## 1. History



Attempts to understand the mind and its operation go back at least to
the Ancient Greeks, when philosophers such as Plato and Aristotle
tried to explain the nature of human knowledge. The study of mind
remained the province of philosophy until the nineteenth century, when
experimental psychology developed. Wilhelm Wundt and his students
initiated laboratory methods for studying mental operations more
systematically. Within a few decades, however, experimental psychology
became dominated by
 behaviorism,
 a view that virtually denied the existence of mind. According to
behaviorists such as J. B. Watson, psychology should restrict itself
to examining the relation between observable stimuli and observable
behavioral responses. Talk of consciousness and mental representations
was banished from respectable scientific discussion. Especially in
North America, behaviorism dominated the psychological scene through
the 1950s.



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


## 2. Methods



Cognitive science has unifying theoretical ideas, but we have to
appreciate the diversity of outlooks and methods that researchers in
different fields bring to the study of mind and intelligence. Although
cognitive psychologists today often engage in theorizing and
computational modeling, their primary method is experimentation with
human participants. People, often undergraduates satisfying course
requirements, are brought into the laboratory so that different kinds
of thinking can be studied under controlled conditions. For example,
psychologists have experimentally examined the kinds of mistakes
people make in deductive reasoning, the ways that people form and
apply concepts, the speed of people thinking with mental images, and
the performance of people solving problems using analogies. Our
conclusions about how the mind works must be based on more than
"common sense" and introspection, since these can give a
misleading picture of mental operations, many of which are not
consciously accessible. Increasingly, psychologists draw their
experimental participants from Amazon's Mechanical Turk and from
culturally diverse sources. Psychological experiments that carefully
approach mental operations from diverse directions are therefore
crucial for cognitive science to be scientific. Experimentation is
also a methodology employed by
 experimental philosophy.




Although theory without experiment is empty, experiment without theory
is blind. To address the crucial questions about the nature of mind,
the psychological experiments need to be interpretable within a
theoretical framework that postulates mental representations and
procedures. One of the best ways of developing theoretical frameworks
is by forming and testing computational models intended to be
analogous to mental operations. To complement psychological
experiments on deductive reasoning, concept formation, mental imagery,
and analogical problem solving, researchers have developed
computational models that simulate aspects of human performance.
Designing, building, and experimenting with computational models is
the central method of artificial intelligence (AI), the branch of
computer science concerned with intelligent systems. Ideally in
cognitive science, computational models and psychological
experimentation go hand in hand, but much important work in AI has
examined the power of different approaches to knowledge representation
in relative isolation from experimental psychology.



While some linguists do psychological experiments or develop
computational models, most currently use different methods. For
linguists in the Chomskian tradition, the main theoretical task is to
identify grammatical principles that provide the basic structure of
human languages. Identification takes place by noticing subtle
differences between grammatical and ungrammatical utterances. In
English, for example, the sentences "She hit the ball" and
"What do you like?" are grammatical, but "She the
hit ball" and "What does you like?" are not. A
grammar of English will explain why the former are acceptable but not
the latter. An alternative approach, cognitive linguistics, puts less
emphasis on syntax and more on semantics and concepts.



Like cognitive psychologists, neuroscientists often perform controlled
experiments, but their observations are very different, since
neuroscientists are concerned directly with the nature of the brain.
With nonhuman subjects, researchers can insert electrodes and record
the firing of individual neurons. With humans for whom this technique
would be too invasive, it is now common to use magnetic and positron
scanning devices to observe what is happening in different parts of
the brain while people are doing various mental tasks. For example,
brain scans have identified the regions of the brain involved in
mental imagery and word interpretation. Additional evidence about
brain functioning is gathered by observing the performance of people
whose brains have been damaged in identifiable ways. A stroke, for
example, in a part of the brain dedicated to language can produce
deficits such as the inability to utter sentences. Like cognitive
psychology, neuroscience is often theoretical as well as experimental,
and theory development is frequently aided by developing computational
models of the behavior of groups of neurons.



Cognitive anthropology expands the examination of human thinking to
consider how thought works in different cultural settings. The study
of mind should obviously not be restricted to how English speakers
think but should consider possible differences in modes of thinking
across cultures. Cognitive science is becoming increasingly aware of
the need to view the operations of mind in particular physical and
social environments. For cultural anthropologists, the main method is
ethnography, which requires living and interacting with members of a
culture to a sufficient extent that their social and cognitive systems
become apparent. Cognitive anthropologists have investigated, for
example, the similarities and differences across cultures in words for
colors.



