Meeting times: Wednesdays, 7-10 pm. New Time!
NOTE: The first, second, and last meetings will be held on Mondays.
Location: Alternating between MIT (NE20-461) and Harvard (WJH 1305).
Instructors: Josh Tenenbaum , Susan Carey
Email: jbt@mit.edu, scarey@wjh.harvard.edu
Office hours: By email appointment.
This class will consider computational models of some of the core structures of human cognition: concepts, causal relationships, word meanings and intuitive theories. We will emphasize questions of inductive learning and inference and the representation of knowledge. Class meetings will mix lectures and discussion, covering both the necessary cognitive science and computational background and confronting state-of-the-art research questions. This class is suitable for intermediate to advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields. Prerequisites: A course in cognitive science, and a course in probability or statistics.
This class is being offered jointly by MIT as 9.52/9.914, taught by Prof. Josh Tenenbaum, and by Harvard as Psychology 2180, taught by Prof. Susan Carey. Meetings will alternate between MIT and Harvard, as indicated on the schedule below.
Before each week's meeting, every student attending the class is required to submit a short (approximately one page) response to one of the topics for that week's discussion. These notes are due by 2 pm on the day of class. They should be submitted electronically by posting to the class discussion board. Students are encouraged to read as many of these responses as they can before class, and to respond to others' posts using the interactive features of the discussion board.
Students will also be required to submit a term project that confronts some open research question related to the topics and approaches discussed in class.
Sept. 8 (Monday, MIT): Introduction and organizational meeting
Readings: none.
Sept. 15 (Monday, MIT): Tutorial on probability theory, Bayesian inference, Bayes nets
Readings:
Tenenbaum,
J. B. (2003). Introduction to probability and Bayesian inference.
Pearl, J. (2000). Causality: models, reasoning, and
inference. New York: Cambridge University Press, pages 1-21
(Sections 1.1, 1.2).
Sivia, D. S. (1996). Data Analysis: A Bayesian Tutorial. Oxford University Press. Pages 1-22.
Charniak, E. (1991). Bayesian networks without tears. AI Magazine.
Sept. 17 (Wednesday, Harvard): Induction
Readings:
Goodman, N. (1955). The new riddle of induction. In
Goodman, Fact, Fiction, and Forecast . Cambridge, MA: Harvard
University Press, Chapter 3.
Quine, W. V. O. (1969). Natural kinds. In
Ontological Relativity and Other Essays . New York: Columbia
University Press, Chapter 5.
Osherson, D.N., Smith, E.E., & Shafir, E. 1986. Some
origins of belief. Cognition, 24, 3, 197-224.
Jefferys, W. and Berger, J. (1992). Ockham's Razor
and Bayesian Analysis. American Scientist, 80 , 64-72.
A note on what to read.
Topics for discussion.
Sept. 24 (Wednesday, MIT): Similarity
Readings:
Tversky, A. (1977). Features of similarity. Psychological
Review, 84, 327-352.
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.
Tenenbaum, J. B. and Griffiths, T. L. (2001). Generalization,
similarity, and Bayesian inference. Behavioral and Brain
Sciences, 24, 629-641.
Oct. 1 (Wednesday, Harvard): Concepts
Readings:
Laurence, S. and Margolis, E. (1999). Concepts and Cognitive Science. In
E. Margolis & S. Laurence (eds.), Concepts: Core Readings.
Cambridge, MA.: Bradford Books/MIT Press, pages 3-81.
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).
Tenenbaum, J. B. (2000). Rules and similarity in concept
learning. In S. A. Solla, T. K. Leen, K.-R. Muller (eds.),
Advances in Neural Information Processing Systems 12 .
Cambridge, MA: MIT Press, pages 59-65.
Margolis, E. and Laurence, S. (2002). Radical
concept nativism. Cognition, 86, 22-55.
Anderson, J. R. (1991). The adaptive nature of human
categorization. Psychological Review, 98, 409-429.
