Josh Tenenbaum

Paul E. Newton Career Development Professor
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology

Email: jbt AT mit DOT edu
Phone: 617-452-2010 (office), 617-253-8335 (fax)
Mail: Building 46-4015, 77 Massachusetts Avenue, Cambridge, MA 02139
Curriculum Vitae

Research interests

I study the computational basis of human learning and inference. Through a combination of mathematical modeling, computer simulation, and behavioral experiments, I try to uncover the logic behind our everyday inductive leaps: constructing perceptual representations, separating "style" and "content" in perception, learning concepts and words, judging similarity or representativeness, inferring causal connections, noticing coincidences, predicting the future. I approach these topics with a range of empirical methods -- primarily, behavioral testing of adults, children, and machines -- and formal tools -- drawn chiefly from Bayesian statistics and probability theory, but also from geometry, graph theory, and linear algebra. My work is driven by the complementary goals of trying to achieve a better understanding of human learning in computational terms and trying to build computational systems that come closer to the capacities of human learners.

The Computational Cognitive Science Group

Special issue of Trends in Cognitive Science, July 2006 (Vol. 10, Issue 7), on "Probabilistic Models of Cognition".

Graduate Summer School in Probabilistic Models of Cognition, July 9-27, Institute for Pure and Applied Mathematics (UCLA).

Courses

Fall 2007: 9.66/9.914/6.804 Computational Cognitive Science
Spring 2007: 9.012 Cognitive Science
Winter 2006: 9.94 The Cognitive Science of Intuitive Theories

Online papers (listed chronologically - see below for listing by topic)

Manuscripts

• Inductive reasoning about causally transmitted properties. Shafto, P., Kemp, C., Baraff, E. R., Coley, J., and Tenenbaum, J. B. (submitted draft - please email for a copy).
• Action understanding as inverse planning. Baker, C. L., Tenenbaum, J. B., and Saxe, R. R. (submitted draft - please email for a copy).
• Structured statistical models of inductive reasoning. Kemp, C. and Tenenbaum, J. B. (submitted draft - please email for a copy).
• Theory-based causal induction. Griffiths, T. L. and Tenenbaum, J. B. (submitted draft - please email for a copy).
• Using speakers' referential intentions to model early cross-situational word learning. Frank, M., Goodman, N. D., and Tenenbaum, J. B. (submitted draft - please email for a copy).
• The learnability of abstract syntactic principles. A. Perfors, J. B. Tenenbaum, and T. Regier (submitted draft - comments welcome but please do not cite without permission).
• A Bayesian framework for cross-situational word-learning. M. C. Frank, N. D. Goodman, and J. B. Tenenbaum (to appear). Advances in Neural Information Processing Systems 20.
• Learning and using relational theories. C. Kemp, N. D. Goodman, and J. B. Tenenbaum (to appear). Advances in Neural Information Processing Systems 20.
• A rational analysis of rule-based concept learning. N. D. Goodman, J. B. Tenenbaum, J. Feldman, and T. L. Griffiths (in press). Cognitive Science.
• Bayesian models of cognition. Griffiths, T. L., Kemp, C., and Tenenbaum, J. B. (to appear). In Ron Sun (ed.), Cambridge Handbook of Computational Cognitive Modeling. Cambridge University Press.

• Theory-based Bayesian models of inductive reasoning. Tenenbaum, J. B., Kemp, C., and Shafto, P. (2007). In Feeney, A. & Heit, E. (eds.), Inductive reasoning. Cambridge University Press.
  
• Causal inference in multisensory perception. Kording, K. P., Beierholm, U., Ma, W. J., Quartz, S., Tenenbaum, J. B., Shams, L. (2007). PLoS ONE. September 2007, Issue 9, e943.

• The role of causality in judgment under uncertainty. Krynski, T. R. and Tenenbaum, J. B. (2007). Journal of Experimental Psychology: General 136(3), 430-450.

• The dynamics of memory are a consequence of optimal adaptation to a changing body. Kording, K. P., Tenenbaum, J. B., and Shadmehr, R. (2007). Nature Neuroscience 10(6), 779-786.

• Goal inference as inverse planning. Baker, C. L., Tenenbaum, J. B., and Saxe, R. R. (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society.
• Modeling human performance in statistical word segmentation. Frank, M., Goldwater, S., Griffiths, T. L., Mansignhka, V. K., and Tenenbaum, J. B. (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society.
• A rational analysis of rule-based concept learning. Goodman, N. D., Griffiths, T. L., Feldman, J., and Tenenbaum, J. B. (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society.
• Learning grounded causal models. Goodman, N. D., Mansignhka, V. K., and Tenenbaum, J. B. (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. [2007 Cognitive Science Computational Modeling Prize, Perception and Action category.]
• Learning causal schemata. Kemp, C., Goodman, N. D., and Tenenbaum, J. B. (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. [2007 Cognitive Science Computational Modeling Prize, Higher-Level Cognition category.]
• Discovering syntactic hierarchies. Savova, V., Roy, D., Schmidt, L., and Tenenbaum, J. B. (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society.
• Parametric embedding for class visualization. Iwata, T., Saito, K., Ueda, N., Stromsten, S., Griffiths, T. L., and Tenenbaum, J. B. (2007). Neural Computation 19, 2536-2556.
  
