Cognitive Science

First published Mon Sep 23, 1996; substantive revision Tue Jan 31, 2023

Cognitive science is the interdisciplinary study of mind andintelligence, embracing philosophy, psychology, artificialintelligence, neuroscience, linguistics, and anthropology. Itsintellectual origins are in the mid-1950s when researchers in severalfields began to develop theories of mind based on complexrepresentations and computational procedures. Its organizationalorigins are in the mid-1970s when the Cognitive Science Society wasformed and the journalCognitive Science began. Since then,more than one hundred universities in North America, Europe, Asia, andAustralia have established cognitive science programs, and many othershave instituted courses in cognitive science.

1. History

Attempts to understand the mind and its operation go back at least tothe Ancient Greeks, when philosophers such as Plato and Aristotletried to explain the nature of human knowledge. The study of mindremained the province of philosophy until the nineteenth century, whenexperimental psychology developed. Wilhelm Wundt and his studentsinitiated laboratory methods for studying mental operations moresystematically. Within a few decades, however, experimental psychologybecame dominated bybehaviorism, a view that virtually denied the existence of mind. According tobehaviorists such as J. B. Watson, psychology should restrict itselfto examining the relation between observable stimuli and observablebehavioral responses. Talk of consciousness and mental representationswas banished from respectable scientific discussion. Especially inNorth America, behaviorism dominated the psychological scene throughthe 1950s.

Around 1956, the intellectual landscape began to change dramatically.George Miller summarized numerous studies which showed that thecapacity of human thinking is limited, with short-term memory, forexample, limited to around seven items. He proposed that memorylimitations can be overcome by recoding information into chunks,mental representations that require mental procedures for encoding anddecoding the information. At this time, primitive computers had beenaround for only a few years, but pioneers such as John McCarthy,Marvin Minsky, Allen Newell, and Herbert Simon were founding the fieldofartificial intelligence. In addition, Noam Chomsky rejected behaviorist assumptions aboutlanguage as a learned habit and proposed instead to explain languagecomprehension in terms of mental grammars consisting of rules. The sixthinkers mentioned in this paragraph can be viewed as the founders ofcognitive science.

2. Methods

Cognitive science has unifying theoretical ideas, but we have toappreciate the diversity of outlooks and methods that researchers indifferent fields bring to the study of mind and intelligence. Althoughcognitive psychologists today often engage in theorizing andcomputational modeling, their primary method is experimentation withhuman participants. People, often undergraduates satisfying courserequirements, are brought into the laboratory so that different kindsof thinking can be studied under controlled conditions. For example,psychologists have experimentally examined the kinds of mistakespeople make in deductive reasoning, the ways that people form andapply concepts, the speed of people thinking with mental images, andthe performance of people solving problems using analogies. Ourconclusions about how the mind works must be based on more than“common sense” and introspection, since these can give amisleading picture of mental operations, many of which are notconsciously accessible. Increasingly, psychologists draw theirexperimental participants from Amazon’s Mechanical Turk and fromculturally diverse sources. Psychological experiments that carefullyapproach mental operations from diverse directions are thereforecrucial for cognitive science to be scientific. Experimentation isalso a methodology employed byexperimental philosophy.

Although theory without experiment is empty, experiment without theoryis blind. To address the crucial questions about the nature of mind,the psychological experiments need to be interpretable within atheoretical framework that postulates mental representations andprocedures. One of the best ways of developing theoretical frameworksis by forming and testing computational models intended to beanalogous to mental operations. To complement psychologicalexperiments on deductive reasoning, concept formation, mental imagery,and analogical problem solving, researchers have developedcomputational models that simulate aspects of human performance.Designing, building, and experimenting with computational models isthe central method of artificial intelligence (AI), the branch ofcomputer science concerned with intelligent systems. Ideally incognitive science, computational models and psychologicalexperimentation go hand in hand, but much important work in AI hasexamined the power of different approaches to knowledge representationin relative isolation from experimental psychology.

While some linguists do psychological experiments or developcomputational models, most currently use different methods. Forlinguists in the Chomskian tradition, the main theoretical task is toidentify grammatical principles that provide the basic structure ofhuman languages. Identification takes place by noticing subtledifferences between grammatical and ungrammatical utterances. InEnglish, for example, the sentences “She hit the ball” and“What do you like?” are grammatical, but “She thehit ball” and “What does you like?” are not. Agrammar of English will explain why the former are acceptable but notthe latter. An alternative approach, cognitive linguistics, puts lessemphasis on syntax and more on semantics and concepts.

