelling evidence for the

independence of priming and the kind of memory im-

paired in amnesia came much later from parallel studies

of perceptual priming (Hamann & Squire, 1997; Stark &Squire, 2000) and conceptual priming (Levy, Stark, &

Squire, in press). This work showed that severely amne-

sic patients can exhibit fully intact priming while

performing at chance on conventional recognition mem-

ory tests for the same test items.

During the period that priming was discovered to be

intact in amnesia, it also became appreciated that motor

skills are not special after all but are a subset of a largerdomain of skill-like abilities, all of which are preserved

in amnesia. The first example was the task of mirror

reading, a perceptual skill, which amnesic patients ac-

quired at a normal rate despite poor memory for the

words that they read (Cohen & Squire, 1980). Other

demonstrations followed (e.g., the ability to resolve ste-

reoscopic images, Benzing & Squire, 1989; cognitive skill

learning, Squire & Frambach, 1990; artificial grammarlearning, Knowlton, Ramus, & Squire, 1992; and cate-

gory learning, Knowlton & Squire, 1993).

Initially, these data were interpreted according to a

distinction between declarative and procedure knowl-

edge (Cohen & Squire, 1980). Other, similar dichotomies

also came into use [e.g., explicit and implicit memory

(Graf & Schacter, 1985); memory and habit (Mishkin,

Malamut, & Bachevalier, 1984)]. However, during the1980s, it became progressively difficult to fit the accumu-

lating data to the two poles of a dichotomy. For exam-

ple, emerging findings about priming led Tulving and his

colleagues to write:

But even if we accept the broad division of memory intoprocedural and propositional forms and the division ofpropositional forms into episodic and semantic forms,there are phenomena that do not seem to fit readily intosuch a taxonomy. (Tulving et al., 1982, p. 336).

172 L.R. Squire / Neurobiology of Learning and Memory 82 (2004) 171–177

Work with experimental animals also influenced the

movement away from dichotomies. First, in the early

1980s, the cerebellum was discovered to be essential

for delay eyeblink conditioning (McCormick, Clark,

Lavond, & Thompson, 1982), a form of learning that

was entirely preserved both in animals with hippocam-pal lesions (Schmaltz & Theios, 1972) and in severely

amnesic patients (Clark & Squire, 1998; Gabrieli, McG-

linchey-Berroth, Gluck, Cermak, & Disterhoft, 1995).

Second, the neostriatum was identified as important

for the sort of gradual, feedback-guided learning that re-

sults in habit memory (Mishkin et al., 1984), and an ele-

gant double dissociation was demonstrated in rats after

fornix and caudate lesions in two tasks that appeared tomeasure declarative memory and habit memory, respec-

tively (Packard, Hirsh, & White, 1989). A similar con-

trast between declarative memory and habit memory

was later demonstrated for amnesic patients and pa-

tients with Parkinsons�s disease (Knowlton, Mangels,

& Squire, 1996). Finally, it was shown that still other

types of learning, which involve the attachment of posi-

tive or negative valence to a stimulus, as in fear condi-tioning or conditioned place preference, have an

essential dependence on the amygdala (Davis, 1992;

Fanselow, 1994; LeDoux, 2004; McDonald & White,

1993).

Given the wide variety of learning and memory tasks

explored in these studies, and the number of different

brain structures that were implicated, an account of

memory based on a two-part dichotomy came to appearoverly simplistic. Indeed, one wondered what the vari-

ous kinds of memory that were preserved in amnesic pa-

tients had in common aside from the fact that they were

not declarative. Accordingly, beginning in the mid

1980s, the perspective shifted to a framework that

accommodated multiple (i.e., more than two) memory

systems (see for example, Tulving, 1985). At that time,

the term ‘‘nondeclarative’’ was introduced with the idea

that declarative memory refers to one memory system

and that ‘‘nondeclarative memory’’ is an umbrella term

referring to several additional memory systems (Squire

& Zola-Morgan, 1988). The seminal volume of this per-iod, The Memory Systems of 1994 (Schacter & Tulving,

1994), presented a collection of writings that largely re-

flected this point of view.

The result of all this was that it was now possible to

reach a clearer, more concrete, and ultimately a more

accurate classification of memory by placing the work

within a biological framework. Fig. 1 illustrates a taxon-

omy that incorporates these ideas (for the earliest ver-sion of this diagram, see Squire, 1987). Declarative

memory is the kind of memory that is meant when the

term ‘‘memory’’ is used in everyday language. It refers

to the capacity for conscious recollection about facts

and events and is the kind of memory that is impaired

in amnesia and dependent on structures in the medial

temporal lobe and midline diencephalon. Other charac-

teristics of declarative memory allow the term to be ex-tended to experimental animals and bring work with

humans and animals into more comfortable contact.

Thus, declarative memory allows remembered material

to be compared and contrasted. It supports the encoding

of memories in terms of relationships among multiple

items and events. The stored representations are flexible

and can guide performance under a wide range of test

conditions. Declarative memory is representational. Itprovides a way to model the external world, and as a

model of the world it is either true or false. In contrast,

nondeclarative memory is neither true nor false. It is dis-

positional and is expressed through performance rather

than recollection. Nondeclarative forms of memory oc-

cur as modifications within specialized performance sys-

Fig. 1. A taxonomy of mammalian long-term memory systems. The taxonomy lists the brain structures thought to be especially important for each

form of declarative and nondeclarative memory. In addition to its central role in emotional learning, the amygdala is able to modulate the strength of

both declarative and nondeclarative memory.

L.R. Squire / Neurobiology of Learning and Memory 82 (2004) 171–177 173

tems. The memories are revealed through reactivation of

the systems within which the learning originally occurred.

Declarative memory can be divided into semantic

memory (facts about the world) and episodic memory

(the capacity to re-experience an event in the context

in which it originally occurred) (Tulving, 1983). Episodicmemory requires the participation of brain systems in

addition to those that support semantic memory, for

example, the frontal lobes (Shimamura & Squire, 1987;

Tulving, 1989). It is an interesting question whether

nonhuman animals have a capacity for episodic memory

(Tulving, 2002), but the idea is difficult to put to test (for

relevant experiment and discussion, see Clayton & Dick-

inson, 1998; Tulving, in press).The various memory systems can be distinguished in

terms of the different kinds of information they process

and the principles by which they operate. In the case of

declarative memory, an important principle is the ability

to detect and encode what is unique about a single event,

which by definition occurs at a particular time and place.