Traditionally, philosophers do not perform systematic empirical
observations or construct computational models, although there has
been a rise in work in experimental philosophy. But philosophy remains
important to cognitive science because it deals with fundamental
issues that underlie the experimental and computational approach to
mind. Abstract questions such as the nature of representation and
computation need not be addressed in the everyday practice of
psychology or artificial intelligence, but they inevitably arise when
researchers think deeply about what they are doing. Philosophy also
deals with general questions such as the relation of mind and body and
with methodological questions such as the nature of explanations found
in cognitive science. In addition, philosophy concerns itself with
normative questions about how people should think as well as with
descriptive ones about how they do. Besides the theoretical goal of
understanding human thinking, cognitive science can have the practical
goal of improving it, which requires normative reflection on what we
want thinking to be. Philosophy of mind does not have a distinct
method, but should share with the best theoretical work in other
fields a concern with empirical results.



In its weakest form, cognitive science is just the sum of the fields
mentioned: psychology, artificial intelligence, linguistics,
neuroscience, anthropology, and philosophy. Interdisciplinary work
becomes much more interesting when there is theoretical and
experimental convergence on conclusions about the nature of mind. For
example, psychology and artificial intelligence can be combined
through computational models of how people behave in experiments. The
best way to grasp the complexity of human thinking is to use multiple
methods, especially psychological and neurological experiments and
computational models. Theoretically, the most fertile approach has
been to understand the mind in terms of representation and
computation.


## 3. Representation and Computation



The central hypothesis of cognitive science is that thinking can best
be understood in terms of representational structures in the mind and
computational procedures that operate on those structures. While there
is much disagreement about the nature of the representations and
computations that constitute thinking, the central hypothesis is
general enough to encompass the current range of thinking in cognitive
science, including
 connectionist
 theories which model thinking using artificial neural networks.



Most work in cognitive science assumes that the mind has
 mental representations
 analogous to computer data structures, and computational procedures
similar to computational algorithms. Cognitive theorists have proposed
that the mind contains such mental representations as logical
propositions, rules, concepts, images, and analogies, and that it uses
mental procedures such as deduction, search, matching, rotating, and
retrieval. The dominant mind-computer analogy in cognitive science has
taken on a novel twist from the use of another analog, the brain.



Connectionists have proposed novel ideas about representation and
computation that use neurons and their connections as inspirations for
data structures, and neuron firing and spreading activation as
inspirations for algorithms. Cognitive science then works with a
complex 3-way analogy among the mind, the brain, and computers. Mind,
brain, and computation can each be used to suggest new ideas about the
others. There is no single computational model of mind, since
different kinds of computers and programming approaches suggest
different ways in which the mind might work. The computers that most
of us work with today are serial processors, performing one
instruction at a time, but the brain and some recently developed
computers are parallel processors, capable of doing many operations at
once.



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


## 4. Theoretical Approaches



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


### 4.1 Formal logic



Formal logic
 provides some powerful tools for looking at the nature of
representation and computation. Propositional and predicate calculus
serve to express many complex kinds of knowledge, and many inferences
can be understood in terms of logical deduction with inferences rules
such as modus ponens. The explanation schema for the logical approach
is:



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



It is not certain, however, that logic provides the core ideas about
representation and computation needed for cognitive science, since
more efficient and psychologically natural methods of computation may
be needed to explain human thinking. (See the entry on
 logic and artificial intelligence.)


### 4.2 Rules



Much of human knowledge is naturally described in terms of rules of
the form IF ... THEN ..., and many kinds of thinking such as
planning can be modeled by rule-based systems. The explanation schema
used 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 of
> possible solutions, and procedures for generating new rules.
> * Procedures for using and forming rules produce the behavior.
> 
> 
> 



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


### 4.3 Concepts



Concepts, which partly correspond to the words in spoken and written
language, are an important kind of mental representation. There are
computational and psychological reasons for abandoning the classical
view that concepts have strict definitions. Instead, concepts can be
viewed as sets of typical features. Concept application is then a
matter of getting an approximate match between concepts and the world.
Schemas and scripts are more complex than concepts that correspond to
words, but they are similar in that they consist of bundles of
features that can be matched and applied to new situations. The
explanatory 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 part
> hierarchies and other associations.
> * People have a set of procedures for concept application, including
> spreading activation, matching, and inheritance.
> * The procedures applied to the concepts produce the behavior.
> * Concepts can be translated into rules, but they bundle information
> differently than sets of rules, making possible different
> computational procedures.
> 
> 
> 

 (See the entry on
 concepts.)
 