Like cognitive psychologists, neuroscientists often perform controlledexperiments, but their observations are very different, sinceneuroscientists are concerned directly with the nature of the brain.With nonhuman subjects, researchers can insert electrodes and recordthe firing of individual neurons. With humans for whom this techniquewould be too invasive, it is now common to use magnetic and positronscanning devices to observe what is happening in different parts ofthe brain while people are doing various mental tasks. For example,brain scans have identified the regions of the brain involved inmental imagery and word interpretation. Additional evidence aboutbrain functioning is gathered by observing the performance of peoplewhose brains have been damaged in identifiable ways. A stroke, forexample, in a part of the brain dedicated to language can producedeficits such as the inability to utter sentences. Like cognitivepsychology, neuroscience is often theoretical as well as experimental,and theory development is frequently aided by developing computationalmodels of the behavior of groups of neurons.

Cognitive anthropology expands the examination of human thinking toconsider how thought works in different cultural settings. The studyof mind should obviously not be restricted to how English speakersthink but should consider possible differences in modes of thinkingacross cultures. Cognitive science is becoming increasingly aware ofthe need to view the operations of mind in particular physical andsocial environments. For cultural anthropologists, the main method isethnography, which requires living and interacting with members of aculture to a sufficient extent that their social and cognitive systemsbecome apparent. Cognitive anthropologists have investigated, forexample, the similarities and differences across cultures in words forcolors.

Traditionally, philosophers do not perform systematic empiricalobservations or construct computational models, although there hasbeen a rise in work in experimental philosophy. But philosophy remainsimportant to cognitive science because it deals with fundamentalissues that underlie the experimental and computational approach tomind. Abstract questions such as the nature of representation andcomputation need not be addressed in the everyday practice ofpsychology or artificial intelligence, but they inevitably arise whenresearchers think deeply about what they are doing. Philosophy alsodeals with general questions such as the relation of mind and body andwith methodological questions such as the nature of explanations foundin cognitive science. In addition, philosophy concerns itself withnormative questions about how people should think as well as withdescriptive ones about how they do. Besides the theoretical goal ofunderstanding human thinking, cognitive science can have the practicalgoal of improving it, which requires normative reflection on what wewant thinking to be. Philosophy of mind does not have a distinctmethod, but should share with the best theoretical work in otherfields a concern with empirical results.

In its weakest form, cognitive science is just the sum of the fieldsmentioned: psychology, artificial intelligence, linguistics,neuroscience, anthropology, and philosophy. Interdisciplinary workbecomes much more interesting when there is theoretical andexperimental convergence on conclusions about the nature of mind. Forexample, psychology and artificial intelligence can be combinedthrough computational models of how people behave in experiments. Thebest way to grasp the complexity of human thinking is to use multiplemethods, especially psychological and neurological experiments andcomputational models. Theoretically, the most fertile approach hasbeen to understand the mind in terms of representation andcomputation.

3. Representation and Computation

The central hypothesis of cognitive science is that thinking can bestbe understood in terms of representational structures in the mind andcomputational procedures that operate on those structures. While thereis much disagreement about the nature of the representations andcomputations that constitute thinking, the central hypothesis isgeneral enough to encompass the current range of thinking in cognitivescience, includingconnectionist theories which model thinking using artificial neural networks.

Most work in cognitive science assumes that the mind hasmental representations analogous to computer data structures, and computational proceduressimilar to computational algorithms. Cognitive theorists have proposedthat the mind contains such mental representations as logicalpropositions, rules, concepts, images, and analogies, and that it usesmental procedures such as deduction, search, matching, rotating, andretrieval. The dominant mind-computer analogy in cognitive science hastaken on a novel twist from the use of another analog, the brain.

Connectionists have proposed novel ideas about representation andcomputation that use neurons and their connections as inspirations fordata structures, and neuron firing and spreading activation asinspirations for algorithms. Cognitive science then works with acomplex 3-way analogy among the mind, the brain, and computers. Mind,brain, and computation can each be used to suggest new ideas about theothers. There is no single computational model of mind, sincedifferent kinds of computers and programming approaches suggestdifferent ways in which the mind might work. The computers that mostof us work with today are serial processors, performing oneinstruction at a time, but the brain and some recently developedcomputers are parallel processors, capable of doing many operations atonce.