In the case of nondeclarativememory, an important prin-

ciple is the ability to gradually extract the common ele-ments from a series of separate events. Sherry and

Schacter (1987) suggested that multiple memory systems

evolved because they serve distinct and functionally

incompatible purposes. For example, the gradual changes

that occur in birdsong learning are fundamentally differ-

ent from and have a different function than the rapid

learning that occurs when a bird caches food for later

recovery.The memory systems of the brain operate in parallel

to support behavior. For example, an aversive child-

hood event involving being knocked down by a large

dog can lead to a stable declarative memory for the

event itself as well as a long-lasting nondeclarative fear

of dogs (a phobia) that is experienced as a personality

trait rather than as a memory. The idea that memory

systems operate independently and in parallel is nicelyillustrated by a study of rats that was carried out in a

four-arm, plus-shaped maze (Packard & McGaugh,

1996). First, the upper (north) arm was blocked, and

rats were started from the lower (south) arm and trained

to turn to the west to find food. Probe trials were intro-

duced at various times by starting rats from the north

arm (with the south arm now blocked). In probe trials

given early in training, rats entered the rewarded (west)arm, that is, they returned to the place where food had

been found. In probe trials introduced later in training,

rats went to the nonrewarded arm, that is, they turned

east, thereby repeating the left-turn response that they

had previously made to find food. Place responding

early in training was abolished by lidocaine injections

into the hippocampus, and rats exhibited no preference

for either arm. Correspondingly, later in training, thepreference for a left-hand turn was abolished by lido-

caine injections into the caudate nucleus. Interestingly,

in this case rats did not behave randomly but now exhib-

ited place responding (that is, they turned west). Thus,

even though behavior was dominated later in training

by the caudate nucleus, and by left-hand turns, informa-

tion remained available about the place where food

could be found. When the caudate nucleus was disabled,the parallel memory system supported by the hippocam-

pus was revealed.

While one memory system may substitute for another

in the sense just described, what is learned differs mark-

edly depending on which memory system is engaged. In

the rat, what is learned might be a spatial location or a

turning response. In humans, the difference can also be

quite striking. For example, consider a task introducedby Tulving, Hayman, & MacDonald (1991), in which

simple three-word sentences are presented as novel

‘‘facts’’ to be learned, e.g., medicine cured hiccups. In a

recent study, sentences were presented repeatedly across

several sessions, and recall was subsequently tested by

asking participants to complete the first two words of

each sentence so as to form a sentence that had been

studied (medicine cured) (Bayley & Squire, 2002). Whenthe hippocampus and related structures were able to

support performance, as in healthy volunteers, learning

occurred rapidly and what was learned was accompa-

nied by conscious knowledge about which answers were

correct. Further, the learning was readily expressed even

if a part of the sentence was replaced by a synonym

(medicine relieved). By contrast, in severe amnesia,

declarative memory was not available and learningwas extremely slow. Importantly, what little was learned

during 12 weeks of training was outside of awareness,

confidence ratings were unrelated to success, and perfor-

mance succeeded only when the first two words in the

test sentences were the same words that had appeared

during training.

These findings in humans and rats emphasize that

what is important is not only the task that is to be learnedbut also what strategy is implemented during learning,

which in turn reflects what memory system is engaged.

Under some circumstances the strategy that is engaged

is not optimal for solving a task. For example, hippo-

campal lesions in rats can facilitate the acquisition of a

maze task that requires repeated visits to illuminated

arms and that is dependent on the caudate nucleus

(Packard et al., 1989). The hippocampal lesion presum-ably disrupts the tendency to use a nonoptimal declara-

tive memory strategy, in the same sense that trying to

memorize what one is doing can interfere with human

skill learning. Indeed, when humans acquire a difficult

habit learning task, structures important for habit learn-

ing and structures important for memorizing (i.e., declar-

ative memory) can appear to compete for control of

performance. Early in learning, fMRI revealed activitywithin the medial temporal lobe, as if participants were

attempting to memorize the task structure (Poldrack et


al., 2001). As performance improved, activity decreased

in the medial temporal lobe and increased in the neostri-

atum. Activity in these two regions was negatively corre-

lated across participants, consistent with the idea that

these regions support different kinds of learning that de-

pend in turn on incompatible learning strategies.The insight that different strategies can be brought to

the same learning problem helps explain the otherwise

surprising discovery that some tasks that are learned

declaratively by humans are nevertheless learned nondec-

laratively by experimental animals. The best-known

example of this circumstance is visual pattern discrimina-

tion learning (e.g., + vs. fi). Monkeys with large medial

temporal lobe lesions are intact at the learning and reten-tion of pattern discriminations (Squire & Zola-Morgan,

1983). Yet, amnesic patients learn such tasks in a few tri-

als, like normal individuals, and then later forget which

stimulus is the correct one (Squire, Zola-Morgan, &

Chen, 1988). The difference appears to lie in the fact that

monkeys learn the pattern discrimination task gradually

during several hundred trials in a manner reminiscent

of skill learning (Iversen, 1976) and that humans ap-proach the task as a simple problem of memorization.

Whereas in humans the learning and retention of pattern

discriminations is dependent on the medial temporal

lobe, inmonkeys the pattern discrimination task is depen-

dent on an inferior temporal lobe-neostriatal pathway

(Mishkin et al., 1984; Teng, Stefanacci, Squire, & Zola,

2000). To achieve a two-choice discrimination task for

humans that is acquired as a skill, and in the way thatmonkeys learn the pattern discrimination task, one might

ask humans to learn to discriminate between the paint-

ings of a master and the paintings of a talented forger.

The notion of multiple memory systems is now widely

accepted (Eichenbaum & Cohen, 2001; Schacter, Wag-

ner, & Buckner, 2000; Squire, Stark, & Clark, 2004).

