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Rendering of human brain based on MRI data

For the journal of the same name, see Cognitive Science (journal)

Cognitive science is usually defined as the scientific study either of mind or of intelligence (e.g. Luger 1994). Practically every introduction to cognitive science also stresses that it is highly interdisciplinary; the primary components of cognitive science include cognitive psychology, cognitive neuroscience, linguistics, philosophy, and computer science, while other components include robotics, anthropology and biology.

It relies on varying scientific methodology (e.g. behavioral experimentation, computational simulations, neuro-imaging, statistical analyses), and spans many levels of analysis of the mind (from low-level learning and decision mechanisms to high-level logic and planning, from neural circuitry to modular brain organization, etc.). The term cognitive science was coined by Christopher Longuet-Higgins in his 1973 commentary on the Lighthill report, which concerned the then-current state of Artificial Intelligence research.[1] In the same decade, the journal Cognitive Science and the Cognitive Science Society were founded.[2] Cognitive science differs from cognitive psychology in that algorithms that are intended to simulate human behavior are implemented or implementable on a computer.[3][4]

History Edit

Like most approaches to the mind, cognitive science can be traced back to philosophical questions, especially about the nature of knowledge. For instance, Plato's dialog Meno, where he investigates the source of knowledge, can be seen as a foundational text.

The modern culture of cognitive science can be traced back to the early cyberneticists in the 1930s and 1940s, such as Warren McCulloch and Walter Pitts, who sought to understand the organizing principles of the mind . McCulloch and Pitts essentially invented the neural network, but did not have the computational tools to develop it into modern form.

Another precursor was the early development of the theory of computation and the digital computer in the 1940s and 1950s. Alan Turing and John von Neumann were instrumental in these developments. The modern computer, or Von Neumann machine, would play a central role in cognitive science, both as a metaphor for the mind, and as a tool for investigation.

In 1959, Noam Chomsky published a scathing review of B. F. Skinner's book Verbal Behavior. At the time, Skinner's behaviorist paradigm dominated psychology: Most psychologists focused on functional relations between stimulus and response, without positing internal representations. Chomsky's work showed that in order to explain language, we needed a theory like his generative grammar, which not only attributed internal representations but characterized their underlying order. This hugely successful theory would inspire much later cognitive science.

In the 1970s and early 1980s, much cognitive science research focused on the possibility of artificial intelligence. Researchers such as Marvin Minsky would write computer programs in languages such as LISP to attempt to formally characterize the steps that human beings went through, for instance, in making decisions and solving problems, in the hope of better understanding human thought, and also in the hope of creating artificial minds. This approach is known as "symbolic AI".


Today a plurality of approaches exist, from connectionism, to a focus on dynamical systems models, to attempts to reintroduce symbolic models using tools from modern computer science such as machine learning.


Levels of analysisEdit

A central tenet of cognitive science is that a complete understanding of the mind/brain cannot be attained by studying only a single level. For example, consider the problem of remembering a phone number and recalling it later. How does this process occur? One approach would be to study behavior through direct observation. A person could be presented with a phone number, asked to recall it after some delay. Then the accuracy of the response could be measured. Another approach would be to study the firings of individual neurons while a person is trying to remember the phone number. Neither of these experiments on their own would fully explain how the process of remembering a phone number works. Even if the technology to map out every neuron in the brain in real-time were available, and it were known when each neuron was firing, it would still be impossible to know how a particular firing of neurons translates into the observed behavior. Thus an understanding of how these two levels relate to each other is needed. This can be provided by a functional level account of the process. Studying a particular phenomenon from multiple levels creates a better understanding of the processes that occur in the brain to give rise to a particular behavior. Marr[5] gave a famous description of three levels of analysis:

  1. the computational theory, specifying the goals of the computation;
  2. representation and algorithm, giving a representation of the input and output and the algorithm which transforms one into the other; and
  3. the hardware implementation, how algorithm and representation may be physically realized.

(See also the entry on functionalism.)