A major trend in current cognitive science is the integration ofneuroscience with many areas of psychology, including cognitive,social, developmental, and clinical. This integration is partlyexperimental, resulting from an explosion of new instruments forstudying the brain, such as functional magnetic resonance imaging,transcranial magnetic stimulation, and optogenetics. The integrationis also theoretical, because of advances in understanding how largepopulations of neurons can perform tasks usually explained withcognitive theories of rules and concepts.

4. Theoretical Approaches

Here is a schematic summary of current theories about the nature ofthe representations and computations that explain how the mindworks.

4.1 Formal logic

Formal logic provides some powerful tools for looking at the nature ofrepresentation and computation. Propositional and predicate calculusserve to express many complex kinds of knowledge, and many inferencescan be understood in terms of logical deduction with inferences rulessuch as modus ponens. The explanation schema for the logical approachis:

Explanation target:
Why do people make the inferences they do?
Explanatory pattern:
People have mental representations similar to sentences inpredicate logic.
People have deductive and inductive procedures that operate onthose sentences.
The deductive and inductive procedures, applied to the sentences,produce the inferences.

It is not certain, however, that logic provides the core ideas aboutrepresentation and computation needed for cognitive science, sincemore efficient and psychologically natural methods of computation maybe needed to explain human thinking. (See the entry onlogic and artificial intelligence.)

4.2 Rules

Much of human knowledge is naturally described in terms of rules ofthe form IF … THEN …, and many kinds of thinking such asplanning can be modeled by rule-based systems. The explanation schemaused is:

Explanation target:
Why do people have a particular kind of intelligent behavior?
Explanatory pattern:
People have mental rules.
People have procedures for using these rules to search a space ofpossible solutions, and procedures for generating new rules.
Procedures for using and forming rules produce the behavior.

Computational models based on rules have provided detailed simulationsof a wide range of psychological experiments, from cryptarithmeticproblem solving to skill acquisition to language use. Rule-basedsystems have also been of practical importance in suggesting how toimprove learning and how to develop intelligent machine systems.

4.3 Concepts

Concepts, which partly correspond to the words in spoken and writtenlanguage, are an important kind of mental representation. There arecomputational and psychological reasons for abandoning the classicalview that concepts have strict definitions. Instead, concepts can beviewed as sets of typical features. Concept application is then amatter of getting an approximate match between concepts and the world.Schemas and scripts are more complex than concepts that correspond towords, but they are similar in that they consist of bundles offeatures that can be matched and applied to new situations. Theexplanatory schema used in concept-based systems is:

Explanatory target:
Why do people have a particular kind of intelligent behavior?
Explanation pattern:
People have a set of concepts, organized via kind and parthierarchies and other associations.
People have a set of procedures for concept application, includingspreading activation, matching, and inheritance.
The procedures applied to the concepts produce the behavior.
Concepts can be translated into rules, but they bundle informationdifferently than sets of rules, making possible differentcomputational procedures.

(See the entry onconcepts.)

4.4 Analogies

Analogies play an important role in human thinking, in areas asdiverse as problem solving, decision making, explanation, andlinguistic communication. Computational models simulate how peopleretrieve and map source analogs in order to apply them to targetsituations. The explanation schema for analogies is:

Explanation target:
Why do people have a particular kind of intelligent behavior?
Explanatory pattern:
People have verbal and visual representations of situations thatcan be used as cases or analogs.
People have processes of retrieval, mapping, and adaptation thatoperate on those analogs.
The analogical processes, applied to the representations ofanalogs, produce the behavior.

The constraints of similarity, structure, and purpose overcome thedifficult problem of how previous experiences can be found and used tohelp with new problems. Not all thinking is analogical, and usinginappropriate analogies can hinder thinking, but analogies can beeffective in applications such as education and design.

4.5 Images

Visual and other kinds of images play an important role in humanthinking. Pictorial representations capture visual and spatialinformation in a much more usable form than lengthy verbaldescriptions. Computational procedures well suited to visualrepresentations include inspecting, finding, zooming, rotating, andtransforming. Such operations can be very useful for generating plansand explanations in domains to which pictorial representations apply.The explanatory schema for visual representation is:

Explanation target:
Why do people have a particular kind of intelligent behavior?
Explanatory pattern:
People have visual images of situations.
People have processes such as scanning and rotation that operateon those images.
The processes for constructing and manipulating images produce theintelligent behavior.