Yet it is interesting that one can still find the exclusively

psychological perspective ‘‘. . . that there is only onememory system, which preserves all experiences and is

used in all tasks’’ (Whittlesea & Price, 2001). Similar

viewpoints have been advanced occasionally during the

past 20 years. Typically, the notion is that there is only

one memory system but that there are multiple processes

operating on this system or multiple ways of accessing

its contents. The difficulty with such views is that they

are unnecessarily abstract and make insufficient contactwith biology. For example, the findings from eyeblink

conditioning provide direct evidence for a kind of mem-

ory that can be acquired, stored, and retrieved in the ab-

sence of the forebrain. Other kinds of memory (e.g.,

perceptual learning, declarative memory) do require

the forebrain. The locus of memory storage is entirely

different in these cases, and the learning proceeds by dif-

ferent principles. Perhaps there is some level of abstrac-tion at which synaptic changes within the cerebellum

and synaptic changes within the neocortex can be

viewed as different expressions of a single memory sys-

tem. However, such a perspective tends to ignore rather

than to embrace the enormous amount that has been

learned about neuroanatomy, the molecular and cellular

biology of synaptic change, and the organization of

brain systems.In biology, the term ‘‘system’’ is defined in terms of

both structure and function. The study of memory has

benefited in recent years as discussion of memory

systems has drawn increasingly on what is known

about biological systems. Strictly functional constructs

founded in psychological science alone are seldom suffi-

cient because psychology has matured to the point

where it is able to connect concepts about memory tobiology. And history shows that, as biological informa-

tion becomes available about structure and mechanism,

explanation becomes more concrete and less dependent

on terminology.

During the past two centuries, the study of memory,

and the study of cognition in general, has been central to

three disciplines: first philosophy, then psychology, and

now biology. One can expect the contributions of biol-ogy to the study of memory to become even more cen-

tral in the coming years as more is learned about the

molecular biology of synaptic change and the neurosci-

ence of brain systems.

Acknowledgments

Supported by the Medical Research Service of the

Department of Veterans Affairs, the National Institute

of Mental Health, and the Metropolitan Life Founda-

tion. I thank Robert Clark and Nicola Broadbent for

comments and discussion.

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Language acquisition, evolution and processing

• How does the mind turn sounds into words (phonology)?

• How does the mind turn words into sentences (syntax)?

• How does the mind understand words and sentences (semantics)?

• How does the mind understand discourse (semantics, pragmatics)?

• How does the mind generate discourse?

• How does the mind translate between languages?

• How does the mind acquire the capacities just described?

• To what extent is knowledge of language innate?


Language acquisition, evolution and processing

Hypotheses about how the mind uses language

• Symbolic

– Linguistic knowledge consists largely of rules that govern phonological and syntactic processing.

– The computational procedures involved in understanding and generating language are largely rule-

based.

– Language learning is learning of rules.

– Many of these rules are innate.

– The leading proponent of this general view has been Noam Chomsky.

– Rule-based models of language comprehension and generation have been developed in the SOAR

system and within other frameworks

• Connectionist

– Linguistic knowledge consists largely of statistical constraints that are less general than rules and

are encoded in neural networks.

– The computational procedures involved in understanding and generating language are largely parallel

constraint satisfaction.


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Embodied Cognition - Cognitive Robots


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TUTORIAL ONEMBODIMENT

Tutorial onEmbodiment

1. Introduction

2. Traditional AI, itsSuccesses andProblems

3. Embodiment

4. InformationTheoretic Implicationsof Embodiment

5. Embodied Cognition

Tutorial on Embodiment

Welcome to the tutorial on embodimentTutorial Content:

1. Introduction

1.1. Outline

1.2. Target Audience

2. Traditional Artificial Intelligence, its Successes and Problems

2.1. The Study of Intelligence

2.2. Successes of Traditional AI

2.3. Problems of Traditional AI

3. Embodiment

3.1. Theoretical Scheme

3.2. Locomotion Case Studies

3.2.1. Brainless and powerless creatures

3.2.1.1. Euthanized trout

3.2.1.2. Passive dynamic walkers

3.2.1.3. Exercise

3.2.2. Brainless and powered creatures

3.2.2.1. Behavioral diversity crazy bird

3.2.2.2. Selfstabilization

3.2.2.3. Exercise

3.2.3. Simple control exploiting body dynamics and interaction with environment

3.2.3.1. Leg coordination in insect walking

3.2.3.2. Exploiting selfadaptation: cockroaches climbing over obstacles

3.2.3.3. Exercise

3.3. Grasping Case Studies

3.3.1. Cheap grasping with a robotic hand

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3.3.2. Cheap grasping with an universal gripper

3.3.3. Exercise

3.4. Additional Online Lectures

4. Information Theoretic Implications of Embodiment

4.1. The Role of Sensory Morphology in Perception

4.1.1. Eye morphology

4.1.2. Slippage detection through skin morphology

4.2. Information Selfstructuring through Sensorymotor Coordination

4.2.1. Visual perception case study

4.2.2. Quantifying information structure

4.3. Exercise

5. Embodied Cognition

5.1. Cognitive Science Paradigms

5.1.1. Cognitivism

5.1.2. Connectionism

5.1.3. Embodied dynamicism and enactivism

5.1.4. Summary

5.2. Learning Categories

5.2.1. Grounding toddler learning in sensorymotor dynamics

5.2.2. Categorization in artificial agents

5.2.3. Where mathematics comes from?

5.3. Cognition from Bottomup

5.3.1. Body schema and forward models

5.3.2. Why is that cognition?

5.3.3. Cognitive development and the iCub humanoid robot

Editors:

Matej Hoffmann, Dorit Assaf, Rolf Pfeifer

Artificial Intelligence LaboratoryDepartment of Informatics

University of Zurich

Switzerland

Contact:

Matej Hoffmann ([email protected]),Dorit Assaf ([email protected])

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Classical and modern view of intelligence.

Illustrations by Shun Iwasawa

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Consciousness

2.1 Creature Consciousness An animal, person or other cognitive system may be regarded as

conscious in a number of different senses.

Sentience. It may be conscious in the generic sense of simply being a sentient creature, one

capable of sensing and responding to its world (Armstrong 1981). Being conscious in this sense

may admit of degrees, and just what sort of sensory capacities are sufficient may not be sharply

defined. Are fish conscious in the relevant respect? And what of shrimp or bees?

Wakefulness. One might further require that the organism actually be exercising such a

capacity rather than merely having the ability or disposition to do so. Thus one might count it

as conscious only if it were awake and normally alert. In that sense organisms would not count

as conscious when asleep or in any of the deeper levels of coma. Again boundaries may be blurry,

and intermediate cases may be involved. For example, is one conscious in the relevant sense when

dreaming, hypnotized or in a fugue state?

