Psych239: Formal and Computational Approaches in Psychology and
Cognitive Science (Spring 2011)
Instructors: Jay McClelland (firstname.lastname@example.org) and Noah Goodman (email@example.com)
Class meetings: Mondays 3:15-5:05pm.
Mailing list: psych239-spring2011 at lists.stanford.edu.
(Go here to register.)
Depending on the ultimate class size, we expect to have one or two students lead a short (~15min) overview of the readings for each meeting.
Registered students will be expected to write a final paper (roughly 10pp) exploring in depth a topic related to the course.
- March 28
- Griffiths, T. L., Kemp, C., and Tenenbaum, J. B. (2008). Bayesian models of cognition. In Ron Sun (ed.), The Cambridge handbook of computational cognitive modeling.
- Thomas, M. S. C. & McClelland, J. L. (2008). Connectionist models of cognition. In R. Sun (Ed). Cambridge handbook of computational psychology.
Chapter 1 of Marr's book "Vision" (pp 8-28, top).
D. E. & McClelland, J. L. (1985). Levels indeed! A response to
Broadbent. Journal of Experimental Psychology: General, 114, 193-197.
Probabilistic models of cognition. N. D. Goodman and J. B. Tenenbaum
- April 4
Grammar-based Bayesian models of concept learning:
- April 11
A connectionist model of word reading (and naive physics knowledge
Understanding normal and impaired word reading: Computational principles in quasi-regular domains. Plaut, D. C., McClelland, J. L., Seidenberg, M. S., and Patterson, K. (1996). Psychological Review.
Schapiro, A. C. & McClelland, J. L. (2009). A connectionist model of a
continuous developmental transition in the balance scale
task. Cognition, 110(1),
- April 18
Algorithmic-level models of Bayesian inference:
- Perceptual multistability as Markov chain Monte Carlo inference. Gershman, Vul, & Tenenbaum (2009).
- Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010). Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review, 117(4).
Modeling the effects of memory on human online sentence
processing with particle filters. Levy, R., Reali, F., & Griffths,
T. L. (2009).
- April 25
Neural-level models of Connectionist processing:
Laing, C. R. & Chow, C. C. (2002). A spiking neuron model of binocular rivalry. Journal of Computational Neuroscience, 12, 39-53.
Wilson, H. R. (2007). Minimal physiological conditions for binocular
rivalry and rivalry memory. Vision Research.
Vallabha, G. K. & McClelland, J. L. (2007). Success and failure of new speech category learning in adulthood: Consequences of learned Hebbian attractors in topographic maps. Cognitive, Affective and Behavioral Neuroscience, 7, 53-73.
Vallabha, G. K., McClelland, J. L., Pons, F., Werker, J. & Amano, S. (2007). Unsupervised learning of vowel categories from infant-directed speech. Proceedings of the National Academy of Science, 104, 13273-13278.
- May 2
Discussion of Gibbs Sampling and Boltzmann Machines.
- May 9
Flexible category learning.
- Vallabha, G. K., McClelland, J. L., Pons, F., Werker, J. & Amano,
S. (2007). Unsupervised learning of vowel categories from
infant-directed speech. Proceedings of the National Academy of
Science, 104, 13273-13278.
Feldman, N. H., Griffiths, T. L., & Morgan, J. L. (2009). "Learning phonetic categories by learning a lexicon." Proceedings of the 31st Annual Conference of the Cognitive Science Society.
- May 16
Compositionality in connectionist models.
McClelland, J.L., St. John, M., and Taraban, R. (1989). Sentence
Comprehension: A Parallel Distributed Processing Approach. Language
and Cognitive Processes, 4 , 287-335.
Bryant, B. D. & Miikkulainen, R. From Word Stream to Gestalt: A direct semantic parse for complex sentences. Technical Report AI98-274, AI Lab, University of Texas at Austin, Austin, TX, June 2001
- May 23
Productivity and reuse in language: a probabilistic