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Type: Journal article
Title: Probabilistic models of cognition: exploring representations and inductive biases
Author: Griffiths, T.
Chater, N.
Kemp, C.
Perfors, A.
Tenenbaum, J.
Citation: Trends in Cognitive Sciences, 2010; 14(8):357-364
Publisher: Elsevier Science London
Issue Date: 2010
ISSN: 1364-6613
Statement of
Thomas L. Griffiths, Nick Chater, Charles Kemp, Amy Perfors and Joshua B. Tenenbaum
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.
Keywords: Brain
Predictive Value of Tests
Models, Psychological
Rights: © 2010 Elsevier Ltd. All rights reserved
DOI: 10.1016/j.tics.2010.05.004
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Appears in Collections:Aurora harvest
Psychology publications

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