### 4.4 Analogies



Analogies play an important role in human thinking, in areas as
diverse as problem solving, decision making, explanation, and
linguistic communication. Computational models simulate how people
retrieve and map source analogs in order to apply them to target
situations. 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 that
> can be used as cases or analogs.
> * People have processes of retrieval, mapping, and adaptation that
> operate on those analogs.
> * The analogical processes, applied to the representations of
> analogs, produce the behavior.
> 
> 
> 



The constraints of similarity, structure, and purpose overcome the
difficult problem of how previous experiences can be found and used to
help with new problems. Not all thinking is analogical, and using
inappropriate analogies can hinder thinking, but analogies can be
effective in applications such as education and design.


### 4.5 Images



Visual and other kinds of images play an important role in human
thinking. Pictorial representations capture visual and spatial
information in a much more usable form than lengthy verbal
descriptions. Computational procedures well suited to visual
representations include inspecting, finding, zooming, rotating, and
transforming. Such operations can be very useful for generating plans
and 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 operate
> on those images.
> * The processes for constructing and manipulating images produce the
> intelligent behavior.
> 
> 
> 



Imagery can aid learning, and some metaphorical aspects of language
may have their roots in imagery. Psychological experiments suggest
that visual procedures such as scanning and rotating employ imagery,
and neurophysiological results confirm a close physical link between
reasoning with mental imagery and perception. Imagery is not just
visual, but can also operate with other sensory experiences such as
hearing, touch, smell, taste, pain, balance, nausea, fullness, and
emotion.


### 4.6 Connectionism



Connectionist networks consisting of simple nodes and links are very
useful for understanding psychological processes that involve parallel
constraint satisfaction. Such processes include aspects of vision,
decision making, explanation selection, and meaning making in language
comprehension. Connectionist models can simulate learning by methods
that include Hebbian learning and backpropagation. The explanatory
schema 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 units
> linked to each other by excitatory and inhibitory connections.
> * People have processes that spread activation between the units via
> their connections, as well as processes for modifying the
> connections.
> * Applying spreading activation and learning to the units produces
> the behavior.
> 
> 
> 



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


### 4.7 Theoretical neuroscience



Theoretical neuroscience is the attempt to develop mathematical and
computational theories and models of the structures and processes of
the brains of humans and other animals. It differs from connectionism
in trying to be more biologically accurate by modeling the behavior of
large numbers of realistic neurons organized into functionally
significant brain areas. Computational models of the brain have become
biologically richer, both with respect to employing more realistic
neurons such as ones that spike and have chemical pathways, and with
respect to simulating the interactions among different areas of the
brain such as the hippocampus and the cortex. These models are not
strictly an alternative to computational accounts in terms of logic,
rules, concepts, analogies, images, and connections, but should mesh
with them and show how mental functioning can be performed at the
neural level. The explanatory schema for theoretical neuroscience
is:



> 
> *Explanation target:*
> * How does the brain carry out functions such as cognitive
> tasks?
> 
>  *Explanatory pattern:*
> * The brain has neurons organized by synaptic connections into
> populations and brain areas.
> * The neural populations have spiking patterns that are transformed
> via sensory inputs and the spiking patterns of other neural
> populations.
> * Interactions of neural populations carry out functions including
> cognitive tasks.
> 
> 
> 



From the perspective of theoretical neuroscience, mental
representations are patterns of neural activity, and inference is
transformation of such patterns. (See the entries on
 neuroscience
 and the
 neuroscience of consciousness.)


### 4.8 Bayesian



Bayesian models are prominent in cognitive science, with applications
to such psychological phenomena as learning, vision, motor control,
language, and social cognition. They have also had effective
applications in robotics. The Bayesian approach assumes that cognition
is approximately optimal in accord with probability theory, especially
Bayes' theorem, which says that the probability of a hypothesis
given evidence is equal to the result of multiplying the prior
probability of the hypothesis by the conditional probability of the
evidence given the hypothesis, all divided by the probability of the
evidence. 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 and
> conditional probabilities.
> * The mind has the capacity for probabilistic computations such as
> applications of Bayes' theorem.
> * Applying probabilistic computations to statistical representations
> accomplishes mental tasks such as inference.
> 
> 
> 



Although Bayesian methods have had impressive applications to a wide
range of phenomena, their psychological plausibility is debatable
because of assumptions about optimality and computations based on
probability theory.


