Syllabus [pdf] (updated 1.26.18)

Instructors

Readings

2/1 Bayesian modeling in the mind-brain sciences

• Feldman, J. “Bayesian models of perception: A tutorial introduction,” in Wagemans (ed.), Handbook of Perceptual Organization, Oxford, 2015.
• Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279–1285.
• Papineau, D. Philosophical Devices, part III, “The nature and uses of probability,” OUP 2012 – especially chapters 7 & 8.

2/8 Perception

• Kersten, D., Mamassian, P., & Yuille, A. (2004). Object perception as Bayesian inference. Annual Review of Psychology, 55, 271–304.
• Rescorla, M. “Bayesian perceptual psychology,” in Matthen (ed.), The Oxford Handbook of the Philosophy of Perception, Oxford 2015.
• Weiss, Y., Simoncelli, E. P., & Adelson, E. H. (2002). Motion illusions as optimal percepts. Nature Neuroscience, 5, 598–604.

2/15 Higher-Level Cognition

• Frank, M. C., & Goodman, N. D. (2012). Predicting pragmatic reasoning in language games. Science, 336, 998.
• Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17, 767–773.
• Nichols, S., & Samuels, R. (2017). Bayesian psychology and human rationality. In Hung & Lane (eds.), Rationality: Constraints and Contexts. Elsevier.

2/22 Development

• Gopnik, A., & Wellman, H. M. (2012). Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory. Psychological Bulletin, 138, 1085–1108.
• Gweon, H., & Schulz, L. E. (2011). 16-month-olds rationally infer causes of failed actions. Science, 332, 1524.

3/1 Universal Bayesianism

• Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36, 181–204.
• Orlandi, N., & Lee, G. (forthcoming) How radical is predictive processing?. Andy Clark and Critics.
• Take another look at: Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279–1285.

3/8 Critiques from Psychology

• Bowers, J. S., & Davis, C. J. (2012). Bayesian just-so stories in psychology and neuroscience. Psychological Bulletin, 138, 389–414.
• Reply: Griffiths, T. L., Chater, N., Norris, D., & Pouget, A. (2012). How the Bayesians got their beliefs (and what those beliefs actually are): Comment on Bowers and Davis (2012). Psychological Bulletin, 138, 415–422.
• Reply to the reply: Bowers, J. S., & Davis, C. J. (2012). Is that what Bayesians believe? Reply to Griffiths, Chater, Norris, and Pouget (2012). Psychological Bulletin, 138, 423–426.
• Marcus, G. F., & Davis, E. (2013). How robust are probabilistic models of higher-level cognition? Psychological Science, 24, 2351–2360.
• Reply: Goodman, N. D., Frank, M. C., Griffiths, T. L., Tenenbaum, J. B., Battaglia, P. W., & Hamrick, J. B. (2015). Relevant and robust: A response to Marcus and Davis (2013). Psychological Science, 26, 539–541.
• Reply to the reply: Marcus, G. F., & Davis, E. (2015). Still searching for principles. Psychological Science, 26, 542–544.

3/15 Critiques from Philosophy

• Glymour, C. 2007. Bayesian Ptolemaic psychology. In Harper & Wheeler (eds.), Probability and inference: Essays in Honor of Henry E. Kyburg, Jr., pp. 123–41. Kings College Publishers.
• Mandelbaum, E. (manuscript). Troubles with Bayesianism: An introduction to the psychological immune system.

3/22 NO CLASS (Spring Break)

3/29 Levels of explanation (+ Noah Goodman's talk [password protected])

• Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015). Rational use of cognitive resources: Levels of analysis between the computational and the algorithmic. Topics in Cognitive Science, 7, 217–229.
• Marr, D. 1982. Vision, MIT Press, Sec 1.2.

4/5 Sampling

• Bonawitz, E., Denison, S., Griffiths, T. L., & Gopnik, A. (2014). Probabilistic models, learning algorithms, and response variability: Sampling in cognitive development. Trends in Cognitive Sciences, 18, 497–500.
• Griffiths, T. L., Vul, E., & Sanborn, A. N. (2012). Bridging levels of analysis for probabilistic models of cognition. Current Directions in Psychological Science, 21, 263–268.
• Sanborn, A. N., & Chater, N. (2016). Bayesian brains without probabilities. Trends in Cognitive Sciences, 20, 883–893.
• Vul, E., & Pashler, H. (2008). Measuring the crowd within: probabilistic representations within individuals. Psychological Science, 19, 645–647.

4/12 Empirical critiques 1: "Anti-Bayesian" phenomena?

• Brayanov, J. B., & Smith, M. A. (2010). Bayesian and “anti-Bayesian” biases in sensory integration for action and perception in the size–weight illusion. Journal of Neurophysiology, 103, 1518–1531.
• Peters, M. A. K., Ma, W. J., & Shams, L. (2016). The size-weight illusion is not anti-Bayesian after all: A unifying Bayesian account. PeerJ, 4, e2124. [+ supplemental material]

4/19 Empirical critiques 2

• Burr, D., & Ross, J. 2008. A visual sense of number. Current Biology, 18, 1-4.
• Webster, M. A. (2015). Visual adaptation. Annual Review of Vision Science, 1, 547–567.
• Perhaps a short presentation by Firestone & Gross!

4/26 Yet more discussion of empirical critiques

• Kemp, C., Perfors, A., & Tenenbaum, J. 2007. Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10, 307–321.
• Wei, X.-X., & Stocker, A. A. (2015). A Bayesian observer model constrained by efficient coding can explain “anti-Bayesian” percepts. Nature Neuroscience, 18, 1509–1517.