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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
Affiliation:
Department of Psychology, University of California, Berkeley, Berkeley, CA 94720-1650. [email protected]://cocosci.berkeley.edu

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
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 3:5765.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. 6773. 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. 5062. 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