Bayesian Cognition(Artificial Intelligence and Cognitive Processes, Fall 2010)
Bayesian probability theory is becoming increasingly popular not only in building learning and reasoning systems under uncertainty in artificial intelligence (AI) but also in efforts to develop a model of how the brain and mind work in the face of uncertainty. In this graduate course we review the probabilistic models of brain, mind, and cognition by reading and discussing the classical and recent papers on this important new paradigm to cognitive science. The papers will be selected and read by the students according to their own research interest. The reading list is given below and includes the papers, for example, in the July 2006 special issue of the Trends in Cognitive Sciences on Probabilistic Models of Cognition. The course attendants are expected to present the papers and submit two review papers on specific subareas of cognitive science, such as inductive learning, categorization, concept formation, sensorimotor integration, language acquisition, perceptual learning, sentence processing, visual processing, and decision making.
- Paper presentations (40%)
- Two miniprojects (40%)
- Participation in discussion (20%)
 Trends in Cognitive Sciences Special Issue on Probabilistic Models of Cognition, Vol. 10, No. 7, July 2006 (see below for the list of papers)
 The Probabilistic Mind: Prospects for Bayesian Cognitive Science, N. Chater & M. Oaksford (Eds.), Oxford University Press, 2008.
 Bayesian Brain: Probabilistic Approaches to Neural Coding, K. Doya, S. Ishii, A. Pouget, & R. Rao (Eds.), MIT Press, 2007.
 Probabilistic Models of the Brain: Perception and Neural Function, R. Rao, B. A. Olshausen, M. S. Lewicki (Eds.), MIT Press, 2002.