9.52-C
Computational Cognitive Science
Spring 2003
Topics
- Introduction and Organizational Meeting
- Tutorial on Probability Theory, Bayesian Inference, Bayes Nets
- Induction
- Similarity
- Concepts
- Causality and Categorization
- Causal Induction
- Theories
- Inductive Reasoning in Biology
- Conceptual Change in Biology
- Word Learning
- Intuitive Physics: Objects, Mass/Density
- Theory of Mind
- Number
Readings
- Ahn, W. and Luhmann, C. C. (in press). “Demystifying Theory-based Categorization.” Building object categories in developmental time. Edited by L. Gershkoff-Stowe, and D. Rakison.
- Anderson, J. R. (1991). “The Adaptive Nature of Human Categorization.” Psychological Review 98, 409-429.
- Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press. Sections 1.0, 1.1 (pages 1-4) 1.8-9 (pages 17-26), 2.0-3 (pages 33-45), 2.5 (pages 49-59).
- Charniak, E. (1991). “Bayesian Networks without Tears.” AI Magazine.
- Gelman, S. (2003). The Essential Child. New York: Oxford University Press. Chapter 1 (pages 3-18) and Chapter 3 (pages 60-88).
- Goodman, N. (1955). “The New Riddle of Induction.” In Goodman , Fact, Fiction, and Forecast . Cambridge, MA: Harvard University Press, Chapter 3.
- Gopnik, A. and Glymour, C. (2002). “Causal Maps and Bayes Nets: A Cognitive and Computational Account of Theory-formation.” The Cognitive Basis of Science. Edited by Carruthers et al. Cambridge: Cambridge University Press.
- Gopnik, A., Glymour, C., Sobel, D., Schulz, L. E., Kushnir, T., and Danks, D. (in press). “A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.” Psychological Review.
- Heckerman, D. (1995). A Tutorial on Learning Bayesian Networks. Technical Report MSR-TR-95-06, Microsoft Research.
- Jefferys, W. and Berger, J. (1992). “Ockham’s Razor and Bayesian Analysis.” American Scientist 80 , 64-72.
- Kemp, C. and Tenenbaum, J. B. (2003). “Theory-based Induction.” In Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.
- Laurence, S. and Margolis, E. (1999). “Concepts and Cognitive Science.” In Concepts: Core Readings. Edited by E. Margolis and S. Laurence. Cambridge, MA.: Bradford Books/MIT Press, pages 3-81.
- Medin, D.L., Coley, J.D., Storms, G., and Hayes, B. (in press). “A Relevance Theory of induction.” Psychonomic Bulletin and Review.
- Osherson, D.N., Smith, E.E., & Shafir, E. 1986.“Some Origins of Belief.” Cognition 24, 3, 197-224.
- Osherson, D. N., Smith, E. E., Wilkie, O., Lopez, A., and Shafir, E. (1990). “Category-based Induction.” Psychological Review 97, 185-200.
- Pearl, J. (2000). Causality: Models, Reasoning, and Inference. New York: Cambridge University Press, pages 1-40.
- Quine, W. V. O. (1969). “Natural Kinds.” Chapter 5 in Ontological Relativity and Other Essays . New York: Columbia University Press.
- Rehder, B. (in press). “A Causal-model Theory of Conceptual Representation and Categorization.” Journal of Experimental Psychology: Learning, Memory, and Cognition.
- Russell, S., and Norvig, P. (2002). Artificial Intelligence: A Modern Approach. Excerpt on relational probability models (pages 519-522).
- Shepard, R. N. (1980). “Multidimensional Scaling, Clustering, and Tree-fitting.” Science 210, 390-398.
- Shepard, R. N. (1987). “Towards a Universal Theory of Generalization for Psychological Science.” Science 237, 1317-1323.
- Sivia, D. S. (1996). Data Analysis: A Bayesian Tutorial. Oxford University Press. Pages 1-22.
- Sloman, S., and Ahn, W. K. (1998). “Feature Centrality and Conceptual Coherence.” Cognitive Science 22, 189-228.
- Tenenbaum, J. B. (2003). Introduction to Probability and Bayesian Inference.
- Tversky, A. (1977). “Features of Similarity.” Psychological Review 84, 327-352.
- Tenenbaum, J. B. (2000). “Rules and Similarity in Concept Learning.” In Advances in Neural Information Processing Systems 12 . Edited by S. A. Solla, T. K. Leen, and K.-R. Muller. Cambridge, MA: MIT Press, pages 59-65.
- Tenenbaum, J. B., and Griffiths, T. L. (2001). “Structure Learning in Human Causal Induction.” Advances in Neural Information Processing Systems 13.
- Tenenbaum, J. B., and Griffiths, T. L. (2003). “Theory-based Causal Inference.” Advances in Neural Information Processing Systems 15.
- Tenenbaum, J. B., and Niyogi, S. (2003). “Learning Causal Laws.” Proceedings of the Twenty-fifth Annual Conference of the Cognitive Science Society.
Sources
- Course catalogue ↗Historical OpenCourseWare
- MIT OCW calendar ↗
- MIT OCW readings ↗
- MIT OCW syllabus ↗
- Historical MIT OpenCourseWare offering. Named reading entries and schedule topics are automatically extracted from the linked source pages.