### 4.9 Deep learning



Artificial intelligence has been a central part of cognitive since the
1950s, and the most dramatic recent advances in AI have come from the
approach of deep learning, which has produced major breakthroughs in
fields that include game playing, object recognition, and translation.
Deep learning builds on ideas from connectionism and theoretical
neuroscience, but uses neural networks with more layers and improved
algorithms, benefitting from faster computers and large data bases of
examples. Another important innovation is combining learning from
examples with reinforcement learning, resulting by 2016 in the
world's leading Go player, AlphaGo. Ideas from deep learning are
spreading back into neuroscience and also beginning to influence
research in cognitive psychology. The explanatory schema for deep
learning is:



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



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


### 4.10 Predictive processing and active inference



Predictive processing is an approach to theoretical neuroscience that
views the brain as constantly generating and updating models of the
environment in order to predict the results of perceptions and
actions. Active inference is a version of predictive processing that
hypothesizes that the brain uses Bayesian calculations to minimize
"free energy" consisting of discrepancies between
expectations and actual observations. Organisms survive when brains
reduce prediction errors by changing their models of the environment
or 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 to
> anticipate perceptions and the results of actions.
> * To reduce prediction error, the brain uses Bayesian updating to
> change its models and uses actions to change its environment, e.g. by
> moving.
> * Effective inference, perception, and action result from these
> reductions in prediction errors.
> 
> 
> 



Active inference is open to numerous challenges. Is brain functioning
really Bayesian updating rather than connectionist constraint
satisfaction or deep reinforcement learning? Can predictive processing
subsume other brain functions that include pattern recognition,
explanation, emotional evaluation, memory, and communication? Does
active inference explain high-level cognitive operations such as
causal reasoning, language, and creativity?


## 5. Philosophical Relevance



Some philosophy, in particular naturalistic philosophy of mind, is
part of cognitive science. But the interdisciplinary field of
cognitive science is relevant to philosophy in several ways. First,
the psychological, computational, and other results of cognitive
science investigations have important potential applications to
traditional philosophical problems in epistemology, metaphysics, and
ethics. Second, cognitive science can serve as an object of
philosophical critique, particularly concerning the central assumption
that thinking is representational and computational. Third and more
constructively, cognitive science can be taken as an object of
investigation in the philosophy of science, generating reflections on
the methodology and presuppositions of the enterprise.


### 5.1 Philosophical Applications



Much philosophical research today is naturalistic, treating
philosophical investigations as continuous with empirical work in
fields such as psychology. From a naturalistic perspective, philosophy
of mind is closely allied with theoretical and experimental work in
cognitive science. Metaphysical conclusions about the nature of mind
are to be reached, not by a priori speculation, but by informed
reflection on scientific developments in fields such as psychology,
neuroscience, and computer science. Similarly, epistemology is not a
stand-alone conceptual exercise, but depends on and benefits from
scientific findings concerning mental structures and learning
procedures. Ethics can benefit by using greater understanding of the
psychology of moral thinking to bear on ethical questions such as the
nature of deliberations concerning right and wrong. Here are some
philosophical problems to which ongoing developments in cognitive
science are highly relevant. Links are provided to other relevant
articles in this *Encyclopedia.*


* *Innateness*.
 To what extent is knowledge innate or acquired by experience? Is
human behavior shaped primarily by nature or nurture?
* *Language of thought*.
 Does the human brain operate with a language-like code or with a more
general
 connectionist
 architecture? What is the relation between symbolic cognitive models
using rules and concepts and sub-symbolic models using neural
networks?
* *Mental imagery*.
 Do human minds think with visual and other kinds of imagery, or only
with language-like representations?
* *Folk psychology*.
 Does a person's everyday understanding of other people consist
of having a theory of mind, or of merely being able to simulate
them?
* *Meaning*.
 How do
 mental representations
 acquire meaning or
 mental content?
 To what extent does the meaning of a representation depend on its
relation to other representations, its relation to the world, and its
relation to a community of thinkers?
* *Mind-brain identity*.
 Are mental states brain states? Or can they be
 multiply realized
 by other material states? What is the relation between psychology and
neuroscience? Is
 materialism
 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 and
meaning?
* *Emotions*.
 What are emotions, and what role do they play in thinking?
* *Consciousness*.
 Can conscious experience be scientifically explained, for example by
the
 neuroscience of consciousness?
* *Mental disorder*.
 What are mental disorders, and how are psychological and neural
processes relevant to their explanation and treatment?
* *Perception and reality*.
 How do minds/brains form and evaluate representations of the external
world?
* Perception and cognition.
 How does perception differ from other kinds of cognition with respect
to representational format and justification?
* *Realism*.
 Is cognitive science consistent with views that minds grasp the real
world? Could minds be computer simulations? Is virtual reality a kind
of reality?
* *Information*.
 How does cognitive science illuminate the operations of information
and misinformation in minds and societies?
* *Social science*.
 How do explanations of the operations of minds interact with
explanations of the operations of groups and societies?



