Introductory Lectures

  • Shepard, R. (1987). Toward a universal law of generalization for psychological science. Science 237(4820): 1317-1323.
  • Tenenbaum, J., &; Griffiths, T. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences 24(2): 629-641.
  • Navarro, D., Lee, M., Dry, M., &; Schultz, B. (2008). Extending and testing the Bayesian theory of generalization. In V. Sloutsky, B. Love, &; K. McRae (eds.) Proceedings of the 30th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society: 1746-1751.
  • McClelland, J. (2009). The place of modeling in cognitive science. Topics in Cognitive Science 1: 11-38.
  • McClelland, J., Botvinick, M., Noelle, D., Plaut, D., Rogers, T., Seidenberg, M., &; Smith, L. (2010). Letting structure emerge: Connectionist and dynamical systems approaches to cognition. Trends in Cognitive Sciences 14(8): 348-356.
  • Griffiths, T., Chater, N., Kemp, C., Perfors, A., &; Tenenbaum, J. (2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences 14(8): 357-364
  • Rumelhart, D.E., Hinton, G.E. &; Williams, R.J. (1986). Learning representations by backpropagating errors. Nature, 323, 533-536
  • McClelland, J., Rogers, T. (2003).The Parallel Distributed Processing approach to semantic cognition. Nature Reviews Neuroscience 4: 310-322.
  • Fodor, J., &; Pylyshyn, Z. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition 28: 3-71.
  • Russell, S., &; Norvig, P. (1995). Artificial Intelligence: A modern approach (1st edition). Chapter 14.2-14.6.
  • Mackay, D.J.C. (2003). Information Theory, Inference and Learning Algorithms. Cambridge, UK: Cambridge University Press. Chapter 28.
  • MacKay, D.J.C. (2003). Information Theory, Inference and; Learning Algorithms. Cambridge, UK: Cambridge University Press. Chapter 2.
  • Jefferys, W., &; Berger, J. (1991). Sharpening Ockham’s Razor on a Bayesian Strop. Purdue University Tech. Report #91-44C.
  • Perfors, A., Tenenbaum, J., Griffiths, T.L., Xu, F. (2011) A tutorial introduction to Bayesian models of cognitive development. Cognition.
  • MacKay, D. (2003). Information theory, inference, and learning algorithms. Cambridge University Press. Chapter 28.
  • MacKay, D. (2003). Information theory, inference, and learning algorithms. Cambridge University Press. Chapter 20.
  • MacKay, D. (2003). Information theory, inference, and learning algorithms. Cambridge University Press. Chapters 22 and 23.
  • Jefferys, W., &; Berger, J. (1991). Sharpening Ockham’s Razor on a Bayesian Strop. Purdue University Tech. Report #91-44C.

    Language

  • Kuhl, P. (2004). Early language acquisition: Cracking the speech code. Nature Review Neuroscience 5:831-843.
  • Feldman, N., Griffiths, T., &; Morgan, J. (2009). Learning phonetic categories by learning a lexicon. Proceedings of the 31st Annual Conference of the Cognitive Science Society.
  • Chater, N., &; Manning, C. (2006). Probabilistic models of language processing and acquisition. Trends in Cognitive Sciences 10(7): 335-344.
  • Manning, C., & Schutze, H. (1999). Foundations of statistical natural language processing. MIT Press. Chapter 5.
  • Saffran, J., Aslin, R., & Newport, E. (1996). Statistical learning by 8-month-old infants. Science 274(5294): 1926-1928.
  • Goldwater, S., Griffiths, T., & Johnson, M. (2009). A Bayesian framework for word segmentation: Exploring the effects of context. Cognition 112: 21-54.
  • Frank, M., S. Goldwater, T. Griffiths, J. Tenenbaum (2007). Modeling human performance in statistical word segmentation. In Proceedings of the 29th Annual Conference of the Cognitive Science Society.
  • Brent, M. (1999). An efficient, probabilistically sound algorithm for segmentation and word discovery. Machine Learning 34: 71-105.
  • Venkataraman, A. (2001). A statistical model for word discovery in transcribed speech. Computational Linguistics 27(3): 351-372.
  • Russell & Norvig (1995). Artificial intelligence: A modern approach. (1st edition). Chapter 24, selected pages.
  • Manning & Schutze (1999). Foundations of statistical natural language processing. Chapter 9.
  • Manning & Schutze (1999). Foundations of statistical natural language processing. Chapter 11.
  • Russell & Norvig (1995). Artificial Intelligence: A Modern Approach. Chapter 22, selected pages.

    Concepts and Categories

  • Hastie, T.H., Tibshirani, R. & Friedman (2001). The Elements of Statistical Learning. Springer. Chapters 2, 4, 6, 13 and 23.
  • Sanborn, A. N., Griffiths T. L. & Navarro D. J. (in press). Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review
  • Kemp, C., Griffiths, T., & Tenenbaum, J. (2004). Discovering latent classes in relational data. AI Memo 2004-019, CSAIL (MIT).
  • Kemp, C., Tenenbaum, J., Griffiths, T., Yamada, T., & Ueda, N. (2006). Learning systems of concepts with an infinite relational model. AAAI.
  • Shafto, P., Kemp, C., Mansinghka, V., Gordon, M., & Tenenbaum, J. (2007). Learning cross-cutting systems of categories. Advances in Neural Information Processing Systems 20.
  • Kemp, C., Perfors, A., & Tenenbaum, J. (2007). Learning overhypotheses. Developmental Science 10(3): 307-321.
  • Perfors, A., & Tenenbaum, J. (2009). Learning to learn categories. Proceedings of the 31st Annual Conference of the Cognitive Science Society.
  • Kemp, C., & Tenenbaum, J. (2008). The discovery of structural form. Proceedings of the National Academy of Sciences 105(31): 10687-10692.