Self-consciousness. A third and yet more demanding sense might define conscious creatures

as those that are not only aware but also aware that they are aware, thus treating creature

consciousness as a form of self-consciousness (Carruthers 2000). The self-awareness requirement

might get interpreted in a variety of ways, and which creatures would qualify as conscious in the

relevant sense will vary accordingly. If it is taken to involve explicit conceptual self-awareness, many

non-human animals and even young children might fail to qualify, but if only more rudimentary

implicit forms of self-awareness are required then a wide range of nonlinguistic creatures might


count as self-conscious.

What it is like. Thomas Nagel’s (1974) famousawhat it is likea criterion aims to capture

another and perhaps more subjective notion of being a conscious organism. According to Nagel,

a being is conscious just if there is asomething that it is likea to be that creature, i.e., some

subjective way the world seems or appears from the creature’s mental or experiential point of

view. In Nagel’s example, bats are conscious because there is something that it is like for a bat to

experience its world through its echo-locatory senses, even though we humans from our human

point of view can not emphatically understand what such a mode of consciousness is like from

the bat’s own point of view.

Subject of conscious states. A fifth alternative would be to define the notion of a conscious

organism in terms of conscious states. That is, one might first define what makes a mental state a

conscious mental state, and then define being a conscious creature in terms of having such states.

One’s concept of a conscious organism would then depend upon the particular account one gives

of conscious states (section 2.2).

Transitive Consciousness. In addition to describing creatures as conscious in these various

senses, there are also related senses in which creatures are described as being conscious of various

things. The distinction is sometimes marked as that between transitive and intransitive notions of

consciousness, with the former involving some object at which consciousness is directed (Rosenthal

1986).


Integrating Emotions and Cognitions


6/4/2014 Cognition and emotion - Scholarpedia

http://www.scholarpedia.org/article/Cognition_and_emotion 1/16

Cognition and emotion +1 Recom m end this on Google

Figure 1: Contrast of viewing fearful and neutral faces.Large portions of occipitotemporal cortex are morestrongly driven by fearful faces. The arrows point to thefusiform gyrus, a ventral temporal area that is stronglydriven by face stimuli. Adapted with permission from theNational Academy of Sciences: Pessoa et al. (2002b),copyright (2002).

Dr. Luiz Pessoa, Indiana University, Bloomington, IN

The relationship between cognition and

emotion has fascinated important thinkers

within the Western intellectual tradition.

Historically, emotion and cognition have been

viewed as largely separate. In the past two

decades, however, a growing body of work has

pointed to the interdependence between the two.

Introduction

Cognition refers to processes such as memory,

attention, language, problem solving, and

planning. Many cognitive processes are thought

to involve sophisticated functions that may be

unique to primates. They often involve so-called

controlled processes, such as when the pursuit of

a goal (e.g., maintaining information in mind)

needs to be protected from interference (e.g., a

distracting stimulus). A prototypical example of

a neural correlate of a cognitive process is the

sustained firing of cells in dorsolateral

prefrontal cortex as a monkey maintains information in mind for brief periods of time (Fuster and Alexander,

1971; Kubota and Niki, 1971). With the advent of functional MRI (fMRI), it appears that cognitive processes

engage cortical regions of the brain (Gazzaniga et al., 2008).

Whereas there is relative agreement about what constitutes cognition, the same cannot be said about emotion.

Some investigators use definitions that incorporate the concepts of drive and motivation: emotions are states

elicited by rewards and punishers (Rolls, 2005). Others favor the view that emotions are involved in the

conscious (or unconscious) evaluation of events (Arnold, 1960) (i.e., appraisals). Some approaches focus on

basic emotions (Ekman, 1992) (e.g., fear, anger), others on an extended set of emotions, including moral ones

(Haidt, 2003; Moll et al., 2005) (e.g., pride, envy). Strong evidence also links emotions to the body (Damasio,

1994). Brain structures linked to emotion are often subcortical, such as the amygdala, ventral striatum, and

hypothalamus. These structures are often considered evolutionarily conserved, or primitive. They are also

believed to operate fast and in an automatic fashion, such that certain trigger features (e.g., the white of the eyes

in a fearful expression (Whalen et al., 2004)) are relatively unfiltered and always evoke responses that may be



important for survival. Accordingly, an individual may not be necessarily conscious of a stimulus that may have

triggered brain responses in an affective brain region, such as the amygdala. For discussion, see (Ohman, 2002;

Pessoa, 2005).

Because of the inherent difficulty in providing clear definitions for both cognition and emotion, they will not be

further defined here. We now turn to illustrating some of the interactions between emotion and cognition. Given

the enormous scope of this topic, by necessity, this review will be relatively narrow in scope and will emphasize

the brain systems involved in the interactions between emotion and i) perception and attention; ii) learning and

memory; and iii) behavioral inhibition and working memory. Other valuable sources include (Damasio, 1994;

LeDoux, 1996; Damasio, 1999; Dolan, 2003; Rolls, 2005; Phelps, 2006). A key conclusion from this review and

from other current discussions of the relationship between cognition and emotion is that it is probably

counterproductive to try to separate them. Instead, current thinking emphasizes their interdependence in ways

that challenge a simple division of labor into separate cognitive and emotional domains. In particular, in the

context of the brain, the general dichotomization alluded to above in terms of cortical-cognitive and subcortical-

emotional brain areas is now viewed as largely simplified and breaks down rather quickly when more in-depth

analyses are carried out; e.g., (Pessoa, 2008).

Before proceeding, however, a brief historical note is in order. The emotion/cognition debate came into sharp

focus with the report of the mere-exposure effect (Kunst-Wilson & Zajonc, 1980), which led to a strong belief

that affect was primary to and independent of cognition. It can be said that the mere-exposure effect and other

behavioral findings shifted ongoing debates to focus on affect as being related to unconscious processing and

subcortical activity, with cognition being related to conscious processing and cortical involvement. Interestingly,

behavioral findings were interpreted in the context of the “low route” suggested by LeDoux (1996), which was

purported to carry affective information subcortically. These early behavioral studies provided a strong impetus

to the wave of neuroscience research in the late 1990s (and beyond) that investigated related phenomena. For

some of the early theoretical arguments, see Fazio et al. (1986), Leventhal & Scherer (1987), Bornstein (1989),

Lazarus (1994), Zajonc (1994), and Bargh (1997); also see Storbeck, Robinson, & McCourt (2006) and Storberk

(2008).