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


### 5.2 Critique of Cognitive Science



The claim that human minds work by representation and computation is
an empirical conjecture and might be wrong. Although the
 computational-representational
 approach to cognitive science has been successful in explaining many
aspects of human problem solving, learning, and language use, some
philosophical critics have claimed that this approach is fundamentally
mistaken. Critics of cognitive science have offered such challenges
as:


1. The
 emotion
 challenge: Cognitive science neglects the important role of emotions
in human thinking.
2. The
 consciousness
 challenge: Cognitive science ignores the importance of consciousness
in human thinking.
3. The world challenge: Cognitive science disregards the significant
role of physical environments in human thinking, which is embedded in
and extended into the world.
4. The body challenge: Cognitive science neglects the contribution of
 embodiment
 to human thought and action.
5. The dynamical systems challenge: The mind is a dynamical system,
not a computational system.
6. The social challenge: Human thought is inherently social in ways
that cognitive science ignores.
7. The mathematics challenge: Mathematical results show that human
thinking cannot be computational in the standard sense, so the brain
must operate differently, perhaps as a quantum computer.
8. The interdisciplinarity challenge: Cognitive science has failed to
go beyond multidisciplinary interactions by developing a core theory
that unifies work in its many disciplines.



The first five challenges are increasingly addressed by advances that
explain emotions, consciousness, action, and embodiment in terms of
neural mechanisms. The social challenge is being met by the
development of computational models of interacting agents. The
mathematics challenge is based on misunderstanding of
 Godel's theorem
 and on exaggeration of the relevance of quantum theory to neural
processes. Response to the interdisciplinary challenge must recognize
that cognitive science still has many contending theoretical
approaches, without the unification that theories of evolution and
genetics provide for biology. Nevertheless, interactions among
psychology, neuroscience, linguistics, philosophy, anthropology, and
computer modeling have contributed to theoretical and empirical
progress 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 questions
that are worthy of investigation by philosophers of science. What is
the nature of representation? What role do computational models play
in the development of cognitive theories? What is the relation among
apparently competing accounts of mind involving symbolic processing,
neural networks, and dynamical systems? What is the relation among the
various fields of cognitive science such as psychology, linguistics,
and neuroscience? Are psychological phenomena subject to reductionist
explanations via neuroscience? Are levels of explanation best
characterized 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 philosophical
questions about
 explanation
 and
 reduction.
 Anti-reductionism, according to which psychological explanations are
completely independent of neurological ones, is becoming increasingly
implausible, but it remains controversial to what extent psychology
can be reduced to neuroscience and molecular biology. Crucial to
answering questions about the nature of reduction are answers to
questions about the nature of explanation. Explanations in psychology,
neuroscience, and biology in general are plausibly viewed as
descriptions of
 mechanisms,
 which are combinations of connected parts that interact to produce
regular changes. In psychological explanations, the parts are mental
representations that interact by computational procedures to produce
new representations. In neuroscientific explanations, the parts are
neural populations that interact by electrochemical processes to
produce new neural activity that leads to actions. If progress in
theoretical neuroscience continues, it should become possible to tie
psychological to neurological explanations by showing how mental
representations such as concepts are constituted by activities in
neural populations, and how computational procedures such as spreading
activation among concepts are carried out by neural processes.



The increasing integration of cognitive psychology with neuroscience
provides evidence for the
 mind-brain identity theory
 according to which mental processes are neural, representational, and
computational. Other philosophers dispute such identification on the
grounds that minds are embodied in biological systems and extended
into the world. However, moderate claims about embodiment are
consistent with the identity theory because brain representations
operate in several modalities (e.g. visual and motor) that enable
minds to deal with the world. Another materialist alternative to
mind-brain identity comes from recognizing that explanations of mind
employ molecular and social mechanisms as well as neural and
representational ones.