Cognitive science

Cognitive science heptagram.svg
About this image
PrirodneNauke.svg
Part of a series on Science

Cognitive science is the interdisciplinary study of mind and how information, e.g., concerning perception, language, reasoning, and emotion, is represented and transformed in the brain. It consists of multiple research disciplines, including psychology, artificial intelligence, philosophy, neuroscience, learning sciences, linguistics, anthropology, sociology, and education.[1] It spans many levels of analysis, from low-level learning and decision mechanisms to high-level logic and planning; from neural circuitry to modular brain organization. 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.[2] In the same decade, the journal Cognitive Science and the Cognitive Science Society were founded.[3]

Contents

History

Cognitive science has a pre-history traceable back to ancient Greek philosophical texts (see Plato's Meno); and certainly must include writers such as Descartes, Immanuel Kant, Benedict de Spinoza, Nicolas Malebranche, Pierre Cabanis, Leibniz and John Locke. But, although these early writers contributed greatly to the philosophical discovery of mind and this would ultimately lead to the development of psychology, they were working with an entirely different set of tools and core concepts than those of the cognitive scientist.

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 developed the first variants of what are now known as artificial neural networks, models of computation inspired by the structure of biological neural networks.

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 argued that in order to explain language, we needed a theory like generative grammar, which not only attributed internal representations but characterized their underlying order.

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".

Eventually the limits of the symbolic AI research program became apparent. For instance, it seemed to be unrealistic to comprehensively list human knowledge in a form usable by a symbolic computer program. The late 80s and 90s saw the rise of neural networks and connectionism as a research paradigm. Under this point of view, often attributed to James McClelland and David Rumelhart, the mind could be characterized as a set of complex associations, represented as a layered network. Critics argue that there are some phenomena which are better captured by symbolic models, and that connectionist models are often so complex as to have little explanatory power. Recently symbolic and connectionist models have been combined, making it possible to take advantage of both forms of explanation.[4]

Principles

Levels of analysis

A central tenet of cognitive science is that a complete understanding of the mind/brain cannot be attained by studying only a single level. An example would be the problem of remembering a phone number and recalling it later. One approach to understanding this process 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 nature

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. Some doubt whether there is a unified cognitive science, and prefer to speak of the cognitive sciences in plural.[6]

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. According to some versions of functionalism, even non-human systems, such as other animal species, alien life forms, or advanced computers can, in principle, have mental states.

Cognitive science: the term

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.

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.

Scope

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.[7]

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 intelligence

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.[8] (See the section on computational modeling in the Research Methods section.)

There is some debate in the field as to whether the mind is best viewed as a huge array of small but individually feeble elements (i.e. neurons), or as a collection of higher-level structures such as symbols, schemas, plans, and rules. The former view uses connectionism to study the mind, whereas the latter emphasizes symbolic computations. One way to view the issue is whether it is possible to accurately simulate a human brain on a computer without accurately simulating the neurons that make up the human brain.

Attention

Main article: Attention

Attention is the selection of important information. The human mind is bombarded with millions of stimuli and it must have a way of deciding which of this information to process. Attention is sometimes seen as a spotlight, meaning one can only shine the light on a particular set of information. Experiments that support this metaphor include the dichotic listening task (Cherry, 1957) and studies of inattentional blindness (Mack and Rock, 1998). In the dichotic listening task, subjects are bombarded with two different messages, one in each ear, and told to focus on only one of the messages. At the end of the experiment, when asked about the content of the unattended message, subjects cannot report it.

Knowledge, and Processing, of Language

A well known example of a Phrase structure tree. This is one way of representing human language that shows how different components are organized hierarchically.
Main articles: Theoretical linguistics, Cognitive linguistics, Language, Linguistics, and Psycholinguistics

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. A major driving force in the theoretical linguistic field is discovering the nature that language must have in the abstract in order to be learned in such a fashion. Some of the driving research questions in studying how the brain itself 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, morphology, syntax, semantics, and pragmatics. Many aspects of language can be studied from each of these components and from their interaction.

The study of language processing in cognitive science is closely tied to the field of linguistics. Linguistics was traditionally studied as a part of the humanities, including studies of history, art and literature. In the last fifty years or so, more and more researchers have studied knowledge and use of language as a cognitive phenomenon, the main problems being how knowledge of language can be acquired and used, and what precisely it consists of. Linguists have found that, while humans form sentences in ways apparently governed by very complex systems, they are remarkably unaware of the rules that govern their own speech. Thus linguists must resort to indirect methods to determine what those rules might be, if indeed rules as such exist. In any event, if speech is indeed governed by rules, they appear to be opaque to any conscious consideration.

Learning and development

Main articles: Learning and Developmental psychology

Learning and development are the processes by which we acquire knowledge and information over time. Infants are born with little or no knowledge (depending on how knowledge is defined), yet they rapidly acquire the ability to use language, walk, and recognize people and objects. Research in learning and development aims to explain the mechanisms by which these processes might take place.

A major question in the study of cognitive development is the extent to which certain abilities are innate or learned. This is often framed in terms of the nature versus nurture debate. The nativist view emphasizes that certain features are innate to an organism and are determined by its genetic endowment. The empiricist view, on the other hand, emphasizes that certain abilities are learned from the environment. Although clearly both genetic and environmental input is needed for a child to develop normally, considerable debate remains about how genetic information might guide cognitive development. In the area of language acquisition, for example, some (such as Steven Pinker)[9] have argued that specific information containing universal grammatical rules must be contained in the genes, whereas others (such as Jeffrey Elman and colleagues in Rethinking Innateness) have argued that Pinker's claims are biologically unrealistic. They argue that genes determine the architecture of a learning system, but that specific "facts" about how grammar works can only be learned as a result of experience.

