Perceptual estimation obeys Occam's razor

Frontiers in Psychology

Volumn 4, Issue SEP, 2013, Pages

  Gershman, Samuel J ^a   Niv, Yael ^b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b PRINCETON UNIVERSITY   (United States)

Author keywords

Bayesian inference; Categorization; Perception; Simplicity; Unsupervised learning

Indexed keywords

EID: 84885362378     PISSN: None     EISSN: 16641078     Source Type: Journal    
DOI: 10.3389/fpsyg.2013.00623     Document Type: Article

References (43)

1. Aldous, D. (1985). "Exchangeability and related topics," in école d'été de Probabilités de Saint-Flour XIII (Berlin:Springer), 1-198.
2. Anderson, J. (1990). The Adaptive Character of Thought. Hillsdale, NJ:Lawrence Erlbaum.
3. Anderson, J. (1991). The adaptive nature of human categorization. Psychol. Rev. 98, 409-429. doi:10.1037/0033-295X.98.3.409.
4. Boehner, P. (Ed.). (1957). Ockham:Philosophical Writings. Nelson:Hackett Publishing.
5. Brainard, D. (1997). The psychophysics toolbox. Spat. Vis. 10, 433-436.
6. Brown, S., and Steyvers, M. (2009). Detecting and predicting changes. Cogn. Psychol. 58, 49-67. doi:10.1016/j.cogpsych.2008.09.002.
7. Chater, N., and Vitányi, P. (2003). Simplicity:a unifying principle in cognitive science? Trends Cogn. Sci. 7, 19-22. doi:10.1016/S1364-6613(02)00005-0.
8. Clapper, J., and Bower, G. (1994). Category invention in unsupervised learning. J. Exp. Psychol. Learn. Mem. Cogn. 20, 443-443. doi:10.1037/0278-7393.20.2.443.
9. Daw, N., and Courville, A. (2008). The pigeon as particle filter. Adv. Neural Inform. Process. Syst. 20, 369-376.
10. Doucet, A., De Freitas, N., and Gordon, N. (2001). Sequential Monte Carlo Methods in Practice. New York, NY:Springer Verlag. doi:10.1007/978-1-4757-3437-9.
11. Feigenson, L., Dehaene, S., and Spelke, E. (2004). Core systems of number. Trends Cogn. Sci. 8, 307-314. doi:10.1016/j.tics.2004.08.010.
12. Feldman, J. (2003). The simplicity principle in human concept learning. Curr. Dir. Psychol. Sci. 12, 227-232. doi:10.1046/j.0963-7214.2003.01267.x.
13. Frank, M., Goldwater, S., Griffiths, T., and Tenenbaum, J. (2010). Modeling human performance in statistical word segmentation. Cognition 117, 107-125. doi:10.1016/j.cognition.2010.07.005.
14. Gelman, A., Carlin, J., Stern, H., and Rubin, D. (2004). Bayesian Data Analysis. Boca Raton, FL:Chapman and Hall/CRC.
15. Gershman, S., and Blei, D. (2012). A tutorial on Bayesian nonparametric models. J. Math. Psychol. 56, 1-12. doi:10.1016/j.jmp.2011.08.004.
16. Gershman, S., Blei, D., and Niv, Y. (2010). Context, learning, and extinction. Psychol. Rev. 117, 197-209. doi:10.1037/a0017808.
17. Gershman, S., and Niv, Y. (2010). Learning latent structure:carving nature at its joints. Curr. Opin. Neurobiol. 20, 251-256. doi:10.1016/j.conb.2010.02.008.
18. Gigerenzer, G., and Goldstein, D. G. (1996). Reasoning the fast and frugal way:models of bounded rationality. Psychol. Rev. 103, 650-669. doi:10.1037/0033-295X.103.4.650.
19. Goldstone, R. (1995). Effects of categorization on color perception. Psychol. Sci. 6, 298-304. doi:10.1111/j.1467-9280.1995.tb00514.x.
20. Gureckis, T., and Goldstone, R. (2008). "The effect of the internal structure of categories on perception," in Proceedings of the 30th Annual Conference of the Cognitive Science Society (Austin, TX), 1876-1881.
21. Hemmer, P., and Steyvers, M. (2009). A Bayesian account of reconstructive memory. Top. Cogn. Sci. 1, 189-202. doi:10.1111/j.1756-8765.2008.01010.x.