Imagery can aid learning, and some metaphorical aspects of languagemay have their roots in imagery. Psychological experiments suggestthat visual procedures such as scanning and rotating employ imagery,and neurophysiological results confirm a close physical link betweenreasoning with mental imagery and perception. Imagery is not justvisual, but can also operate with other sensory experiences such ashearing, touch, smell, taste, pain, balance, nausea, fullness, andemotion.

4.6 Connectionism

Connectionist networks consisting of simple nodes and links are veryuseful for understanding psychological processes that involve parallelconstraint satisfaction. Such processes include aspects of vision,decision making, explanation selection, and meaning making in languagecomprehension. Connectionist models can simulate learning by methodsthat include Hebbian learning and backpropagation. The explanatoryschema for the connectionist approach is:

Explanation target:
Why do people have a particular kind of intelligent behavior?
Explanatory pattern:
People have representations that involve simple processing unitslinked to each other by excitatory and inhibitory connections.
People have processes that spread activation between the units viatheir connections, as well as processes for modifying theconnections.
Applying spreading activation and learning to the units producesthe behavior.

Simulations of various psychological experiments have shown thepsychological relevance of the connectionist models, which are,however, only very rough approximations to actual neural networks.(For more information, see the entry onconnectionism.)

4.7 Theoretical neuroscience

Theoretical neuroscience is the attempt to develop mathematical andcomputational theories and models of the structures and processes ofthe brains of humans and other animals. It differs from connectionismin trying to be more biologically accurate by modeling the behavior oflarge numbers of realistic neurons organized into functionallysignificant brain areas. Computational models of the brain have becomebiologically richer, both with respect to employing more realisticneurons such as ones that spike and have chemical pathways, and withrespect to simulating the interactions among different areas of thebrain such as the hippocampus and the cortex. These models are notstrictly an alternative to computational accounts in terms of logic,rules, concepts, analogies, images, and connections, but should meshwith them and show how mental functioning can be performed at theneural level. The explanatory schema for theoretical neuroscienceis:

Explanation target:
How does the brain carry out functions such as cognitivetasks?
Explanatory pattern:
The brain has neurons organized by synaptic connections intopopulations and brain areas.
The neural populations have spiking patterns that are transformedvia sensory inputs and the spiking patterns of other neuralpopulations.
Interactions of neural populations carry out functions includingcognitive tasks.

From the perspective of theoretical neuroscience, mentalrepresentations are patterns of neural activity, and inference istransformation of such patterns. (See the entries onneuroscience and theneuroscience of consciousness.)

4.8 Bayesian

Bayesian models are prominent in cognitive science, with applicationsto such psychological phenomena as learning, vision, motor control,language, and social cognition. They have also had effectiveapplications in robotics. The Bayesian approach assumes that cognitionis approximately optimal in accord with probability theory, especiallyBayes’ theorem, which says that the probability of a hypothesisgiven evidence is equal to the result of multiplying the priorprobability of the hypothesis by the conditional probability of theevidence given the hypothesis, all divided by the probability of theevidence. The explanatory schema for Bayesian cognition is:

Explanation target:
How does the mind carry out functions such as inference?
Explanatory pattern:
The mind has representations for statistical correlations andconditional probabilities.
The mind has the capacity for probabilistic computations such asapplications of Bayes’ theorem.
Applying probabilistic computations to statistical representationsaccomplishes mental tasks such as inference.

Although Bayesian methods have had impressive applications to a widerange of phenomena, their psychological plausibility is debatablebecause of assumptions about optimality and computations based onprobability theory.

4.9 Deep learning

Artificial intelligence has been a central part of cognitive since the1950s, and the most dramatic recent advances in AI have come from theapproach of deep learning, which has produced major breakthroughs infields that include game playing, object recognition, and translation.Deep learning builds on ideas from connectionism and theoreticalneuroscience, but uses neural networks with more layers and improvedalgorithms, benefitting from faster computers and large data bases ofexamples. Another important innovation is combining learning fromexamples with reinforcement learning, resulting by 2016 in theworld’s leading Go player, AlphaGo. Ideas from deep learning arespreading back into neuroscience and also beginning to influenceresearch in cognitive psychology. The explanatory schema for deeplearning is:

Explanation target:
How does the brain carry out functions such as cognitivetasks?
Explanatory pattern:
The brain has large numbers of neurons organized into 6–20layers.
The brain has powerful mechanisms for learning from examples andfor learning actions that are reinforced by their successes.
Applying learning mechanisms to layered neural networks makes themcapable of human and sometimes even super-human performance.

