SESSION 14: BAYESIAN MODELING/DYNAMIC SYSTEMS
Slides from class: Bayesian.pdf [PDF]
Background Readings:
Griffiths, T. L., & Yuille, A. (2006). A primer on probabilistic inference. Trends in Cognitive Sciences, 10. Supplement to special issue on Probabilistic Models of Cognition.
Ratcliff, R. (?). Diffusion and random walk processes. A good simple introduction to the diffusion model, but I have no idea where this is published.
References:
Kruschke, J. K. (2006). Locally Bayesian learning with applications to retrospective revaluation and highlighting. Psychological Review, 113, 677-699.
J. B. Tenenbaum (1999). Bayesian modeling of human concept learning. Advances in Neural Information Processing Systems, 11. Kearns, M., Solla, S., and Cohn, D. (eds). Cambridge, MIT Press, 59-65
Perfors, A., Tenenbaum, J. B., & Regier, T. (2006). Poverty of the stimulus? A rational
approach. Proceedings of the Twenty-Eighth Annual Conference of the
Cognitive Science Society.
Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences, 10, 309-318.
Steyvers, M., Griffiths, T. L., & Dennis, S. (2006). Probabilistic inference in human semantic memory. Trends in Cognitive Sciences, 10, 327-334.
Chater, N., & Manning, C. D. (2006). Probabilistic models of language processing and acquisition. Trends in Cognitive Sciences, 10, 335-344.
Smith, L. B., & Thelen, E. (2003). Development as a dynamic system. Trends in Cognitive Sciences, 7, 343-348.
Resources: