Computational cognition (sometimes referred to as computational cognitive science or computational psychology) is the study of the
computation
Computation is any type of arithmetic or non-arithmetic calculation that follows a well-defined model (e.g., an algorithm).
Mechanical or electronic devices (or, historically, people) that perform computations are known as '' computers''. An esp ...
al basis of
learning and
inference
Inferences are steps in reasoning, moving from premises to logical consequences; etymologically, the word '' infer'' means to "carry forward". Inference is theoretically traditionally divided into deduction and induction, a distinction that ...
by
mathematical model
A mathematical model is a description of a system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modeling. Mathematical models are used in the natural sciences (such as physics, ...
ing,
computer simulation
Computer simulation is the process of mathematical modelling, performed on a computer, which is designed to predict the behaviour of, or the outcome of, a real-world or physical system. The reliability of some mathematical models can be dete ...
, and
behavioral
Behavior (American English) or behaviour (British English) is the range of actions and mannerisms made by individuals, organisms, systems or artificial entities in some environment. These systems can include other systems or organisms as we ...
experiments. In psychology, it is an approach which develops computational models based on experimental results. It seeks to understand the basis behind the human method of
processing of information. Early on computational cognitive scientists sought to bring back and create a scientific form of
Brentano's
Brentano's was an American bookstore chain with numerous locations in the United States.
As of the 1970s, there were three Brentano's in New York: the Fifth Avenue flagship store at Rockefeller Center, one in Greenwich Village, and one in Whit ...
psychology.
Artificial intelligence
There are two main purposes for the productions of artificial intelligence: to produce intelligent behaviors regardless of the quality of the results, and to model after intelligent behaviors found in nature.
In the beginning of its existence, there was no need for artificial intelligence to emulate the same behavior as human cognition. Until 1960s, economist
Herbert Simon and
Allen Newell
Allen Newell (March 19, 1927 – July 19, 1992) was a researcher in computer science and cognitive psychology at the RAND Corporation and at Carnegie Mellon University’s School of Computer Science, Tepper School of Business, and Departmen ...
attempted to formalize human problem-solving skills by using the results of psychological studies to develop programs that implement the same problem-solving techniques as people would. Their works laid the foundation for
symbolic AI
In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. S ...
and computational cognition, and even some advancements for
cognitive science and
cognitive psychology
Cognitive psychology is the scientific study of mental processes such as attention, language use, memory, perception, problem solving, creativity, and reasoning.
Cognitive psychology originated in the 1960s in a break from behaviorism, which ...
.
The field of symbolic AI is based on the
physical symbol systems hypothesis by Simon and Newell, which states that expressing aspects of cognitive intelligence can be achieved through the manipulation of
symbols
A symbol is a mark, sign, or word that indicates, signifies, or is understood as representing an idea, object, or relationship. Symbols allow people to go beyond what is known or seen by creating linkages between otherwise very different co ...
.
However,
John McCarthy focused more on the initial purpose of artificial intelligence, which is to break down the essence of logical and abstract reasoning regardless of whether or not human employs the same mechanism.
Over the next decades, the progress made in artificial intelligence started to be focused more on developing logic-based and knowledge-based programs, veering away from the original purpose of symbolic AI. Researchers started to believe that symbolic artificial intelligence might never be able to imitate some intricate processes of human cognition like
perception
Perception () is the organization, identification, and interpretation of sensory information in order to represent and understand the presented information or environment. All perception involves signals that go through the nervous system, ...
or
learning. The then perceived impossibility (since refuted ) of implementing emotion in AI, was seen to be a stumbling block on the path to achieving human-like cognition with computers. Researchers began to take a “sub-symbolic” approach to create intelligence without specifically representing that knowledge. This movement led to the emerging discipline of
computational model
A computational model uses computer programs to simulate and study complex systems using an algorithmic or mechanistic approach and is widely used in a diverse range of fields spanning from physics, chemistry and biology to economics, psychology, ...
ing,
connectionism
Connectionism refers to both an approach in the field of cognitive science that hopes to explain mind, mental phenomena using artificial neural networks (ANN) and to a wide range of techniques and algorithms using ANNs in the context of artificial ...
, and
computational intelligence
The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no ...
.
Computational modeling
As it contributes more to the understanding of human cognition than artificial intelligence, computational cognitive modeling emerged from the need to define various cognition functionalities (like motivation, emotion, or perception) by representing them in computational models of mechanisms and processes.
Computational models study
complex system
A complex system is a system composed of many components which may interact with each other. Examples of complex systems are Earth's global climate, organisms, the human brain, infrastructure such as power grid, transportation or communicatio ...
s through the use of algorithms of many variables and extensive
computational resource
In computational complexity theory, a computational resource is a resource used by some computational models in the solution of computational problems.