Oct. 8 (Wednesday, MIT): Causality and Categorization
Readings:
Ahn,
W. and Luhmann, C. C. (in press). Demystifying theory-based
categorization. In L. Gershkoff-Stowe & D. Rakison (Eds.) Building
object categories in developmental time.
Sloman, S., Love, B., and Ahn, W. (1998). Feature centrality and
conceptual coherence. Cognitive Science, 22, 189-228.
Rehder, B. (in
press). A causal-model theory of conceptual representation and
categorization. Journal of Experimental Psychology: Learning,
Memory, and Cognition.
Gelman, S. (2003). The Essential Child. New York:
Oxford University Press. Chapter 1 (pages 3-18) and Chapter 3 (pages 60-88).
Heckerman, D. (1995). A tutorial on learning Bayesian networks.
Supplemental:
Strevens, M. (2000). The Naive Aspect of Essentialist Theories. Cognition 74, 149-175.
Supplemental: Ahn,
W., Kalish, C., Gelman, S. A., Medin, D. L., Luhmann, C., Atran, S.,
Coley, J. D., Shafto, P. (2001). Why essences are essential in the
psychology of concepts. Cognition, 82, 59-69.
Oct. 15 (Wednesday, Harvard): Causal induction
Readings:
Glymour, C. (2003). Learning, prediction and
causal Bayes nets. Trends in Cognitive Science, 7, 43-48.
Gopnik, A., Glymour, C., Sobel, D., Schulz, L. E., Kushnir, T., &
Danks, D. (in press). A theory of causal learning in children:
Causal maps and Bayes nets. Psychological Review.
Pearl, J. (2000). Causality: models, reasoning, and
inference. New York: Cambridge University Press, pages 1-40.
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.
Oct. 22 (Wednesday, MIT): Theories
Readings:
Keil, F. (2003). Folkscience: Coarse interpretations of a complex
reality. Trends in Cognitive Sciences, 7, 368-373.
Wellman, H. M. and Gelman, S. A. (1992). Cognitive
development: foundational theories of core domains. Annual Review
of Psychology, 43, 337-75.
Gopnik, A. and Glymour, C. (2002). Causal maps and
Bayes nets: a cognitive and computational account of theory-formation.
In Carruthers et al. (eds.), The Cognitive Basis of Science.
Cambridge: Cambridge University Press.
McClelland, J. L. and Rogers, T. T. (2003). The Parallel Distributed Processing Approach to Semantic Cognition. Nature Reviews Neuroscience, 4, 310-322.
Tenenbaum, J. B. and Niyogi, S. (2003). Learning causal laws.
Proceedings of the Twenty-Fifth Annual Conference of the Cognitive
Science Society.
Russell, S. and Norvig, P. (2002). Artificial
Intelligence: A Modern Approach . Excerpt on relational
probability models (pages 519-522).
Oct. 29 (Wednesday, Harvard): Inductive reasoning in biology
Readings:
Osherson, D. N., Smith, E. E., Wilkie, O., Lopez, A., and Shafir, E. (1990). Category-based induction. Psychological Review, 97, 185-200.
Atran, S. (1998). Folk Biology and the Anthropology of Science:
Cognitive Universals and Cultural Particulars. Behavioral and
Brain Sciences.
Kemp, C. and Tenenbaum, J. B. (2003). Theory-based induction. In
Proceedings of the Twenty-Fifth Annual Conference of the Cognitive
Science Society.
Medin, D.L., Coley, J.D., Storms, G. & Hayes, B. (In Press). A
Relevance Theory of induction. Psychonomic Bulletin and Review.
Smith,
E. E., Shafir, E., and Osherson, D. N. (1993). Similarity,
plausibility, and judgments of probability. Cognition, 49, 2, 67-96.
Nov. 5 (Wednesday, MIT): Conceptual change in biology
Readings: TBA
Nov. 12 (Wednesday, Harvard): Word learning
Readings: TBA
Nov. 19 (Wednesday, MIT): Intuitive physics - objects, mass/density
Readings: TBA
Dec. 3 (Wednesday, Harvard): Theory of mind
Readings: TBA
Dec. 8 (*Monday*, MIT): Number
Readings: TBA