• Learning annotated hierarchies from relational data. Roy, D., Kemp, C., Mansinghka, V., and Tenenbaum, J. B. (2007). Advances in Neural Information Processing Systems 19.

• Combining causal and similarity-based reasoning. Kemp, C., Shafto, P., Berke, A., and Tenenbaum, J. B. (2007). Advances in Neural Information Processing Systems 19. [Honorable mention, Outstanding Student Paper award.]

• Multiple timescales and uncertainty in motor adaptation. Kording, K., Tenenbaum, J. B., and Shadmehr, R. (2007). Advances in Neural Information Processing Systems 19.

• Causal inference in sensorimotor integration. Kording, K. and Tenenbaum, J. B. (2007). Advances in Neural Information Processing Systems 19.

• AClass: An online algorithm for generative classification. Mansinghka, V. K., Roy, D. M., Rifkin, R., and Tenenbaum, J. B. (2007). Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS07).

• Word learning as Bayesian inference. Xu, F. and Tenenbaum, J. B. (2007). Psychological Review 114(2).

• Topics in semantic representation. Griffiths, T. L., Steyvers, M., and Tenenbaum, J. B. (2007). Psychological Review 114(2).

• Bayesian networks, Bayesian learning, and cognitive development. Gopnik, A. and Tenenbaum, J. B. (2007). Developmental Science 10(3), 281-287.
  
• Sensitivity to sampling in Bayesian word learning. Xu, F. and Tenenbaum, J. B. (2007). Developmental Science 10(3), 288-297.
  
• Learning overhypotheses with hierarchical Bayesian models. Kemp, C., Perfors, A., and Tenenbaum, J. B. (2007). Developmental Science 10(3), 307-321.
  
• From mere coincidences to meaningful discoveries. Griffiths, T. L. and Tenenbaum, J. B. (2007). Cognition 103(2), 180-226.
  
• Intuitive theories as grammars for causal inference. Tenenbaum, J.B., Griffiths, T. L., and Niyogi, S. (2007). In Gopnik, A., & Schulz, L. (eds.), Causal learning: Psychology, philosophy, and computation. Oxford University Press.
  
• Two proposals for causal grammars. Griffiths, T. L. and Tenenbaum, J. B. (2007). In Gopnik, A., & Schulz, L. (eds.), Causal learning: Psychology, philosophy, and computation. Oxford University Press.
  

• Optimal predictions in everyday cognition. Griffiths, T. L. and Tenenbaum, J. B. (2006). Psychological Science 17(9), 767-773. Article in The Economist

• Statistics and the Bayesian mind. Griffiths, T. L. and Tenenbaum, J. B. (2006). Significance 3(3), 130-133.
  
• Theory-based Bayesian models of inductive learning and reasoning. Tenenbaum, J. B., Griffiths, T. L., and Kemp, C. (2006). Trends in Cognitive Sciences, 10(7), 309-318.
  
• Probabilistic models of cognition: Conceptual foundations. Chater, N., Tenenbaum, J. B., and Yuille, A. (2006). Trends in Cognitive Sciences, 10(7), 287-291.
  
• Unsupervised topic modelling for multi-party spoken discourse. Purver, M., Kording, K. P., Griffiths, T. L., & Tenenbaum, J. B. (2006). Proceedings of Coling/ACL 2006.
  
• Structured priors for structure learning. Mansinghka, V. K., Kemp, C., Tenenbaum, J. B., and Griffiths, T. L. (2006). Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI 2006).
• Learning systems of concepts with an infinite relational model. Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T., and Ueda, N. (2006). Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06). IRM Code
• Learning cross-cutting systems of categories. Shafto, P. Kemp, C., Mansignhka, V., Gordon, M., and Tenenbaum, J. B. (2006). Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society.
• Poverty of the Stimulus? A rational approach. Perfors, A., Regier, T. and Tenenbaum, J. B. (2006). Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society.
• Nonsense and sensibility: Inferring unseen possibilities. Schmidt, L. A., Kemp, C., and Tenenbaum, J. B. (2006). Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society.
• Learning overhypotheses. Kemp, C., Perfors, A. and Tenenbaum, J. B. (2006). Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society.
• Intuitive theories of mind: A rational approach to false belief. Goodman, N. D., Bonawitz, E. B., Baker, C. L., Mansinghka, V. K, Gopnik, A., Wellman, H., Schulz, L. and Tenenbaum, J. B. (2006). Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society.
• Bayesian models of human action understanding. C. L. Baker, J. B. Tenenbaum, R. R. Saxe (2006). Advances in Neural Information Processing Systems 18.

