Toward a method of selecting among computational models of cognition.

Full Abstract

The question of how one should decide among competing explanations of data is at the heart of the scientific enterprise. Computational models of cognition are increasingly being advanced as explanations of behavior. The success of this line of inquiry depends on the development of robust methods to guide the evaluation and selection of these models. This article introduces a method of selecting among mathematical models of cognition known as minimum description length, which provides an intuitive and theoretically well-grounded understanding of why one model should be chosen. A central but elusive concept in model selection, complexity, can also be derived with the method. The adequacy of the method is demonstrated in 3 areas of cognitive modeling:
psychophysics, information integration, and categorization.

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Author information

Author/s: Pitt, Mark A (MA); Myung, In Jae (IJ); Zhang, Shaobo (S);

Affiliation: Department of Psychology, The Ohio State University, Columbus 43210-1222, USA. pitt.2(-atsign-)osu.edu

Grants: MH57472 (Agency:United States NIMH)

Journal and publication information

Publication Type: Journal Article; Research Support, U.S. Gov't, P.H.S.; Review

Journal: Psychological review (Psychol Rev), published in United States. (Language: eng)

Reference: 2002-Jul; vol 109 (issue 3) : pp 472-91

Dates: Created 2002/06/28; Completed 2002/07/23; Revised 2007/11/14;

PMID: 12088241, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Cognition
Cognitive Science - statistics & numerical data
Data Interpretation, Statistical
Decision Support Techniques

Decision Support Techniques
Humans
Models, Psychological
Psychophysics - statistics & numerical data

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