Although deep learning has produced dramatic improvements in some AIsystems, it is not clear how it can be applied to aspects of humanthought that include causal reasoning, imagery, emotion, and analogy.For further discussion, see Section 11 (on deep learning) of the entryonconnectionism.

4.10 Predictive processing and active inference

Predictive processing is an approach to theoretical neuroscience thatviews the brain as constantly generating and updating models of theenvironment in order to predict the results of perceptions andactions. Active inference is a version of predictive processing thathypothesizes that the brain uses Bayesian calculations to minimize“free energy” consisting of discrepancies betweenexpectations and actual observations. Organisms survive when brainsreduce prediction errors by changing their models of the environmentor by changing the environment through action.

The explanatory schema for active inference is:

Explanation target:
How does the brain function to support perception and action?
Explanatory pattern:
The brain is a prediction engine that uses probabilistic models toanticipate perceptions and the results of actions.
To reduce prediction error, the brain uses Bayesian updating tochange its models and uses actions to change its environment, e.g. bymoving.
Effective inference, perception, and action result from thesereductions in prediction errors.

Active inference is open to numerous challenges. Is brain functioningreally Bayesian updating rather than connectionist constraintsatisfaction or deep reinforcement learning? Can predictive processingsubsume other brain functions that include pattern recognition,explanation, emotional evaluation, memory, and communication? Doesactive inference explain high-level cognitive operations such ascausal reasoning, language, and creativity?

5. Philosophical Relevance

Some philosophy, in particular naturalistic philosophy of mind, ispart of cognitive science. But the interdisciplinary field ofcognitive science is relevant to philosophy in several ways. First,the psychological, computational, and other results of cognitivescience investigations have important potential applications totraditional philosophical problems in epistemology, metaphysics, andethics. Second, cognitive science can serve as an object ofphilosophical critique, particularly concerning the central assumptionthat thinking is representational and computational. Third and moreconstructively, cognitive science can be taken as an object ofinvestigation in the philosophy of science, generating reflections onthe methodology and presuppositions of the enterprise.

5.1 Philosophical Applications

Much philosophical research today is naturalistic, treatingphilosophical investigations as continuous with empirical work infields such as psychology. From a naturalistic perspective, philosophyof mind is closely allied with theoretical and experimental work incognitive science. Metaphysical conclusions about the nature of mindare to be reached, not by a priori speculation, but by informedreflection on scientific developments in fields such as psychology,neuroscience, and computer science. Similarly, epistemology is not astand-alone conceptual exercise, but depends on and benefits fromscientific findings concerning mental structures and learningprocedures. Ethics can benefit by using greater understanding of thepsychology of moral thinking to bear on ethical questions such as thenature of deliberations concerning right and wrong. Here are somephilosophical problems to which ongoing developments in cognitivescience are highly relevant. Links are provided to other relevantarticles in thisEncyclopedia.

Innateness. To what extent is knowledge innate or acquired by experience? Ishuman behavior shaped primarily by nature or nurture?
Language of thought. Does the human brain operate with a language-like code or with a moregeneralconnectionist architecture? What is the relation between symbolic cognitive modelsusing rules and concepts and sub-symbolic models using neuralnetworks?
Mental imagery. Do human minds think with visual and other kinds of imagery, or onlywith language-like representations?
Folk psychology. Does a person’s everyday understanding of other people consistof having a theory of mind, or of merely being able to simulatethem?
Meaning. How domental representations acquire meaning ormental content? To what extent does the meaning of a representation depend on itsrelation to other representations, its relation to the world, and itsrelation to a community of thinkers?
Mind-brain identity. Are mental states brain states? Or can they bemultiply realized by other material states? What is the relation between psychology andneuroscience? Ismaterialism true?
Free will. Is human action free or merely caused by brain events?
Moral psychology. How do minds/brains make ethical judgments?
The meaning of life. How can minds construed naturalistically as brains find value andmeaning?
Emotions. What are emotions, and what role do they play in thinking?
Consciousness. Can conscious experience be scientifically explained, for example bytheneuroscience of consciousness?
Mental disorder. What are mental disorders, and how are psychological and neuralprocesses relevant to their explanation and treatment?
Perception and reality. How do minds/brains form and evaluate representations of the externalworld?
Perception and cognition. How does perception differ from other kinds of cognition with respectto representational format and justification?
Realism. Is cognitive science consistent with views that minds grasp the realworld? Could minds be computer simulations? Is virtual reality a kindof reality?
Information. How does cognitive science illuminate the operations of informationand misinformation in minds and societies?
Social science. How do explanations of the operations of minds interact withexplanations of the operations of groups and societies?

