TY - JOUR
T1 - Exemplar models as a mechanism for performing Bayesian inference
AU - Shi, Lei
AU - Griffiths, Thomas L.
AU - Feldman, Naomi H.
AU - Sanborn, Adam N.
N1 - Funding Information:
This research was supported by Grant FA9550-07-1-0351 from the Air Force Office of Scientific Research. Preliminary results from Simulations 1 and 4 were presented at the 30th Annual Conference of the Cognitive Science Society (Shi, Feldman, & Griffiths, 2008).
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
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U2 - 10.3758/PBR.17.4.443
DO - 10.3758/PBR.17.4.443
M3 - Article
C2 - 20702863
AN - SCOPUS:77957277972
SN - 1069-9384
VL - 17
SP - 443
EP - 464
JO - Psychonomic Bulletin and Review
JF - Psychonomic Bulletin and Review
IS - 4
ER -