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.

  • Evaluation:    

-   Paper presentations (40%)

-   Two miniprojects (40%)

-   Participation in discussion (20%)

  • Reading List:

[1] Trends in Cognitive Sciences Special Issue on Probabilistic Models of Cognition, Vol. 10, No. 7, July 2006 (see below for the list of papers)

[2] The Probabilistic Mind: Prospects for Bayesian Cognitive Science, N. Chater & M. Oaksford (Eds.), Oxford University Press, 2008.

[3] Bayesian Brain: Probabilistic Approaches to Neural Coding, K. Doya, S. Ishii, A. Pouget, & R. Rao (Eds.), MIT Press, 2007.

[4] Probabilistic Models of the Brain: Perception and Neural Function, R. Rao, B. A. Olshausen, M. S. Lewicki (Eds.), MIT Press, 2002.

 ·  Lecture Schedule







Course overview

Byoung-Tak Zhang


Probabilistic models of cognition: Conceptual foundations & Where next? [1]

Sun Kyu Kim

Bayesian theories of conditioning in a changing world [1]

Wonhee Choe


Vision as Bayesian inference: analysis by synthesis? [1]

Kye Sam Jeong

Probabilistic models of language processing and acquisition [1]

Ho-Sik Seok


No class (Thanksgiving holiday)



(1) K. Friston, Hierarchical Models in the Brain. PLoS 2008

(2) K. Friston & K. E., Stephan, Free-energy and the brain. Synthese 2007

(3) K. Friston & S. Kiebel, Cortical circuits for perceptual inference. Neural Networks 2009

(4) K. Friston, The free-energy principle: a unified brain theory? Nature Rev. NeuroSci. 2010

Joon Shik Kim


A primer on probabilistic inference [1] [2]

Bado Lee

Probabilistic inference in human semantic memory [1]

Jin-Seok Nam


No class (School anniversary)


A Probability primer [3]

Ji-Hoon Lee

A decision-by-sampling account of decision under risk [2]

Eun-Sol Kim


Probabilistic mind: where next? [2]

Woongchang Yoon

Visual cue integration for depth perception [4]

Wonhee Choe


No class



Review Paper Presentation

Choe / Kim / Jeong


Spiking Code [3]

Kye Sam Jeong

Bayesian Models of Sensory Cue Integration [3]

Sun Kyu Kim


No class



Bayesian Treatments of Neuroimaging Data [3]

Wonhee Choe

Sparse Codes and Spikes [4]

Sun Kyu Kim


Vision, Psychophysics and Bayes [4]

 Kye Sam Jeong

Bayesian Modelling of Visual Perception [4]

Wonhee Choe


Final Review Paper Presentation

Choe / Kim / Jeong