Interdisciplinary natureEdit

Cognitive science is an interdisciplinary field with contributors from various fields, including psychology, neuroscience, linguistics, philosophy of mind, computer science, anthropology, sociology, and biology. Cognitive science tends to view the world outside the mind much as other sciences do. Thus it too has an objective, observer-independent existence. The field is usually seen as compatible with the physical sciences, and uses the scientific method as well as simulation or modeling, often comparing the output of models with aspects of human behavior. Still, there is much disagreement about the exact relationship between cognitive science and other fields, and the interdisciplinary nature of cognitive science is largely both unrealized and circumscribed.[How to reference and link to summary or text]

Many, but not all, who consider themselves cognitive scientists have a functionalist view of the mind—the view that mental states are classified functionally, such that any system that performs the proper function for some mental state is considered to be in that mental state. Thus, according to functionalism about the mind, even non-human systems, such as other animal species, alien life forms, or advanced computers can, in principle, have mental states. This perspective is one of the reasons the term "cognitive science" is not exactly coextensive with neuroscience, psychology, or some combination of the two.[How to reference and link to summary or text]

From the external point of view, the largest interdisciplinary context of cognitive science is systemics. It includes the socio-cognitive extension of the cognition models and theories over different social environments social systems, with the emphasis on distributed cognition and intelligence.[How to reference and link to summary or text]

Cognitive science: the termEdit

The term "cognitive" in "cognitive science" is "used for any kind of mental operation or structure that can be studied in precise terms" (Lakoff and Johnson, 1999). This conceptualization is very broad, and should not be confused with how "cognitive" is used in some traditions of analytic philosophy, where "cognitive" has to do only with formal rules and truth conditional semantics. (Nonetheless, that interpretation would bring one close to the historically dominant school of thought within cognitive science on the nature of cognition - that it is essentially symbolic, propositional, and logical.)

The earliest entries for the word "cognitive" in the OED take it to mean roughly pertaining "to the action or process of knowing". The first entry, from 1586, shows the word was at one time used in the context of discussions of Platonic theories of knowledge. Most in cognitive science, however, presumably do not believe their field is the study of anything as certain as the knowledge sought by Plato.


Cognitive science is a large field, and covers a wide array of topics on cognition. However, it should be recognized that cognitive science is not equally concerned with every topic that might bear on the nature and operation of the mind or intelligence. Social and cultural factors, emotion, consciousness, animal cognition, comparative and evolutionary approaches are frequently de-emphasized or excluded outright, often based on key philosophical conflicts. Another important mind-related subject that the cognitive sciences tend to avoid is the existence of qualia, with discussions over this issue being sometimes limited to only mentioning qualia as a philosophically-open matter. Some within the cognitive science community, however, consider these to be vital topics, and advocate the importance of investigating them.[6]

Below are some of the main topics that cognitive science is concerned with. This is not an exhaustive list, but is meant to cover the wide range of intelligent behaviors. See List of cognitive science topics for a list of various aspects of the field.

Artificial intelligenceEdit

Main article: Artificial intelligence

"... One major contribution of AI and cognitive science to psychology has been the information processing model of human thinking in which the metaphor of brain-as-computer is taken quite literally. ." AAAI Web pages.

Artificial intelligence (AI) involves the study of cognitive phenomena in machines. One of the practical goals of AI is to implement aspects of human intelligence in computers. Computers are also widely used as a tool with which to study cognitive phenomena. Computational modeling uses simulations to study how human intelligence may be structured. [7] (See the section on computational modeling in the Research Methods section.)



Main article: Attention


Language processingEdit

Main article: Cognitive linguistics

The ability to learn and understand language is an extremely complex process. Language is acquired within the first few years of life, and all humans under normal circumstances are able to acquire language proficiently. Some of the driving research questions in studying how the brain processes language include: (1) To what extent is linguistic knowledge innate or learned?, (2) Why is it more difficult for adults to acquire a second-language than it is for infants to acquire their first-language?, and (3) How are humans able to understand novel sentences?

The study of language processing ranges from the investigation of the sound patterns of speech to the meaning of words and whole sentences. Linguistics often divides language processing into orthography, phonology and phonetics, syntactics, semantics, and pragmatics. Many aspects of language can be studied from each of these components and from their interaction.