• Parametric Embedding for Class Visualization. T. Iwata, K. Saito, N. Ueda, S. Stromsten, T. L. Griffiths, J. B. Tenenbaum (2005). Advances in Neural Information Processing Systems 17.

• Context-sensitive induction. Shafto, P., Kemp, C., Baraff, L., Coley, J., and Tenenbaum, J. B. (2005). Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society.
  
• Word learning as Bayesian inference: Evidence from preschoolers. Xu, F. and Tenenbaum, J. B. (2005). Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society.
  
• Integrating topics and syntax. T. L. Griffiths, M. Steyvers, D. Blei, and J. B. Tenenbaum (2005). Advances in Neural Information Processing Systems 17.
  

• The large-scale structure of semantic networks: statistical analyses and a model of semantic growth. M. Steyvers, J. B. Tenenbaum (2005), Cognitive Science, 29(1).

• A generative theory of similarity. Kemp, C., Bernstein, A., and Tenenbaum, J. B. (2005). Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society.
  
• Secret agents: inferences about hidden causes by 10- and 12-month-old infants. Saxe, R., Tenenbaum, J.B., and Carey, S. (2005). Psychological Science 16(12), 995-1001.
  
• Structure and strength in causal induction. Griffiths, T.L., & Tenenbaum, J.B. (2005). Cognitive Psychology 51, 334-384.
  (This paper was formerly titled "Elemental causal induction.") MATLAB code for computing causal support.

• Learning domain structures. Kemp, C. S., Perfors, A., and Tenenbaum, J. B. (2004). Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society.
  
• Discovering latent classes in relational data. C. Kemp, T. L. Griffiths, & J. B. Tenenbaum (2004). MIT AI Memo 2004-019.
• Semi-supervised learning with trees. Kemp, C., Griffiths, T. L, Stromsten, S., and Tenenbaum, J. B. (2004). Advances in Neural Information Processing Systems 16.
• Hierarchical topic models and the nested Chinese restaurant process. D. Blei, T. L. Griffiths, M. I. Jordan, and J. B. Tenenbaum (2004). Advances in Neural Information Processing Systems 16. [Best Student Paper, NIPS 2003, Synthetic Systems Category.]
  

• From algorithmic to subjective randomness. Griffiths, T. L. and Tenenbaum, J. B. (2004). Advances in Neural Information Processing Systems 16. [Best Student Paper, NIPS 2003, Natural Systems Category.]

• Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers. D. Sobel, J. B. Tenenbaum, A. Gopnik (2004), Cognitive Science, 28, 303-333.
  

• Using physical theories to infer hidden causal structure. Griffiths, T.L., Baraff, E.R., & Tenenbaum, J.B. (2004). Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society. [Marr Prize for Best Student Paper, Honorable Mention, Cognitive Science 2004.]
  

• Learning style translation for the lines of a drawing. W.T. Freeman, J.B. Tenenbaum, E. Pasztor (2003). ACM Transactions on Graphics 22 (1), January 2003, 33-46. (uncorrected proofs - SMALL pdf)

• Theory-based induction. Kemp, C. S. and Tenenbaum, J. B. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.
  
• The role of causal models in reasoning under uncertainty. Krynski, T. R. and Tenenbaum, J. B. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.
  

• V1 neurons signal acquisition of an internal representation of stimulus location. Sharma, J., Dragoi, V., Tenenbaum, J. B., Miller, E. K., and Sur, M. (2003). Science, 300, 1758-1763.
  

• Probability, algorithmic complexity, and subjective randomness. Griffiths, T. L. and Tenenbaum, J. B. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.

• Learning causal laws. Tenenbaum, J. B. and Niyogi, S. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.
  
• Dynamical causal learning. Danks, D., Griffiths, T.L., & Tenenbaum, J.B. (2003). Advances in Neural Information Processing Systems 15. Becker, S., Thrun, S., and Obermayer. (eds). Cambridge, MIT Press, 2003, 67-74.
  

• Inferring causal networks from observations and interventions. M. Steyvers, J. B. Tenenbaum, E. J. Wagenmakers, B. Blum (2003), Cognitive Science 27: 453-489.

• Theory-based causal inference. J. B. Tenenbaum, T. L. Griffiths (2003), Advances in Neural Information Processing Systems 15. Becker, S., Thrun, S., and Obermayer. (eds). Cambridge, MIT Press, 2003, 35-42.