22. Holland, M., and Lockhead, G. (1968). Sequential effects in absolute judgments of loudness. Atten. Percept. Psychophys. 3, 409-414. doi:10.3758/BF03205747.
23. Huttenlocher, J., Hedges, L., and Duncan, S. (1991). Categories and particulars:prototype effects in estimating spatial location. Psychol. Rev. 98, 352-376. doi:10.1037/0033-295X.98.3.352.
24. Huttenlocher, J., Hedges, L., and Vevea, J. (2000). Why do categories affect stimulus judgment? J. Exp. Psychol. Gen. 129, 220-241. doi:10.1037/0096-3445.129.2.220.
25. Izard, V., and Dehaene, S. (2008). Calibrating the mental number line. Cognition 106, 1221-1247. doi:10.1016/j.cognition.2007.06.004.
26. Jaynes, E. (2003). Probability Theory:The Logic of Science. Cambridge:Cambridge University Press. doi:10.1017/CBO9780511790423.
27. Kass, R., and Raftery, A. (1995). Bayes factors. J. Am. Stat. Associat. 90, 773-795. doi:10.1080/01621459.1995.10476572.
28. Kemp, C., Tenenbaum, J., Niyogi, S., and Griffiths, T. (2010). A probabilistic model of theory formation. Cognition 114, 165-196. doi:10.1016/j.cognition.2009.09.003.
29. Kruschke, J. (1992). Alcove:an exemplar-based connectionist model of category learning. Psychol. Rev. 99, 22-44. doi:10.1037/0033-295X.99.1.22.
30. Li, M., and Vitányi, P. (2008). An Introduction to Kolmogorov Complexity and its Applications. New York, NY:Springer-Verlag. doi:10.1007/978-0-387-49820-1.
31. Liberman, A., Harris, K., Hoffman, H., and Griffith, B. (1957). The discrimination of speech sounds within and across phoneme boundaries. J. Exp. Psychol. 54, 358-368. doi:10.1037/h0044417.
32. Love, B., Medin, D., and Gureckis, T. (2004). SUSTAIN:a network model of category learning. Psychol. Rev. 111, 309-332. doi:10.1037/0033-295X.111.2.309.
33. Medin, D., and Schaffer, M. (1978). Context theory of classification learning. Psychol. Rev. 85, 207-238. doi:10.1037/0033-295X.85.3.207.
34. Moyer, R. S., and Landauer, T. K. (1967). Time required for judgements of numerical inequality. Nature 215, 1519-1520. doi:10.1038/2151519a0.
35. Nosofsky, R. (1986). Attention, similarity, and the identification-categorization relationship. J. Exp. Psychol. Gen. 115, 39-57. doi:10.1037/0096-3445.115.1.39.
36. Nosofsky, R. (1988). Exemplar-based accounts of relations between classification, recognition, and typicality. J. Exp. Psychol. Learn. Mem. Cogn. 14, 700-708. doi:10.1037/0278-7393.14.4.700.
37. Nosofsky, R. (1991). Relation between the rational model and the context model of categorization. Psychol. Sci. 2, 416-421. doi:10.1111/j.1467-9280.1991.tb00176.x.
38. Pitman, J. (2002). Combinatorial Stochastic Processes. Notes for Saint Flour Summer School. Techincal Report 621, Department of Statistics, University of California, Berkeley.
39. Pothos, E., and Chater, N. (2002). A simplicity principle in unsupervised human categorization. Cogn. Sci. 26, 303-343. doi:10.1207/s15516709cog2603_6.
40. Restle, F. (1970). Speed of adding and comparing numbers. J. Exp. Psychol. 83, 274-278. doi:10.1037/h0028573.
41. Sailor, K., and Antoine, M. (2005). Is memory for stimulus magnitude bayesian? Mem. Cogn. 33, 840-851. doi:10.3758/BF03193079.
42. Sanborn, A., Griffiths, T., and Navarro, D. (2010). Rational approximations to rational models:alternative algorithms for category learning. Psychol. Rev. 117, 1144-1167. doi:10.1037/a0020511.
43. Zeithamova, D., and Maddox, W. (2009). Learning mode and exemplar sequencing in unsupervised category learning. J. Exp. Psychol. Learn. Mem. Cog. 35, 731-741. doi:10.1037/a0015005.