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 1531-5320

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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.

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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
ISSN:	1531-5320
Date:	2010
Volume:	Vol.17
Number:	No.4
Page Range:	pp. 443-64
Identification Number:	10.3758/PBR.17.4.443
Status:	Peer Reviewed
Funder:	United States. Air Force. Office of Scientific Research
Grant number:	FA9550-07-1-0351 (AFOSR)
References:	Anderson, J. R. (1990). The adaptive character of thought. Hillsdale, NJ: Erlbaum. Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98, 409-429. Anderson, J. R., & Milson, R. (1989). Human memory: An adaptive perspective. Psychological Review, 96, 703-719. Ashby, F. G., & Alfonso-Reese, L. A. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39, 216-233. Brown, S. D., & Steyvers, M. (2009). Detecting and predicting changes. Cognitive Psychology, 58, 49-67. Daw, N., & Courville, A. C. (2008). The pigeon as particle filter. In J. Platt, D. Koller, Y. Singer, & S. Roweis (Eds.), Advances in neural information processing systems 20 (pp. 369-376). Cambridge, MA: MIT Press. DeLosh, E. L., Busemeyer, J. R., & McDaniel, M. A. (1997). Extrapolation: The sine qua non of abstraction in function learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 23, 968-986. Feldman, N. H., Griffiths, T. L., & Morgan, J. L. (2009). The influence of categories on perception: Explaining the perceptual magnet effect as optimal statistical inference. Psychological Review, 116, 752-782. Fry, D. B., Abramson, A. S., Eimas, P. D., & Liberman, A. M. (1962). The identification and discrimination of synthetic vowels. Language & Speech, 5, 171-189. Gigerenzer, G., & Todd, P. M. (1999). Simple heuristics that make us smart. New York: Oxford University Press. Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17, 767-773. Guenther, F. H., & Gjaja, M. N. (1996). The perceptual magnet effect as an emergent property of neural map formation. Journal of the Acoustical Society of America, 100, 1111-1121. Hemmer, P., & Steyvers, M. (2009). A Bayesian account of reconstructive memory. Topics in Cognitive Science, 1, 189-202. Huttenlocher, J., Hedges, L. V., & Vevea, J. L. (2000). Why do categories affect stimulus judgment? Journal of Experimental Psychology: General, 129, 220-241. Iverson, P., & Kuhl, P. K. (1995). Mapping the perceptual magnet effect for speech using signal detection theory and multidimensional scaling. Journal of the Acoustical Society of America, 97, 553-562. Juslin, P., & Persson, M. (2002). PROBabilities from EXemplars (PROBEX): A “lazy” algorithm for probabilistic inference from generic knowledge. Cognitive Science, 26, 563-607. Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3, 430-454. Körding, K., & Wolpert, D. M. (2004). Bayesian integration in sensorimotor learning. Nature, 427, 244-247. Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22-44. Kruschke, J. K. (2006). Locally Bayesian learning with applications to retrospective revaluation and highlighting. Psychological Review, 113, 677-699. Kuhl, P. K., Williams, K. A., Lacerda, F., Stevens, K. N., & Lindblom, B. (1992). Linguistic experience alters phonetic perception in infants by 6 months of age. Science, 255, 606-608. Levy, R., Reali, F., & Griffiths, T. L. (2009). Modeling the effects of memory on human online sentence processing with particle filters. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems 21 (pp. 937-944). Cambridge, MA: MIT Press. Lewandowsky, S., Griffiths, T. L., & Kalish, M. L. (2009). The wisdom of individuals: Exploring people’s knowledge about everyday events using iterated learning. Cognitive Science, 33, 969-998. Liberman, A. M., Harris, K. S., Hoffman, H. S., & Griffith, B. C. (1957). The discrimination of speech sounds within and across phoneme boundaries. Journal of Experimental Psychology, 54, 358-368. Luce, R. D. (1959). Individual choice behavior. New York: Wiley. Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006). Bayesian inference with probabilistic population codes. Nature Neuroscience, 9, 1432-1438. Marr, D. (1982). Vision. San Francisco: Freeman. Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207-238. Mozer, M., Pashler, H., & Homaei, H. (2008). Optimal predictions in everyday cognition: The wisdom of individuals or crowds? Cognitive Science, 32, 1133-1147. Myung, I. J., & Shepard, R. N. (1996). Maximum entropy inference and stimulus generalization. Journal of Mathematical Psychology, 40, 342-347. Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo methods (Tech. Rep. No. CRG-TR-93-1). Toronto: University of Toronto. Nosofsky, R. M. (1986). Attention, similarity, and the identification– categorization relationship. Journal of Experimental Psychology: General, 115, 39-57. Nosofsky, R. M. (1990). Relations between exemplar-similarity and likelihood models of classification. Journal of Mathematical Psychology, 34, 393-418. Oaksford, M., & Chater, N. (1994). A rational analysis of the selection task as optimal data selection. Psychological Review, 101, 608-631. Robert, C. P., & Casella, G. (1999). Monte Carlo statistical methods. New York: Springer. Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2006). A more rational model of categorization. In R. Sun (Ed.,), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 726- 731). Mahwah, NJ: Erlbaum. Shepard, R. N. (1962). The analysis of proximities: Multidimensional scaling with an unknown distance function: I. Psychometrika, 27, 124- 140. Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317-1323. Shi, L., Feldman, N. H., & Griffiths, T. L. (2008). Performing Bayesian inference with exemplar models. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society (pp. 745-750). Austin, TX: Cognitive Science Society. Shi, L., & Griffiths, T. L. (2009). Neural implementation of hierarchical Bayesian inference by importance sampling. In Y. Bengio, D. Schuurmans, J. Lafferty, C. Williams, & A. Culotta (Eds.), Advances in neural information processing systems 22 (pp. 1669-1677). Cambridge, MA: MIT Press. Shiffrin, R. M., & Steyvers, M. (1997). A model for recognition memory: REM—Retrieving effectively from memory. Psychonomic Bulletin & Review, 4, 145-166. Simon, H. A. (1957). Models of man. New York: Wiley. Smith, E. R., & Zarate, M. A. (1992). Exemplar-based model of social judgment. Psychological Review, 99, 3-21. Steyvers, M., Tenenbaum, J. B., Wagenmakers, E.-J., & Blum, B. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27, 453-489. Tenenbaum, J. B. (1999). A Bayesian framework for concept learning. Unpublished doctoral dissertation, Massachusetts Institute of Technology, Cambridge, MA. Tenenbaum, J. B., & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral & Brain Sciences, 24, 629-640. Yi, S. K. M., Steyvers, M., & Lee, M. D. (2009). Modeling human performance in restless bandits using particle filters. Journal of Problem Solving, 2(2), 33-53. Yuille, A., & Kersten, D. (2006). Vision as Bayesian inference: Analysis by synthesis? Trends in Cognitive Sciences, 10, 301-308. Zemel, R. S., Dayan, P., & Pouget, A. (1998). Probabilistic interpretation of population codes. Neural Computation, 10, 403-430.
URI:	http://wrap.warwick.ac.uk/id/eprint/36004

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