Perception and attention

Viewing emotion-laden visual stimuli is linked to heightened and more extensive visual system activation

(Pessoa et al., 2002a; Vuilleumier, 2005). For instance, viewing faces with emotional expressions evokes

increased responses relative to viewing neutral faces throughout ventral occipitotemporal visual cortex ( Figure

1).

Visual responses are also stronger when subjects view emotional scenes (e.g., a war scene) compared do neutral

scenes (e.g., a lake scene). Increased visual activation is observed in both late visual areas, such as the fusiform

gyrus and superior temporal sulcus, and early visual cortex in occipital cortex. Recent studies suggest that, in

humans, even retinotopically organized visual cortex, including visual areas V1 and V2 along the calcarine

fissure, are modulated by the affective significance of a stimulus (Padmala and Pessoa, 2008).



Figure 2: Functional brain imaging results in humans support a roleof the amygdala in modulating visual responses to emotional stimuli.Patients with medial temporal lobe sclerosis who have lesionsinvolving the hippocampus alone (upper row) show normalactivation of the fusiform cortex when contrasting fearful vs. neutralfaces (right), as do healthy subjects (not shown). Patients withadditional lesions involving the amygdala (lower row) show no effectof fear expression in visual cortex. However, fusiform cortex is stillnormally activated in both patient groups when they perform a taskon faces relative to houses (left). These results suggest that amygdaladamage can have distant functional consequences on the activity ofvisual cortex, selectively affecting emotional modulation. Adaptedfrom Vuilleumier (2005), Trends Cogn Sci(http://www.sciencedirect.com/science/journal/13646613) , Howbrains beware: neural mechanisms of emotional attention, copyright(2005), with permission from Elsevier. Original data from(Vuilleumier et al., 2004).

Enhanced visual activation when viewing emotional stimuli is consistent with the observed improvements in

behavioral performance in several visual tasks. For instance, angry and happy faces are detected faster in visual

search tasks (Eastwood et al., 2001), and possibly other emotional stimuli, too, such as a snake or spider

(Ohman et al., 2001) compared to neutral stimuli. Stronger evidence comes from studies of the attentional blink

paradigm, in which subjects are asked to report the occurrence of two targets (T1 and T2) among a rapid stream

of visual stimuli. When T2 follows T1 by a brief delay, participants are more likely to miss it, as if they had

blinked (hence the name). The attentional blink is believed to reflect a capacity-limited processing stage,

possibly linked to a process of consolidation of the detected item for conscious reports. Interestingly, the

attentional blink has been shown to be modulated by emotional stimuli, as subjects are significantly better at

detecting T2 when it is an emotion-laden word (e.g., rape) than when it is a neutral word (Anderson, 2005).

Converging evidence for a link between perception, attention, and emotion comes from additional studies. For

example, patients with unilateral inattention due to spatial hemineglect (often as a result of right hemisphere

parietal lesions) are better at detecting happy or angry faces compared to neutral ones (Vuilleumier and

Schwartz, 2001). These findings are consistent with the notion that emotional faces may direct the allocation of

attention. For instance, in one study, emotional faces were flashed at spatial locations that subsequently

displayed low-contrast visual stimuli (Phelps et al., 2006). They found that detection of the target was strongest

when the fear face served as the spatial cue, suggesting that emotional stimuli can provide additional attentional

guidance above and beyond a generic spatial cue. How is the increase in perceptual processing and attentional

capture that is observed during the perception of affective stimuli mediated in the brain? Growing evidence links

the amygdala, a subcortical region, with these effects.

For instance, patients with amygdala

lesions do not exhibit improved

detection of T2 emotional targets

during the attentional blink (i.e., a

decrease in the magnitude of the blink

effect) (Anderson and Phelps, 2001),

and show less evidence of increased

responses in visual cortex during the

viewing of fearful faces (Vuilleumier et

al., 2004); see Figure 2.

Thus, it appears that the amygdala may

underlie a form of emotional

modulation of information that in

many ways parallels attentional effects

that are observed with non-emotional

information (Pessoa et al., 2002a;

Vuilleumier, 2005). There are several

ways in which emotional modulation

may be accomplished. First, it is

possible that direct projections from

the amygdala to visual processing

regions enhance visual processing. The



amygdala sends projections across all levels of the visual system, including anterior regions in temporal cortex

and posterior regions in occipital cortex (including V1 and V2) (Amaral et al., 1992). Thus, the amygdala is well

situated to modulate sensory processing according to the affective significance of a visual object. A second

possibility is that the amygdala interacts with other brain regions that are important for the control of attention,

such as frontal and parietal regions (Barbas, 1995), which, by their turn, modulate visual processing. In the latter

scenario, the amygdala (possibly indirectly) would recruit attentional circuits so as to enhance the sensory

processing of emotion-laden stimuli.

A final issue that should be addressed when considering interactions between emotion and perception/attention

is whether the perception of emotion-laden stimuli is automatic, namely independent of attention and

awareness. This question has received considerable attention because specific answers to this question (no or

yes) suggest potentially different relationships between emotion and cognition (more or less independence

between the two, respectively). Interestingly, evidence both for and against automaticity has been presented. For

instance, emotional faces evoke responses in the amygdala even when attention is diverted to other stimuli

(Vuilleumier et al., 2001; Anderson et al., 2003). Perhaps even more strikingly, amygdala responses are

sometimes reported for emotional faces of which subjects are not conscious (Morris et al., 1998; Whalen et al.,

1998; Etkin et al., 2004; Whalen et al., 2004). Furthermore, cases of affective blindsight have been reported.