One approach to cognitive issues in language is the pragmatics of language, that is, the current use of the language by a real speaker. From a pragmatic analytical perspective it is possible to show that some people who have a profession in which they categorically work with language (e.g. journalists) have a behavior which is not predictable by known theories. The pragmatic approach is also useful in the study of collective distributed decision making, particularly in broadcasted systems (for instance aviation approach control - AAP).

Learning and developmentEdit

Main article: Learning




Main article: Memory


Memory is also often grouped into declarative and procedural forms. Declarative memory--grouped into subsets of semantic and episodic forms of memory--refers to our memory for facts and specific knowledge, specific meanings, and specific experiences (e.g., Who was the first president of the U.S.A.?, or "What did I eat for breakfast four days ago?). Procedural memory allows us to remember actions and motor sequences (e.g. how to ride a bicycle) and is often dubbed implicit knowledge or memory .


Perception and actionEdit

File:Necker cube.svg
Main article: Perception




Research methodsEdit


Behavioral experimentsEdit


    • sameness judgments for colors, tones, textures, etc.
    • threshold differences for colors, tones, textures, etc.

Brain imagingEdit

Main article: Neuroimaging


Brain imaging involves analyzing activity within the brain while performing various cognitive tasks. This allows us to link behavior and brain function to help understand how information is processed. Different types of imaging techniques vary in their temporal (time-based) and spatial (location-based) resolution. Brain imaging is often used in cognitive neuroscience.


Computational modelingEdit

MultiLayerNeuralNetwork english

A Neural network with two layers.

Computational models require a mathematically and logically formal representation of a problem. Computer models are used in the simulation and experimental verification of different specific and general properties of intelligence. Computational modeling can help us to understand the functional organization of a particular cognitive phenomenon. There are two basic approaches to cognitive modeling. The first is focused on abstract mental functions of an intelligent mind and operates using symbols, and the second, which follows the neural and associative properties of the human brain, and is called subsymbolic.

  • Symbolic modeling evolved from the computer science paradigms using the technologies of Knowledge-based systems, as well as a philosophical perspective, see for example "Good Old-Fashioned Artificial Intelligence" (GOFAI). They are developed by the first cognitive researchers and later used in information engineering for expert systems . Since the early 1990s it was generalized in systemics for the investigation of functional human-like intelligence models, such as personoids, and, in parallel, developed as the SOAR environment. Recently, especially in the context of cognitive decision making, symbolic cognitive modeling is extended to socio-cognitive approach including social and organization cognition interrelated with a sub-symbolic not conscious layer.
  • Subsymbolic modeling includes Connectionist/neural network models. Connectionism relies on the idea that the mind/brain is composed of simple nodes and that the power of the system comes primarily from the existence and manner of connections between the simple nodes. Neural nets are textbook implementations of this approach. Some critics of this approach feel that while these models approach biological reality as a repetition of how the system works, they lack explanatory powers because complicated systems of connections with even simple rules are extremely complex and often less interpretable than the system they model.

Other approaches gaining in popularity include the use of Dynamical systems theory and also techniques putting symbolic models and connectionist models into correspondence (Neural-symbolic integration). Bayesian models, often drawn from machine learning, are also gaining popularity.


Neurobiological methodsEdit


Key findingsEdit

Cognitive science has much to its credit. Among other accomplishments, it has given rise to models of human cognitive bias and risk perception, and has been influential in the development of behavioral finance, part of economics. It has also given rise to a new theory of the philosophy of mathematics, and many theories of artificial intelligence, persuasion and coercion. It has made its presence firmly known in the philosophy of language and epistemology - a modern revival of rationalism - as well as constituting a substantial wing of modern linguistics. Fields of cognitive science have been influential in understanding the brain's particular functional systems (and functional deficits) ranging from speech production to auditory processing and visual perception. It has made progress in understanding how damage to particular areas of the brain affect cognition, and it has helped to uncover the root causes and results of specific disfunction, such as dyslexia, anopia, and hemispatial neglect.

Criticism Edit

The philosophical underpinnings of research in cognitive science have been criticized by philosophers and scientists alike[attribution needed]. In a speech given shortly before his death[9], B.F. Skinner stated that "cognitive science is the creationism of psychology." See Functionalism (philosophy of mind) for an extended entry on criticisms.

