Rational Approximations to Rational Models: Alternative Algorithms for Category Learning
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| Title: | Rational Approximations to Rational Models: Alternative Algorithms for Category Learning |
|---|---|
| Language: | English |
| Authors: | Sanborn, Adam N., Griffiths, Thomas L., Navarro, Daniel J. |
| Source: | Psychological Review. Oct 2010 117(4):1144-1167. |
| Availability: | American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002-4242. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org/publications |
| Peer Reviewed: | Y |
| Physical Description: | |
| Page Count: | 24 |
| Publication Date: | 2010 |
| Document Type: | Journal Articles Reports - Evaluative |
| Descriptors: | Models, Cognitive Processes, Psychology, Monte Carlo Methods, Computer Science, Bayesian Statistics, Inferences, Classification, Learning, Computation, Mathematics, Sampling |
| DOI: | 10.1037/a0020511 |
| ISSN: | 0033-295X |
| Abstract: | Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models is thus explaining how optimal solutions can be approximated by psychological processes. We outline a general strategy for answering this question, namely to explore the psychological plausibility of approximation algorithms developed in computer science and statistics. In particular, we argue that Monte Carlo methods provide a source of "rational process models" that connect optimal solutions to psychological processes. We support this argument through a detailed example, applying this approach to Anderson's (1990, 1991) rational model of categorization (RMC), which involves a particularly challenging computational problem. Drawing on a connection between the RMC and ideas from nonparametric Bayesian statistics, we propose 2 alternative algorithms for approximate inference in this model. The algorithms we consider include Gibbs sampling, a procedure appropriate when all stimuli are presented simultaneously, and particle filters, which sequentially approximate the posterior distribution with a small number of samples that are updated as new data become available. Applying these algorithms to several existing datasets shows that a particle filter with a single particle provides a good description of human inferences. (Contains 11 figures, 3 tables and 2 footnotes.) |
| Abstractor: | As Provided |
| Number of References: | 82 |
| Entry Date: | 2010 |
| Accession Number: | EJ903778 |
| Database: | ERIC |
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