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Beyond Single‐Level Accounts: The Role of Cognitive Architectures in Cognitive Scientific Explanation
Author(s) -
Cooper Richard P.,
Peebles David
Publication year - 2015
Publication title -
topics in cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 56
eISSN - 1756-8765
pISSN - 1756-8757
DOI - 10.1111/tops.12132
Subject(s) - cognitive architecture , cognition , cognitive science , computer science , set (abstract data type) , representation (politics) , reductionism , cognitive model , artificial intelligence , bayesian probability , computational model , cognitive psychology , theoretical computer science , psychology , epistemology , neuroscience , programming language , philosophy , politics , political science , law
We consider approaches to explanation within the cognitive sciences that begin with Marr's computational level (e.g., purely Bayesian accounts of cognitive phenomena) or Marr's implementational level (e.g., reductionist accounts of cognitive phenomena based only on neural‐level evidence) and argue that each is subject to fundamental limitations which impair their ability to provide adequate explanations of cognitive phenomena. For this reason, it is argued, explanation cannot proceed at either level without tight coupling to the algorithmic and representation level. Even at this level, however, we argue that additional constraints relating to the decomposition of the cognitive system into a set of interacting subfunctions (i.e., a cognitive architecture) are required. Integrated cognitive architectures that permit abstract specification of the functions of components and that make contact with the neural level provide a powerful bridge for linking the algorithmic and representational level to both the computational level and the implementational level.