The simplest computational resources are computation time, the number of steps necessary to s ...
s to produce
computer simulation
Computer simulation is the process of mathematical modelling, performed on a computer, which is designed to predict the behaviour of, or the outcome of, a real-world or physical system. The reliability of some mathematical models can be dete ...
.
Simulation is achieved by adjusting the variables, changing one alone or even combining them together, to observe the effect on the outcomes. The results help experimenters make predictions about what would happen in the real system if those similar changes were to occur.
When computational models attempt to mimic human cognitive functioning, all the details of the function must be known for them to transfer and display properly through the models, allowing researchers to thoroughly understand and test an existing theory because no variables are vague and all variables are modifiable. Consider a
model of memory built by Atkinson and Shiffrin in 1968, it showed how rehearsal leads to
long-term memory
Long-term memory (LTM) is the stage of the Atkinson–Shiffrin memory model in which informative knowledge is held indefinitely. It is defined in contrast to short-term and working memory, which persist for only about 18 to 30 seconds. Long-t ...
, where the information being rehearsed would be stored. Despite the advancement it made in revealing the function of memory, this model fails to provide answers to crucial questions like: how much information can be rehearsed at a time? How long does it take for information to transfer from rehearsal to long-term memory? Similarly, other computational models raise more questions about cognition than they answer, making their contributions much less significant for the understanding of human cognition than other cognitive approaches.
An additional shortcoming of computational modeling is its reported lack of objectivity.
John Anderson John Anderson may refer to:
Business
*John Anderson (Scottish businessman) (1747–1820), Scottish merchant and founder of Fermoy, Ireland
* John Byers Anderson (1817–1897), American educator, military officer and railroad executive, mentor of ...
in his Adaptive Control of Thought-Rational (ACT-R) model uses the functions of computational models and the findings of cognitive science. The ACT-R model is based on the theory that the brain consists of several modules which perform specialized functions separate of each other.
The ACT-R model is classified as a
symbolic approach to cognitive science.
Connectionist networks
Another approach which deals more with the semantic content of cognitive science is connectionism or neural network modeling. Connectionism relies on the idea that the brain consists of simple units or nodes and the behavioral response comes primarily from the layers of connections between the nodes and not from the environmental stimulus itself.
Connectionist network differs from computational modeling specifically because of two functions:
neural back-propagation and
parallel-processing. Neural back-propagation is a method utilized by connectionist networks to show evidence of learning. After a connectionist network produces a response, the simulated results are compared to real-life situational results. The feedback provided by the backward propagation of errors would be used to improve accuracy for the network's subsequent responses.
The second function, parallel-processing, stemmed from the belief that knowledge and perception are not limited to specific modules but rather are distributed throughout the cognitive networks. The present of parallel distributed processing has been shown in psychological demonstrations like the
Stroop effect
----
----
Naming the font color of a printed word is an easier and quicker task if word meaning and font color are congruent. If two words are both printed in red, the average time to say "red" in response to the written word "green" is ...
, where the brain seems to be analyzing the perception of color and meaning of language at the same time.
However, this theoretical approach has been continually disproved because the two cognitive functions for color-perception and word-forming are operating separately and simultaneously, not parallel of each other.
The field of cognition may have benefitted from the use of connectionist networks, but setting up the neural network models can be quite a tedious task and the results may be less interpretable than the system they are trying to model. Therefore, the results may be used as evidence for a broad theory of cognition without explaining the particular process happening within the cognitive function. Other disadvantages of connectionism lie in the research methods it employs or hypothesis it tests as they have been proven inaccurate or ineffective often, taking connectionist models away from an accurate representation of how the brain functions. These issues cause neural network models to be ineffective on studying higher forms of information-processing, and hinder connectionism from advancing the general understanding of human cognition.
References
Further reading
*
*
* {{cite book , editor-last=Sun , editor-first=Ron , editor-link=Ron Sun , date=2008 , title=The Cambridge handbook of computational psychology , location=Cambridge, UK; New York , publisher=
Cambridge University Press
Cambridge University Press is the university press of the University of Cambridge. Granted letters patent by Henry VIII of England, King Henry VIII in 1534, it is the oldest university press in the world. It is also the King's Printer.
Cambr ...
, isbn=9780521857413 , oclc=153772906 , doi=10.1017/CBO9780511816772
External links and bibliography
MIT Computational Cognitive Science GroupNYU Computation and Cognition LabPrinceton Computational Cognitive Science LabStanford Computation and Cognition Lab
Cognition
Computational science
Computational fields of study