Notable researchersEdit

Some of the more recognized names in cognitive science are usually either the most controversial or the most cited. Within philosophy familiar names include Daniel Dennett who writes from a computational systems perspective, John Searle known for his controversial Chinese Room, Jerry Fodor who advocates functionalism, and Douglas Hofstadter, famous for writing Gödel, Escher, Bach, which questions the nature of words and thought. In the realm of linguistics, Noam Chomsky and George Lakoff have been influential (both have also become notable as political commentators). In Artificial intelligence Marvin Minsky and Kevin Warwick are prominent. Popular names in the discipline of psychology include James McClelland and Steven Pinker.

The academic field of cognitive science in its own right is very recently established. The University of California, San Diego was the first university in the world to establish a cognitive science department and the first Ph.D. in cognitive science was awarded to Agneta Gulz of Lund University in 1991.[10][11][12]

See alsoEdit


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  2. Cognitive Science Society
  3. [1]
  4. Luger, George (1994). Cognitive science : the science of intelligent systems, San Diego: Academic Press.
  6. A number of authors consider the qualia problem to be part of the cognitive science field, e.g. Some philosophical issues in cognitive science: qualia, intentionality, and the mind-body problem, Qualia: The Hard Problem, and indeed the entire discipline of philosophy as being part of the cog sci field, e.g. What is Cognitive Science?, while other reputable sources that cover both qualia and cog sci do not draw any obvious connection between them, e.g. the Stanford encyclopedia of philosophy (Jan 2008 online edition) does have full-size articles on both qualia and cog sci, but qualia are not even mentioned in the cog sci article while cog sci is not mentioned in the qualia article.
  8. Pinker S., Bloom P. (1990). Natural language and natural selection. Behavioral and Brain Sciences 13 (4): 707–784.
  9. ref.

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  1. Baumgartner, P., et al. Eds. (1995). Speaking Minds: Interviews With Twenty Eminent Cognitive Scientists. Princeton, NJ: Princeton University Press.
  2. Bechtel, W. et al. Ed. (1999). A Companion to Cognitive Science. Blackwell Companions to Philosophy. Malden, MA: Blackwell Publishers.
  3. Damasio, A. R. (1994). Descartes' Error: Emotion, Reason and the Human Brain. New York: Grosset/Putnam.
  4. Gardner, H. (1985). The Mind's New Science. New York: Basic Books.
  5. Gazzaniga, M. S. Ed. (1996). Conversations in the Cognitive Neurosciences. New York: The MIT Press.
  6. Hunt, M. (1982). The Universe Within: A New Science Explores the Human Mind. Brighton: The Harvester Press.
  7. Lakoff, G and Johnson, M. (1999). Philosophy In The Flesh. New York: Basic Books.
  8. Port, Robert F. and vanGelder, Tim (1995). Mind as Motion: Explorations in the Dynamics of Cognition. Cambridge, MA: The MIT Press. ISBN 0262161508 .
  9. Sun, Ron & L. Bookman, (eds.), Computational Architectures Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994.
  10. Thagard, P. (2nd, 2005). Mind : Introduction to Cognitive Science. Cambridge, MA: The MIT Press.
  11. Thelen, Esther and Smith, Linda B. (1996). A Dynamic Systems Approach to the Development of Cognition and Action. Cambridge, MA: The MIT Press. ISBN 026270059X .
  12. Gardner, Howard (1987). The Minds New Science: A History of the Cognitive Revolution. New York: Basic Books.

Further readingEdit

List of People
Publications & Publishers
Classic Texts
  • Glimcher, Paul W. (2003). Decision, Uncertainty, and the Brain, The MIT Press.
  • Rumelhart, David E.; James L. McClelland and the PDP Research Group (1987). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, The MIT Press.
  • Gleitman, Lila R.; Mark Liberman (Eds.) (1995). An Invitation to Cognitive Science, Vol. 1: Language, The MIT Press. ISBN 978-0262650441.
  • Hutto, Daniel D. (2008). Folk Psychological Narratives: The Sociocultural Basis of Understanding Reasons, The MIT Press.
  • Sun, Ron (2008). The Cambridge Handbook of Computational Psychology, Cambridge University Press, New York.

External linksEdit


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