These and other related findings suggest that at least some types of emotional perception occur outside of

cognitive processing – and may rely on direct subcortical pathways conveying visual information to the

amygdala (LeDoux, 1996). At the same time, recent findings have suggested that the perception of emotion-laden

items requires attention, as revealed by attentional manipulations that consume most processing resources,

leaving relatively few resources for the processing of unattended emotional items (Pessoa et al., 2002b; Bishop

et al., 2004; Pessoa et al., 2005; Bishop et al., 2007; Hsu and Pessoa, 2007; Lim et al., 2008). Furthermore, it

also appears that amygdala responses evoked by unaware stimuli depend somewhat on the manner by which

awareness is operationally defined (Merikle et al., 2001), such that no unaware responses are observed when

awareness is defined, for instance, via signal detection theory methods (Pessoa et al., 2006). Overall, the

automaticity debate remains unresolved and controversial (Pessoa, 2005; Wiens, 2006; Bishop, 2007).

Memory and learning

Research on classical fear conditioning suggests that the amygdala is involved in the acquisition, storage, and

expression of a conditioned fear response – such as when an animal learns that a neutral stimulus (e.g., tone)

predicts an aversive event (e.g., mild shock). Whereas fear conditioning is believed to involve a more primitive

form of affective learning, instructed fear illustrates a situation in which cognition and emotion interact more

explicitly ( Figure 4C). In this paradigm, subjects are verbally informed of the possibility of an aversive event

given the presentation of one type of neutral stimulus (e.g., tone), while the presentation of another neutral

stimulus (e.g., light) indicates that the aversive event will not occur. Interestingly, instructed fear generates

robust physiological results to the threat stimulus that resemble the responses to a conditioned stimulus (e.g.,

tone) in fear conditioning, even though the aversive event is never administered to the subjects (only a verbal

threat occurs) (Hugdahl and Ohman, 1977; Phelps et al., 2001) ( Figure 3D). Research with humans indicates that

the left amygdala appears to be necessary for instructed fear (Funayama et al., 2001). Another example of

cognitive-affective learning involves observational fear, in which an acquired fear response is learned via social

observation ( Figure 4B). In this case, both humans and nonhuman primates are capable of learning the affective



Figure 3: Fear learning in the humanamygdala. (a) The outlined box containsthe area of the medial temporal lobe thatincludes the bilateral amygdala. (b–d)Amygdala activation to the CS is seenbilaterally after fear conditioning (b) andobservational fear learning (c), andunilaterally (d) in the left amygdala afterinstructed fear. Reprinted by permissionfrom Macmillan Publishers Ltd: NatureNeuroscience (Olsson and Phelps, 2007),

properties of stimuli through observing the emotional reactions of a conspecific (Ohman and Mineka, 2001). As

in the case of instructed fear, observational fear results in the expression of conditioned fear that is similar to

the one observed during fear conditioning (Olsson and Phelps, 2004) ( Figure 3C).

Emotional content can change the formation and recollection of a memory event, consistent with findings in

both human and animal studies. Compared to neutral items, humans remember better emotionally arousing

information, including emotionally charged stories, film clips, pictures, and words. For instance, in one study

participants viewed two videos, one composed of neutral film clips and another composed of emotional film clips

(Cahill et al., 1996). Although the two types of clips were taken from the same source and were equated in terms

of levels of understandability, subjects were better at remembering emotional relative to neutral clips when

tested approximately 3 weeks following the initial viewing of the films. In another study (Bradley et al., 1992),

subjects viewed a large array of emotional and neutral pictures from the International Affective Picture System,

a stimulus set that has been normed in terms of the dimensions of valence (positive/negative) and arousal

(calm/excited). Participants initially rated the pictures along

the dimensions of valence and arousal. An incidental free-recall

test was administered both immediately and at one year

following the rating sessions. Pictures rated as highly arousing

were remembered better than all other pictures, including

those rated as moderately arousing. Interestingly, the pattern

of results was very similar when the subjects were tested a year

later, namely, highly arousing pictures were better

remembered.

In humans, the amygdala is known to be a critical structure for

the enhancement of memory by emotion, consistent with both

lesion (Adolphs et al., 1997) and neuroimaging work (for a

review, see Phelps, 2004). Recent studies have begun to

delineate some of the specific functions of this structure. For

instance, it appears that the right amygdala is more strongly

involved in emotional memory formation, whereas the left

amygdala is engaged by the retrieval of those memories

(Sergerie et al., 2006), suggesting a potential hemispheric

dissociation of amygdala involvement at different stages of

emotional memory. In addition, amygdala responses are also

linked to a novelty effect on memory tasks – i.e., the tendency

to classify items as new as opposed to old (Sergerie et al.,

2007).

In humans, there is some support for the notion that the

enhancement of memory due to emotion is due mainly to the

arousal dimension of emotional items and not valence

(positive/negative) per se (Phelps, 2006), a notion that is more

firmly established in nonhuman animal studies (McGaugh,

2004). In these studies, the effects of emotion on memory have



copyright (2007).

Figure 4: Nonsocial and socialfear learning in humans. Anindividual learns to fear a CSthrough its pairing with (a) anelectric shock to the wrist (fearconditioning), (b) a learningmodel’s expression of distress(observational fear learning), and(c) verbal information about itsaversive qualities (instructedfear). Reprinted by permissionfrom Macmillan Publishers Ltd:Nature Neuroscience (Olsson andPhelps, 2007), copyright (2007).

been revealed by a vast array of experimental manipulations,

including inhibitory avoidance training, contextual fear

conditioning, cued fear conditioning, water-maze spatial and cued

training, among others. Typically, the effects of emotion on memory

are investigated via drug administration, including agonists and

antagonists of specific brain receptors. For instance, in one experiment,

rats were trained to swim to an escape platform after being placed in a

water tank (Packard et al., 1994). To mimic the effects of arousal, a

group of animals received an injection of d-amphetamine immediately

after training; a control group received a saline injection. Behavioral

testing revealed that d-amphetamine administration in the amygdala

enhanced memory both on a spatial task and on a non-spatial cued task.

A growing body of animal studies strongly supports a model in which

emotion influences memory by modulating memory storage (McGaugh,

2004). In particular, the amygdala and the closely associated basal

forebrain system involving the stria terminalis appear to play a major

role in this modulatory process. These structures are thought to play a

central role on memory consolidation by modulating activation in a

network of brain regions, including the hippocampus, which is centrally

involved in memory formation, but also additional brain structures,

such as the nucleus accumbens, caudate nucleus, entorhinal cortex, in

addition to other cortical regions (McGaugh, 2002) ( Figure 5).

Behavioral inhibition and working memory

An important dimension of cognition involves behavioral inhibition.

Response inhibition, namely the processes required to cancel an

intended action, is believed to involve control regions in prefrontal

cortex (e.g., dorsolateral prefrontal cortex, anterior cingulate cortex,

and inferior frontal cortex) (Rubia et al., 2003; Aron et al., 2004).

Response inhibition is often investigated by using so-called go/no-go

tasks in which subjects are asked to execute a motor response when

shown the go stimulus (e.g., press a key as fast as possible when you see

a letter stimulus), but to withhold the response when shown the no-go

stimulus (e.g., do not respond when you see the letter Y). Typically, the

go and no-go stimuli are shown as part of a rapid stream of stimuli (e.g.,

a sequence of letters). A recent study investigated the interaction

between the processing of emotional words and response inhibition (Goldstein et al., 2007). Response inhibition

following negative words (e.g., worthless) engaged the dorsolateral prefrontal cortex. Interestingly, this region

was not recruited by negative valence or inhibitory task demands per se; instead, the dorsolateral cortex was

sensitive to the explicit interaction between behavioral inhibition and the processing of negatively valenced

words.

Working memory, another important cognitive operation, involves the maintenance and updating of



Figure 5: Projections from the basolateral complex of the amygdala toother brain areas involved in memory consolidation. Reprinted fromMcGaugh (2002), Trends Neurosci(http://www.sciencedirect.com/science/journal/01662236) ,Memory consolidation and the amygdala: a systems perspective,copyright (2002), with permission from Elsevier.

information in mind when the information is no longer available to sensory systems (e.g., when keeping a phone

number in mind for a few seconds before dialing the number). Evidence for cognitive-emotional interaction

comes from working memory studies, too. For instance, when participants were asked to keep in mind neutral or

emotional pictures, maintenance-related activity in dorsolateral prefrontal cortex was modulated by the valence

of the picture, with pleasant pictures enhancing activity and unpleasant pictures decreasing activity relative to

neutral ones (Perlstein et al., 2002). Interestingly, emotional pictures did not affect dorsolateral responses

during a second experimental condition during which participants were not required to keep information in

mind, indicating that the modulation of

sustained activity by emotional valence

was particular to the experimental

context requiring active maintenance.

In another study, participants watched

short videos intended to induce

emotional states (e.g., clips from

uplifting or sad movies), after which

they performed challenging working

memory tasks (Gray et al., 2002).

Bilateral lateral prefrontal cortex

activity reflected equally the emotional

and working memory task components

( Figure 6). In other words, prefrontal

activity did not stem from the working

memory task alone or by the mood

ensuing from the viewing of the video,

but resulted from an interaction

between cognition and emotion.

Impact of cognition onemotion

Although this short review focuses on

the impact of emotional content on

cognitive functions, here we briefly

discuss another important line of

studies that has investigated cognitive-emotional interactions, namely, cognitive emotion regulation (Ochsner

and Gross, 2005; Ochsner and Gross, 2008). A particularly informative regulation strategy is “cognitive

reappraisal”, which involves rethinking the meaning of affectively charged stimuli or events in terms that alter

their emotional impact. Reappraisal appears to depend upon interactions between prefrontal and cingulate

regions that are frequently implicated in cognitive control and systems like the amygdala and insula that have

been implicated in emotional responding. Interestingly, having the goal to think about stimuli in ways that

maintain or increase emotion may boost amygdala activity whereas having the goal to decrease emotion may

diminish it. Furthermore, changes in emotional experience and autonomic responding may correlate with the

concomitant rise or fall of prefrontal and/or amygdala activity. Although much of the work on the cognitive



Figure 6: Emotion-cognition interaction inprefrontal cortex. Lateral prefrontal activityreflected equally the emotional and workingmemory task components, revealing the integrationof emotional and cognitive processes in prefrontalcortex. Adapted with permission from the NationalAcademy of Sciences: Gray et al. (2002), copyright(2002).

regulation of emotion has relied on a relatively strict separation between cognition and emotion, which are in

this context viewed as engaged in tug-of-war for the control of behavior, this framework is likely overly

simplistic. As proposed by Ochsner and Gross (2008), a more fruitful approach will entail developing an

integrated framework for specifying what combinations of interacting subsystems are involved in emotional

responding, as individuals exert varying degrees and types of regulatory control over their emotions.

Anatomical basis for cognitive-emotional interactions

In attempting to understand the relationship between emotion and cognition, it is important to consider

anatomical information. Advances in our

understanding of brain connectivity suggest that a

given brain region is only a few synapses away from

every other brain region(Sporns et al., 2004; Sporns

and Zwi, 2004). Indeed, it appears that the brain is

configured according to a small-world topology in

which the path length between nodes is small –

typically, cortical areas are connected directly or via

just one or two intermediate areas (Hilgetag et al.,

2000; Sporns et al., 2000) – and nodes are highly

clustered (Sporns, 2006). Thus, a careful consideration

of brain connectivity is informative in understanding

potential cognitive-emotional interactions.

In the past decade, several quantitative analyses of

brain connectivity have been undertaken (Young et al.,

1994; Stephan et al., 2000). Not surprisingly,

prefrontal areas are among those most distant from the

sensory periphery, suggesting that they receive highly-

processed and integrated sensory information. Such

potential insulation of the prefrontal cortex from the

periphery is thought to be a key anatomical feature of this region and presumably confers the primate brain with

a greater degree of flexibility (Mesulam, 2002). Highly processed information would also be able to support

more abstract processing that is required for cognition. Interestingly, the amygdala, a region often linked to

emotional processing, appears to be equally removed from the sensory periphery – although in some species,

direct sensory thalamic projections may be present (LeDoux, 1996). In addition, the amygdala makes very

widespread projections. Overall, it appears that the amygdala is very well situated to integrate and distribute

information ( Figure 7).