Memory

Main article: Memory

Memory allows us to store information for later retrieval. Memory is often thought of consisting of both a long-term and short-term store. Long-term memory allows us to store information over prolonged periods (days, weeks, years). We do not yet know the practical limit of long-term memory capacity. Short-term memory allows us to store information over short time scales (seconds or minutes).

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 .

Cognitive scientists study memory just as psychologists do, but tend to focus in more on how memory bears on cognitive processes, and the interrelationship between cognition and memory. One example of this could be, what mental processes does a person go through to retrieve a long-lost memory? Or, what differentiates between the cognitive process of recognition (seeing hints of something before remembering it, or memory in context) and recall (retrieving a memory, as in "fill-in-the-blank")?

Perception and action

The Necker cube, an example of an optical illusion
Main article: Perception

Perception is the ability to take in information via the senses, and process it in some way. Vision and hearing are two dominant senses that allow us to perceive the environment. Some questions in the study of visual perception, for example, include: (1) How are we able to recognize objects?, (2) Why do we perceive a continuous visual environment, even though we only see small bits of it at any one time? One tool for studying visual perception is by looking at how people process optical illusions. The image on the right of a Necker cube is an example of a bistable percept, that is, the cube can be interpreted as being oriented in two different directions.

The study of haptic (tactile), olfactory, and gustatory stimuli also fall into the domain of perception.

Action is taken to refer to the output of a system. In humans, this is accomplished through motor responses. Spatial planning and movement, speech production, and complex motor movements are all aspects of action.

Research methods

Many different methodologies are used to study cognitive science. As the field is highly interdisciplinary, research often cuts across multiple areas of study, drawing on research methods from psychology, neuroscience, computer science and systems theory.

Behavioral experiments

In order to have a description of what constitutes intelligent behavior, one must study behavior itself. This type of research is closely tied to that in cognitive psychology and psychophysics. By measuring behavioral responses to different stimuli, one can understand something about how those stimuli are processed.

Brain imaging

Main article: Neuroimaging
Image of the human head with the brain. The arrow indicates the position of the hypothalamus.

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 modeling

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.

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.

All the above approaches tend to be generalized to the form of integrated computational models of a synthetic/abstract intelligence, in order to be applied to the explanation and improvement of individual and social/organizational decision-making and reasoning.

Neurobiological methods

Research methods borrowed directly from neuroscience and neuropsychology can also help us to understand aspects of intelligence. These methods allow us to understand how intelligent behavior is implemented in a physical system.

Key findings

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

See also: Functionalism (philosophy of mind)#Criticism

In a paper written shortly before his death, B.F. Skinner stated that "cognitive science is the creation science of psychology."[10]

Notable researchers

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, Herbert Simon, Allen Newell, and Kevin Warwick are prominent. Popular names in the discipline of psychology include James McClelland and Steven Pinker. Anthropologists Dan Sperber, Scott Atran, Pascal Boyer and Joseph Henrich have been involved in collaborative projects with cognitive and social psychologists, political scientists and evolutionary biologists in attempts to develop general theories of culture formation, religion and political association.

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.[11][12][13] The first standalone Ph.D. in Cognitive Science from Indiana University at Bloomington was awarded in 2008 to Ohio University professor Ronaldo Vigo.[14]

See also
  • Dynamicism
  • Educational psychology
  • Educational neuroscience
  • Heterophenomenology
  • Human Cognome Project
  • Indiana Archives of Cognitive Science
  • Informatics
  • Embodied cognitive science
  • Embodied cognition
  • Enactivism
  • Linguistics
  • List of cognitive scientists
  • List of institutions granting degrees in cognitive science
  • Neural Darwinism

Notes

  1. ↑ Thagard, Paul, Cognitive Science, The Stanford Encyclopedia of Philosophy (Fall 2008 Edition), Edward N. Zalta (ed.).
  2. ↑ Longuet-Higgins, H. C. (1973). "Comments on the Lighthill Report and the Sutherland Reply", in Artificial Intelligence: a paper symposium, Science Research Council, 35-37
  3. ↑ Cognitive Science Society
  4. ↑ Artur S. d'Avila Garcez, Luis C. Lamb and Dov M. Gabbay. Neural-Symbolic Cognitive Reasoning. Cognitive Technologies. Springer, 2008, ISBN 978-3-540-73245-7, 2008.
  5. ↑ Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman.
  6. ↑ Miller, G. A. (2003). The cognitive revolution: a historical perspective. Trends in Cognitive Sciences, 7, 141-144.
  7. ↑ 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. ↑ Sun, Ron (ed.) (2008). The Cambridge Handbook of Computational Psychology. Cambridge University Press, New York.
  9. ↑ Pinker S., Bloom P. (1990). "Natural language and natural selection". Behavioral and Brain Sciences 13 (4): 707–784. 
  10. ↑ B. F. Skinner, "Can Psychology be a Science of Mind?", American Psychologist, November 1990, page 1209, At the APA Web Site Successfully accessed 29 Dec 2009
  11. ↑ http://www.linguistlist.org/issues/19/19-2813.html
  12. ↑ http://www.cogsci.ucsd.edu/index.php?cat=about&page=overview
  13. ↑ http://www.lucs.lu.se/Agneta.Gulz/
  14. ↑ http://www.cogs.indiana.edu/publications/newsletters/sp09.pdf

References

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

Further reading

General
Classic texts
Definitions
Publications & publishers

External links