Additional philosophical problems arise from examining thepresuppositions of current approaches to cognitive science.

5.2 Critique of Cognitive Science

The claim that human minds work by representation and computation isan empirical conjecture and might be wrong. Although thecomputational-representational approach to cognitive science has been successful in explaining manyaspects of human problem solving, learning, and language use, somephilosophical critics have claimed that this approach is fundamentallymistaken. Critics of cognitive science have offered such challengesas:

Theemotion challenge: Cognitive science neglects the important role of emotionsin human thinking.
Theconsciousness challenge: Cognitive science ignores the importance of consciousnessin human thinking.
The world challenge: Cognitive science disregards the significantrole of physical environments in human thinking, which is embedded inand extended into the world.
The body challenge: Cognitive science neglects the contribution ofembodiment to human thought and action.
The dynamical systems challenge: The mind is a dynamical system,not a computational system.
The social challenge: Human thought is inherently social in waysthat cognitive science ignores.
The mathematics challenge: Mathematical results show that humanthinking cannot be computational in the standard sense, so the brainmust operate differently, perhaps as a quantum computer.
The interdisciplinarity challenge: Cognitive science has failed togo beyond multidisciplinary interactions by developing a core theorythat unifies work in its many disciplines.

The first five challenges are increasingly addressed by advances thatexplain emotions, consciousness, action, and embodiment in terms ofneural mechanisms. The social challenge is being met by thedevelopment of computational models of interacting agents. Themathematics challenge is based on misunderstanding ofGödel’s theorem and on exaggeration of the relevance of quantum theory to neuralprocesses. Response to the interdisciplinary challenge must recognizethat cognitive science still has many contending theoreticalapproaches, without the unification that theories of evolution andgenetics provide for biology. Nevertheless, interactions amongpsychology, neuroscience, linguistics, philosophy, anthropology, andcomputer modeling have contributed to theoretical and empiricalprogress concerning many aspects of cognition. For example,computational philosophy uses programmed models to address questions in epistemology, ethics,and other areas of philosophy.

5.3 Philosophy of Cognitive Science

Cognitive science raises many interesting methodological questionsthat are worthy of investigation by philosophers of science. What isthe nature of representation? What role do computational models playin the development of cognitive theories? What is the relation amongapparently competing accounts of mind involving symbolic processing,neural networks, and dynamical systems? What is the relation among thevarious fields of cognitive science such as psychology, linguistics,and neuroscience? Are psychological phenomena subject to reductionistexplanations via neuroscience? Are levels of explanation bestcharacterized in terms of ontological levels (molecular, neural,psychological, social) or methodological ones (computational,algorithmic, physical)?

The increasing prominence of neural explanations in cognitive, social,developmental, and clinical psychology raises important philosophicalquestions aboutexplanation andreduction. Anti-reductionism, according to which psychological explanations arecompletely independent of neurological ones, is becoming increasinglyimplausible, but it remains controversial to what extent psychologycan be reduced to neuroscience and molecular biology. Crucial toanswering questions about the nature of reduction are answers toquestions about the nature of explanation. Explanations in psychology,neuroscience, and biology in general are plausibly viewed asdescriptions ofmechanisms, which are combinations of connected parts that interact to produceregular changes. In psychological explanations, the parts are mentalrepresentations that interact by computational procedures to producenew representations. In neuroscientific explanations, the parts areneural populations that interact by electrochemical processes toproduce new neural activity that leads to actions. If progress intheoretical neuroscience continues, it should become possible to tiepsychological to neurological explanations by showing how mentalrepresentations such as concepts are constituted by activities inneural populations, and how computational procedures such as spreadingactivation among concepts are carried out by neural processes.

The increasing integration of cognitive psychology with neuroscienceprovides evidence for themind-brain identity theory according to which mental processes are neural, representational, andcomputational. Other philosophers dispute such identification on thegrounds that minds are embodied in biological systems and extendedinto the world. However, moderate claims about embodiment areconsistent with the identity theory because brain representationsoperate in several modalities (e.g. visual and motor) that enableminds to deal with the world. Another materialist alternative tomind-brain identity comes from recognizing that explanations of mindemploy molecular and social mechanisms as well as neural andrepresentational ones.

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