Bayesian methods have undergone tremendous progress in recent years, due largely to mathematical advances in probability and estimation theory (Chater et al. 2006). These advances have allowed theorists to express and derive predictions from far more sophisticated models than previously possible. These models have generated a good deal of excitement for at least two reasons. First, they offer a new interpretation of the goals of cognitive systems, in terms of inductive probabilistic inference, which has revived attempts at rational explanation of human behavior (Oaksford and Chater 2007). Second, Bayesian models may have the potential to explain some of the most complex aspects of human cognition, such as language acquisition or reasoning under uncertainty, where structured information and incomplete knowledge combine in a way that has defied previous approaches (e.g., Kemp and Tenenbaum 2008...
- Chater, N., Tenenbaum, J. B., & Yuille, A. (2006). Probabilistic models of cognition: Conceptual foundations. Trends in Cognitive Sciences, 10(7), 287–291.Google Scholar
- Geisler, W. S., Perry, J. S., Super, B. J., & Gallogly, D. P. (2001). Edge co-occurrence in natural images predicts contour grouping performance. Vision Research, 41, 711–724.Google Scholar
- Jones, M., & Love, B. C. (in press, 2011). Bayesian fundamentalism or enlightenment? On the explanatory status and theoretical contributions of bayesian models of cognition. Behavioral and Brain Sciences.Google Scholar
- Kemp, C., & Tenenbaum, J. B. (2008). The discovery of structural form. Proceedings of the National Academy of Sciences, 105, 10687–10692.Google Scholar
- Kemp, C., Perfors, A., & Tenenbaum, J. B. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10, 307–321.Google Scholar
- Marr, D. (1982). Vision. San Francisco: W.H. Freeman.Google Scholar
- Oaksford, M., & Chater, N. (2007). Bayesian rationality: The probabilistic approach to human reasoning. Oxford: Oxford University Press.Google Scholar