Griffiths, Thomas L., Chater, Nick, Kemp, Charles, Perfors, Amy and Tenenbaum, Joshua B. (2010) Probabilistic models of cognition: exploring representations and inductive biases. Trends in Cognitive Sciences, Vol.14 (No.8). pp. 357-364. doi:10.1016/j.tics.2010.05.004

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Abstract

Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind faces involve induction. The probabilistic approach to modeling cognition begins by identifying ideal solutions to these inductive problems. Mental processes are then modeled using algorithms for approximating these solutions, and neural processes are viewed as mechanisms for implementing these algorithms, with the result being a top-down analysis of cognition starting with the function of cognitive processes. Typical connectionist models, by contrast, follow a bottom-up approach, beginning with a characterization of neural mechanisms and exploring what macro-level functional phenomena might emerge. We argue that the top-down approach yields greater flexibility for exploring the representations and inductive biases that underlie human cognition.

Item Type:	Journal Article
Subjects:	H Social Sciences > HV Social pathology. Social and public welfare
Divisions:	Faculty of Social Sciences > Warwick Business School > Behavioural Science Faculty of Social Sciences > Warwick Business School
Journal or Publication Title:	Trends in Cognitive Sciences
Publisher:	Elsevier Science
ISSN:	1364-6613
Official Date:	August 2010
Dates:	Date Event August 2010 Published
Volume:	Vol.14
Number:	No.8
Number of Pages:	8
Page Range:	pp. 357-364
DOI:	10.1016/j.tics.2010.05.004
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access

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