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

 

Date

Paper

Speaker

Slides

9/3

Course overview

Byoung-Tak Zhang

9/10

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

Sun Kyu Kim

Bayesian theories of conditioning in a changing world [1]

Wonhee Choe

9/17

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

Kye Sam Jeong

Probabilistic models of language processing and acquisition [1]

Ho-Sik Seok

9/24

No class (Thanksgiving holiday)

 

10/1

(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

10/8

A primer on probabilistic inference [1] [2]

Bado Lee

Probabilistic inference in human semantic memory [1]

Jin-Seok Nam

10/15

No class (School anniversary)

10/22

A Probability primer [3]

Ji-Hoon Lee

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

Eun-Sol Kim

10/29

Probabilistic mind: where next? [2]

Woongchang Yoon

Visual cue integration for depth perception [4]

Wonhee Choe

11/5

No class

 

11/12

Review Paper Presentation

Choe / Kim / Jeong

11/19

Spiking Code [3]

Kye Sam Jeong

Bayesian Models of Sensory Cue Integration [3]

Sun Kyu Kim

11/26

No class

 

12/3

Bayesian Treatments of Neuroimaging Data [3]

Wonhee Choe

Sparse Codes and Spikes [4]

Sun Kyu Kim

12/10

Vision, Psychophysics and Bayes [4]

 Kye Sam Jeong

Bayesian Modelling of Visual Perception [4]

Wonhee Choe

12/17

Final Review Paper Presentation

Choe / Kim / Jeong