Rational Approximations to Rational Models: Alternative Algorithms for Category Learning.
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 fo...
| Publicado en: | Psychological Review Vol. 117; no. 4; pp. 1144 - 1168 |
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| Autores principales: | , , |
| Formato: | Artículo |
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American Psychological Association
October 2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=511531726&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511531726 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0033295X PYV jtl: Psychological Review issn: 0033295X maglogo: N pubinfo: dt: October 2010 vid: 117 iid: 4 pid: 34 pub: American Psychological Association artinfo: ui: 511531726 10.1037MIX12-051 ppf: 1144 ppct: 24 formats: tig: atl: Rational Approximations to Rational Models: Alternative Algorithms for Category Learning. aug: au: Sanborn, Adam N. Griffiths, Thomas L. Navarro, Daniel J. su: Categorization (Psychology) Cognition Mathematical models Psychological techniques Psychology -- Statistical methods Psychology of learning sug: subj: Categorization (Psychology) Cognition Mathematical models Psychological techniques Psychology -- Statistical methods Psychology of learning ab: 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. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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