Exemplar models as a mechanism for performing Bayesian inference
Shi, Lei, Griffiths, Thomas L., Feldman, Naomi H. and Sanborn, Adam N.. (2010) Exemplar models as a mechanism for performing Bayesian inference. Psychonomic bulletin & review, Vol.17 (No.4). pp. 443-64. ISSN 1531-5320Full text not available from this repository.
Probabilistic models have recently received much attention as accounts of human cognition. However, most research in which probabilistic models have been used has been focused on formulating the abstract problems behind cognitive tasks and their optimal solutions, rather than on mechanisms that could implement these solutions. Exemplar models are a successful class of psychological process models in which an inventory of stored examples is used to solve problems such as identification, categorization, and function learning. We show that exemplar models can be used to perform a sophisticated form of Monte Carlo approximation known as importance sampling and thus provide a way to perform approximate Bayesian inference. Simulations of Bayesian inference in speech perception, generalization along a single dimension, making predictions about everyday events, concept learning, and reconstruction from memory show that exemplar models can often account for human performance with only a few exemplars, for both simple and relatively complex prior distributions. These results suggest that exemplar models provide a possible mechanism for implementing at least some forms of Bayesian inference.
|Item Type:||Journal Article|
|Subjects:||B Philosophy. Psychology. Religion > BF Psychology
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
|Divisions:||Faculty of Science > Psychology|
|Library of Congress Subject Headings (LCSH):||Categorization (Psychology) -- Testing, Bayesian statistical decision theory, Inference, Human information processing|
|Journal or Publication Title:||Psychonomic bulletin & review|
|Publisher:||Springer New York LLC|
|Page Range:||pp. 443-64|
|Funder:||United States. Air Force. Office of Scientific Research|
|Grant number:||FA9550-07-1-0351 (AFOSR)|
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