Editing Cognitive science (section)

===Computational modeling===
{{See also|Computational cognition|Cognitive model}}
[[Image:Multi-Layer Neural Network-Vector.svg|thumb|200px|An [[artificial neural network]] with two layers]]

[[computer model|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 [[property|properties]] of [[intelligence]]. Computational modeling can help us understand the functional organization of a particular cognitive phenomenon.
Approaches to cognitive modeling can be categorized as: (1) symbolic, on abstract mental functions of an intelligent mind by means of symbols; (2) subsymbolic, on the neural and associative properties of the human brain; and (3) across the symbolic–subsymbolic border, including hybrid.
* ''Symbolic modeling'' evolved from the computer science paradigms using the technologies of [[knowledge-based systems]], as well as a philosophical perspective (e.g. "Good Old-Fashioned Artificial Intelligence" ([[GOFAI]])). They were developed by the first cognitive researchers and later used in [[information engineering]] for [[expert system]]s. Since the early 1990s it was generalized in [[systemics]] for the investigation of functional human-like intelligence models, such as [[personoid]]s, and, in parallel, developed as the [[Soar (cognitive architecture)|SOAR]] environment. Recently, especially in the context of cognitive decision-making, symbolic cognitive modeling has been extended to the [[socio-cognitive]] approach, including social and organizational cognition, interrelated with a sub-symbolic non-conscious layer.
* ''Subsymbolic modeling'' includes ''[[Connectionism|connectionist/neural network models]].'' Connectionism relies on the idea that the mind/brain is composed of simple nodes and its problem-solving capacity derives from the connections between them. [[Neural nets]] are textbook implementations of this approach. Some critics of this approach feel that while these models approach biological reality as a representation of how the system works, these models lack explanatory powers because, even in systems endowed with simple connection rules, the emerging high complexity makes them less interpretable at the connection-level than they apparently are at the macroscopic level.
* Other approaches gaining in popularity include (1) [[Cognitive model#Dynamical systems|dynamical systems]] theory, (2) mapping symbolic models onto connectionist models (Neural-symbolic integration or [[hybrid intelligent systems]]), and (3) and [[Bayesian cognitive science|Bayesian models]], which are often drawn from [[machine learning]].

All the above approaches tend either to be generalized to the form of integrated computational models of a synthetic/abstract intelligence (i.e. [[cognitive architecture]]) in order to be applied to the explanation and improvement of individual and social/organizational [[decision-making]] and [[Psychology of reasoning|reasoning]]<ref>{{Cite book|last=Lieto|first=Antonio|title=Cognitive Design for Artificial Minds|year=2021|location=London, UK | publisher=Routledge, Taylor & Francis | isbn=9781138207929}}</ref><ref>Sun, Ron (ed.), Grounding Social Sciences in Cognitive Sciences. MIT Press, Cambridge, Massachusetts. 2012.</ref> or to focus on single simulative programs (or microtheories/"middle-range" theories) modelling specific cognitive faculties (e.g. vision, language, categorization etc.).