Exemplar models as a mechanism for performing Bayesian inference
Sábháilte in:
| Foilsithe in: | Psychonomic Bulletin & Review vol. 17, no. 4 (Aug 2010), p. 443-464 |
|---|---|
| Príomhchruthaitheoir: | |
| Rannpháirtithe: | , , |
| Foilsithe / Cruthaithe: |
Springer Nature B.V.
|
| Ábhair: | |
| Rochtain ar líne: | Citation/Abstract Full Text Full Text - PDF |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 749769575 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1069-9384 | ||
| 022 | |a 1531-5320 | ||
| 035 | |a 749769575 | ||
| 045 | 2 | |b d20100801 |b d20100831 | |
| 084 | |a 20702863 | ||
| 084 | |a 162336 |2 nlm | ||
| 100 | 1 | |a Shi, Lei | |
| 245 | 1 | |a Exemplar models as a mechanism for performing Bayesian inference | |
| 260 | |b Springer Nature B.V. |c Aug 2010 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. [PUBLICATION ABSTRACT] 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. | |
| 650 | 2 | 2 | |a Attention |
| 650 | 1 | 2 | |a Bayes Theorem |
| 650 | 1 | 2 | |a Cognition |
| 650 | 2 | 2 | |a Concept Formation |
| 650 | 2 | 2 | |a Forecasting |
| 650 | 2 | 2 | |a Generalization (Psychology) |
| 650 | 2 | 2 | |a Humans |
| 650 | 2 | 2 | |a Memory |
| 650 | 1 | 2 | |a Models, Statistical |
| 650 | 2 | 2 | |a Monte Carlo Method |
| 650 | 2 | 2 | |a Normal Distribution |
| 650 | 2 | 2 | |a Pattern Recognition, Visual |
| 650 | 1 | 2 | |a Perception |
| 650 | 2 | 2 | |a Recognition (Psychology) |
| 650 | 2 | 2 | |a Speech Perception |
| 653 | |a Bayesian analysis | ||
| 653 | |a Studies | ||
| 653 | |a Cognition & reasoning | ||
| 653 | |a Models | ||
| 700 | 1 | |a Griffiths, Thomas L | |
| 700 | 1 | |a Feldman, Naomi H | |
| 700 | 1 | |a Sanborn, Adam N | |
| 773 | 0 | |t Psychonomic Bulletin & Review |g vol. 17, no. 4 (Aug 2010), p. 443-464 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/749769575/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/749769575/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/749769575/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |