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The strengths of – and some of the challenges for – Bayesian models of cognition

Published online by Cambridge University Press:  12 February 2009

Thomas L. Griffiths
Thomas L. Griffiths
Affiliation:
Department of Psychology, University of California, Berkeley, Berkeley, CA 94720-1650. tom_griffiths@berkeley.eduhttp://cocosci.berkeley.edu
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Abstract

Bayesian Rationality (Oaksford & Chater 2007) illustrates the strengths of Bayesian models of cognition: the systematicity of rational explanations, transparent assumptions about human learners, and combining structured symbolic representation with statistics. However, the book also highlights some of the challenges this approach faces: of providing psychological mechanisms, explaining the origins of the knowledge that guides human learning, and accounting for how people make genuinely new discoveries.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 32 , Issue 1 , February 2009 , pp. 89 - 90
Copyright
Copyright © Cambridge University Press 2009

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References

Anderson, J. R. (1990) The adaptive character of thought. Erlbaum.Google Scholar
Chater, N. & Manning, C. D. (2006) Probabilistic models of language processing and acquisition. Trends in Cognitive Sciences 10:335–44.CrossRefGoogle ScholarPubMed
Chater, N. & Oaksford, M. (1999a) Ten years of the rational analysis of cognition. Trends in Cognitive Science 3:57–65.CrossRefGoogle ScholarPubMed
Friedman, N., Getoor, L., Koller, D. & Pfeffer, A. (1999) Learning probabilistic relational models. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI), ed. Dean, T., pp. 1300–309. Morgan Kaufmann.Google Scholar
Griffiths, T. L. & Ghahramani, Z. (2006) Infinite latent feature models and the Indian buffet process. In: Advances in neural information processing systems, vol. 18, ed. Weiss, Y., Scholkopf, B. & Plaut, J., pp. 475–82. MIT Press.Google Scholar
Griffiths, T. L. & Tenenbaum, J. B. (2005) Structure and strength in causal induction. Cognitive Psychology 51:354–84.CrossRefGoogle ScholarPubMed
Milch, B., Marthi, B. & Russell, S. (2004) BLOG: Relational modeling with unknown objects. In: ICML 2004 workshop on statistical relational learning and its connections to other fields, Banff, Alberta, Canada, ed. Dietterich, T., Getoor, L. & Murphy, K., pp. 67–73. Available at: www.cs.umd.edu/projects/srl2004/srl2004_complete.pdf.Google Scholar
Oaksford, M. & Chater, N. (2007) Bayesian rationality: The probabilistic approach to human reasoning. Oxford University Press.CrossRefGoogle Scholar
Reichenbach, H. (1938) Experience and prediction. University of Chicago Press.Google Scholar
Sanborn, A. N., Griffiths, T. L. & Navarro, D. J. (2006) A more rational model of categorization. In: Proceedings of the 28th Annual Conference of the Cognitive Science Society. Erlbaum.Google Scholar
Shepard, R. N. (1987) Towards a universal law of generalization for psychological science. Science 237:1317–23.CrossRefGoogle Scholar
Shepard, R. N. (1995) Mental universals: Toward a twenty-first century science of mind. In: The science of the mind: 2001 and beyond, ed. Solso, R. L. & Massaro, D. W., pp. 50–62. Oxford University Press.CrossRefGoogle Scholar
Shi, L., Feldman, N. & Griffiths, T. L. (2008) Performing Bayesian inference with exemplar models. In: Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society, ed. Love, B., McRae, K. & Sloutsky, V., pp. 745–50. Cognitive Science Society.Google Scholar
Tenenbaum, J. B., Griffiths, T. L. & Kemp, C. (2006) Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Science 10:309–18.CrossRefGoogle ScholarPubMed