BCS 532: Lecture Schedule

Only students who are enrolled in the course may access the readings online. You must be logged into Blackboard to download the course readings.

Class 1: Introduction: Jacobs, R. A. & Kruschke, J. K. (2011). Bayesian learning theory applied to human cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 2, 8-21.; Lee, M. D. (2008). Three case studies in the Bayesian analysis of cognitive models. Psychonomic Bulletin and Review, 15, 1-15.
Class 2: Language I: Goldwater, S., Griffiths, T. L., & Johnson, M. (2009). A Bayesian framework for word segmentation: Exploring the effects of context. Cognition, 112, 21-54.
Class 3: Language II: Jaeger, T. F. (2010). Redundancy and reduction: Speakers manage syntactic information density. Cognitive Psychology, 61, 23-62.
Class 4: Language III: Kemp, C. & Regier, T. (2012). Kinship categories across languages reflect general communicative principles. Science, 336, 1049-1054.; Piantadosi, S. T., Tily, H., & Gibson, E. (2011). Word lengths are optimized for efficient communication. Proceedings of the National Academy of Sciences USA, 108, 3526-3529.; Piantadosi, S. T., Tily, H., & Gibson, E. (2012). The communicative function of ambiguity in language. Cognition, 122, 280-291.
Class 5: Perception I: Kersten, D., Mamassian, P., & Yuille, A. (2004). Object perception as Bayesian inference. Annual Review of Psychology, 55, 271-304.
Class 6: Perception II: Kording, K. P. & Wolpert, D. M. (2004). Bayesian integration in sensorimotor learning. Nature, 427, 244-247.; Knill, D. C. (2007). Robust cue integration: A Bayesian model and evidence from cue-conflict studies with stereoscopic and figure cues to slant. Journal of Vision, 7(7):5, 1-24.
Class 7: Perception III: Austerweil J. L. & Griffiths, T. L. (2009). The effect of distributional information on feature learning. Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society.; Yildirim, I. & Jacobs, R. A. (2012). A rational analysis of the acquisition of multisensory representations. Cognitive Science, 36, 305-332.
Class 8: Perception IV: Van den Berg, R., Shin, H., Chou, W.-C., George, R., and Ma, W. J. (2012). Variability in encoding precision accounts for visual short-term memory limitations. Proceedings of the National Academy of Sciences USA, 109, 8780-8785.; Sims, C. R., Jacobs, R. A., & Knill, D. C. (2011). An ideal observer model of visual short-term memory predicts human capacity-precision tradeoffs. Proceedings of the Thirty-Third Annual Conference of the Cognitive Science Society.; Orhan, A. E. & Jacobs, R. A. (2011). A nonparametric Bayesian model of visual short-term memory. Proceedings of the Thirty-Third Annual Conference of the Cognitive Science Society.
Class 9: Sampling and Human Cognition: Shi, L., Griffiths, T. L., Feldman, N. H, & Sanborn, A. N. (2010). Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review, 17, 443-464.; Sanborn, A. N., Griffiths, T. L., & Shiffrin, R. (2010). Uncovering mental representations with Markov chain Monte Carlo. Cognitive Psychology, 60, 63-106.
Class 10: Numerical Cognition: Piantadosi, S. T., Tenenbaum, J. B., & Goodman, N. D. (2012). Bootstrapping in a language of thought: A formal model of numerical concept learning. Cognition, 123, 199-217.; Lee, M. D. & Sarnecka, B. W. (2011). Number-knower levels in young children: Insights from Bayesian modeling. Cognition, 120, 391-402.
Class 11: Decision Making and the Brain I: Dayan, P. & Daw, N. D. (2008). Decision theory, reinforcement learning, and the brain. Cognitive, Affective, & Behavioral Neuroscience, 8, 429-453.
Class 12: Decision Making and the Brain II: Gold, J. I. & Shadlen, M. N. (2001). Neural computations that underlie decisions about sensory stimuli. Trends in Cognitive Sciences, 5, 10-16.; Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., Shadlen, M. N., Latham, P. E., & Pouget, A. (2008). Probabilistic population codes for Bayesian decision making. Neuron, 60, 1142-1152.

top