It is also instructive to consider the connectivity of the hypothalamus (Risold et al., 1997), as it has been long

recognized for its importance in emotional behaviours (Swanson, 2000, 2003). In particular, via its descending

connections that innervate brainstem motor systems, this structure is thought to play a key role in the

implementation of goal-directed behaviors. Hypothalamic signals also can be conveyed to the cortex, mostly by

way of the thalamus. Critically, prefrontal cortical territories project directly to the hypothalamus. Thus, the



Figure 7: Brain connectivity graph. Quantitative analysis of brain connectivity revealsseveral clusters of highly interconnected regions (represented by different colors). Inthis analysis, the amygdala (Amyg, centre of figure) was connected to all but 8 corticalareas. These connections involved multiple region clusters, suggesting that theamygdala is not only highly connected, but that its connectivity topology might beconsistent with that of a hub that links multiple functional clusters. In this manner,the amygdala may be important for the integration of cognitive and emotionalinformation. Figure labels represent different cortical areas with the exception of Hipp(hippocampus) and Amyg, which represent subcortical areas. Figure reproduced fromYoung et al. (1994) with permission from Freund Publishing House Ltd. Analysis ofconnectivity: Neural systems in the cerebral cortex, Reviews in the Neurosciences;copyright (1994).

hypothalamus appears to be organized in such a way that it can generate both relatively reflexive behaviors and

behaviors that are voluntarily triggered by inputs from the cerebral cortex (Swanson, 2000). Overall, this

structure appears to be connected with all levels of the nervous system, including the neocortex (Swanson,

2000), enabling important hypothalamic regulatory signals to have widespread effects on the brain.

It is also important to consider the role of the ascending systems. For instance, the basal nucleus of Maynert is a

major part of the so-

called magnocellular

basal forebrain system

(Heimer and Van

Hoesen, 2006). The

projections from this

system reach all parts

of the cortical mantle

(Heimer and Van

Hoesen, 2006), and

are involved in cortical

plasticity in sensory

cortex in the context of

classical conditioning

(Weinberger, 1995), in

addition to arousal

and attention

mechanisms (see

citations in (Sarter and

Bruno, 2000; Heimer

and Van Hoesen,

2006)). In particular,

basal forebrain

corticopetal

cholinergic projections

appear to be crucial

for diverse attentional

functions, including

sustained, selective,

and divided attention

(Sarter and Bruno,

1999; Sarter et al.,

1999; Sarter and Bruno, 2000). Of importance in the present context, the basal forebrain receives both cortical

and amygdala inputs (for citations, see (Sarter and Bruno, 2000)). Notably, recent anatomical evidence suggests

the existence of specific topographically organized prefrontal-basal forebrain-prefrontal loops (Zaborszky et al.,

1999; Zaborszky, 2002; Zaborszky et al., 2005), so that specific prefrontal cortical targets of the basal forebrain

connect back to sites from which the corticopetal fibers originate. Such loops provide a direct substrate for



cognitive-emotional integration, for example by allowing amygdala signals to be broadcast widely, including to

frontoparietal regions known to be important for the control of attention. More generally, the overall

anatomical arrangement of the basal forebrain may involve multiple functional-anatomical macrosystems

(Alheid and Heimer, 1988; Zahm, 2006) with wide-ranging effects on brain computations and important clinical

implications (Alheid and Heimer, 1988; Sarter and Bruno, 1999). In summary, the picture that emerges from

anatomical connectivity data suggests a remarkable potential for integration of information.

Conclusion: from interactions to integration

Historically, emotion and cognition have been viewed as separate entities. One factor that may have contributed

to this separation in the past century is methodological. For instance, data arising from single-unit or lesion

studies usually allow the researcher to only derive conclusions concerning the specific areas being targeted.

Research in the past two decades suggests, however, that such a view is likely deficient and that, in order to

understand how complex behaviors are carried out in the brain, an understanding of the interactions between

the two may be indispensable. Indeed, some studies have suggested that it may be important to go beyond

understanding interactions, some of which are suggested to be mutually antagonistic, to understanding how

cognition and emotion are effectively integrated in the brain. As stated recently, at some point of processing

functional specialization is lost, and emotion and cognition conjointly and equally contribute to the control of

thought and behavior (Gray et al., 2002). While these statements were offered as a summary of specific findings

concerning working memory performance following mood induction (see above), they may aptly characterize a

vast array of real-world situations. In other words, whereas many behaviors may be reasonably well

characterized in terms of cognitive-emotional interactions such that emotion and cognition are partly separable,

in many situations, true integration of emotion and cognition may also take place ( Figure 8). The latter further

blurs the distinction between cognition and emotion. See Duncan and Barrett (2007) for a similar view.

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Figure 8: Potential relationship betweenanatomical sites, neural computations andbehaviors. Brain areas (for example, A2), whichare connected to form networks (ellipses), areinvolved in multiple neural computations (forexample, NC2, NC3 and NC4) and specificcomputations (for example, NC4) are carried out byseveral areas (for example, A2 and A3). Therefore,the structure–function mapping is both one-to-many and many-to-one; in other words, many-to-many. Multiple neural computations underliebehavior. Each behavior has both affective andcognitive components, indicated by the affectiveand cognitive axes. Note that the axes are notorthogonal, indicating that the dimensions are notindependent from each other. Brain areas with ahigh degree of connectivity (hubs) may be especiallyimportant for regulating the flow and interaction ofinformation between regions. Reprinted bypermission from Macmillan Publishers Ltd: NatureNeuroscience (Pessoa, 2008), copyright (2008).See (Mesulam, 1990) for a related scheme.

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Recommended reading

Bishop, S.J. Neurocognitive mechanisms of anxiety: an integrative account. Trends Cogn Sci 11, 307-16

(2007).

Damasio, A.R. Descartes' error: Emotion, reason, and the human brain (G.P. Putnam, New York, 1994).

Damasio, A.R. The feeling of what happens: body and emotion in the making of consciousness (Harcourt

Brace, New York, 1999).

Dolan, R. Emotion, cognition, and behavior. Science 298, 1191-1194 (2003).

Duncan, S. & Barrett, L.F. Affect is a form of cognition: A neurobiological analysis. Cognition and Emotion

21, 1184-1211 (2007).

LeDoux, J.E. The emotional brain (Simon & Schuster, New York, 1996).

Lewis, M.D. Bridging emotion theory and neurobiology through dynamic systems modeling. Behav Brain Sci

28, 169-94; discussion 194-245 2005).

Pessoa, L. On the relationship between emotion and cognition. Nat Rev Neurosci 9, 148-58 (2008).

Phelps, E.A. Emotion and cognition: insights from studies of the human amygdala. Annu Rev Psychol 57, 27-

53 (2006).

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Netw 18, 317-52 (2005).



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