Beyond single-level accounts: The role of cognitive architectures in cognitive scientific explanation

Richard P. Cooper, David Peebles

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

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.

LanguageEnglish
Pages243-258
Number of pages16
JournalTopics in Cognitive Science
Volume7
Issue number2
DOIs
Publication statusPublished - 1 Apr 2015

Fingerprint

Cognitive Science
Cognitive systems
Aptitude
Decomposition
Specifications
contact
ability
science
evidence

Cite this

@article{4646d3f02a574f81945ee0efeaafd6fb,
title = "Beyond single-level accounts: The role of cognitive architectures in cognitive scientific explanation",
abstract = "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.",
keywords = "Algorithmic and representational level, Bayes' rule, Cognitive architecture, Cognitive neuroscience, Explanation",
author = "Cooper, {Richard P.} and David Peebles",
year = "2015",
month = "4",
day = "1",
doi = "10.1111/tops.12132",
language = "English",
volume = "7",
pages = "243--258",
journal = "Topics in Cognitive Science",
issn = "1756-8757",
publisher = "Wiley-Blackwell",
number = "2",

}

Beyond single-level accounts : The role of cognitive architectures in cognitive scientific explanation. / Cooper, Richard P.; Peebles, David.

In: Topics in Cognitive Science, Vol. 7, No. 2, 01.04.2015, p. 243-258.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Beyond single-level accounts

T2 - Topics in Cognitive Science

AU - Cooper, Richard P.

AU - Peebles, David

PY - 2015/4/1

Y1 - 2015/4/1

N2 - 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.

AB - 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.

KW - Algorithmic and representational level

KW - Bayes' rule

KW - Cognitive architecture

KW - Cognitive neuroscience

KW - Explanation

UR - http://www.scopus.com/inward/record.url?scp=84928587534&partnerID=8YFLogxK

U2 - 10.1111/tops.12132

DO - 10.1111/tops.12132

M3 - Article

VL - 7

SP - 243

EP - 258

JO - Topics in Cognitive Science

JF - Topics in Cognitive Science

SN - 1756-8757

IS - 2

ER -