title: Toward a method of selecting among computational models of cognition.
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.
authors.affiliation: Department of Psychology, The Ohio State University, Columbus 43210-1222, USA. pitt.2@osu.edu
authors.forename: Mark A
authors.initials: MA
authors.lastname: Pitt
authors.forename: In Jae
authors.initials: IJ
authors.lastname: Myung
authors.forename: Shaobo
authors.initials: S
authors.lastname: Zhang
mesh_terms: D003071:Cognition; D019336:Cognitive Science / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D003661:Decision Support Techniques; D006801:Humans; D008960:Models, Psychological; D011601:Psychophysics / Q000706:statistics & numerical data