• The Isomap Algorithm and Topological Stability. M. Balasubramanian, E. L. Shwartz, J. B. Tenenbaum, V. de Silva, and J. C. Langford (2002). Science Jan 4 2002: 7.

• Global versus local methods in nonlinear dimensionality reduction. V. de Silva, J. B. Tenenbaum (2002). Advances in Neural Information Processing Systems 15. S. Becker, S., Thrun, S., and Obermayer, K. (eds). Cambridge, MIT Press, 2002, 705-712.

• Unsupervised learning of curved manifolds. V. de Silva, J.B. Tenenbaum (2002). In D.D. Denison, M. H. Hansen, C. C. Holmes, B. Mallick and B. Yu (eds.), Nonlinear Estimation and Classification , Springer-Verlag, New York, 453-466.

• Bayesian models of inductive generalization. N. Sanjana, J. B. Tenenbaum (2002), Advances in Neural Information Processing Systems 15. Becker, S., Thrun, S., Obermayer, K. (eds). Cambridge, MIT Press, 2002, 51-58. [Best Student Paper, Honorable Mention, NIPS 2001.]

• Generalization, similarity, and Bayesian inference. J. B. Tenenbaum, T. L. Griffiths (2001), Behavioral and Brain Sciences, 24 pp. 629-641.

• Some specifics about generalization. J. B. Tenenbaum, T. L. Griffiths (2001), Behavioral and Brain Sciences, 24, pages 772-778.

• The rational basis of representativeness. J. B. Tenenbaum, T. L. Griffiths (2001), 23rd Annual Conference of the Cognitive Science Society. 1036-1041.
  

• Randomness and coincidences: Reconciling intuition and probability theory. T. L. Griffiths, J. B. Tenenbaum (2001), 23rd Annual Conference of the Cognitive Science Society. 370-375. Article in Psychology Today

• Structure learning in human causal induction. J. B. Tenenbaum, T. L. Griffiths (2001), Advances in Neural Information Processing Systems 13. Leen, T., Dietterich, T., and Tresp, V., Cambridge, MIT Press, 2001, 59-65. (postscript)

• A global geometric framework for nonlinear dimensionality reduction. J. B. Tenenbaum, V. De Silva and J. C. Langford (2000). Science 290 (5500), 2319-2323. Website

• Separating style and content with bilinear models. J. B. Tenenbaum, W. T. Freeman (2000). Neural Computation 12 (6), 1247-1283. [Conference version received the Outstanding Paper Award at IEEE CVPR 1997.]

• Rules and similarity in concept learning. J. B. Tenenbaum (2000), Advances in Neural Information Processing Systems 12. Solla, S., Leen, T. and Muller, K. (eds). Cambridge, MIT Press, 2000, 59-65. (postscript)
• Word learning as Bayesian inference. J. B. Tenenbaum, F. Xu (2000), Proceedings of the 22nd Annual Conference of the Cognitive Science Society (postscript)

• Teacakes, trains, toxins, and taxicabs: A Bayesian account of predicting the future. T. L. Griffiths, J. B. Tenenbaum (2000), Proceedings of the 22nd Annual Conference of the Cognitive Science Society. 202-207. (postscript)

• Bayesian modeling of human concept learning. J. B. Tenenbaum (1999), Advances in Neural Information Processing Systems 11. Kearns, M., Solla, S., and Cohn, D. (eds). Cambridge, MIT Press, 1999, 59-65. (postscript)
• A Bayesian Framework for Concept Learning. J. B. Tenenbaum, Ph.D. Thesis, MIT, 1999

• Mapping a manifold of perceptual observations. J. B. Tenenbaum (1998). Advances in Neural Information Processing Systems 10. Jordan, M., Kearns, M., and Solla, S. (eds). Cambridge, MIT Press, 1998, 682-688. (postscript)

• Learning the structure of similarity. J. B. Tenenbaum (1995), Advances in Neural Information Processing Systems 8. Toretzky, D., Mozer, M., and Hasselmo, M. (eds). Cambridge, MIT Press, 1995, 3-9. (postscript)

Online papers (by topic - this list is somewhat out of date)

Nonlinear dimensionality reduction

• Parametric Embedding for Class Visualization. T. Iwata, K. Saito, N. Ueda, S. Stromsten, T. L. Griffiths, J. B. Tenenbaum (2005). Advances in Neural Information Processing Systems 17.

• The Isomap Algorithm and Topological Stability. M. Balasubramanian, E. L. Shwartz, J. B. Tenenbaum, V. de Silva, and J. C. Langford (2002). Science Jan 4 2002: 7.

• Global versus local methods in nonlinear dimensionality reduction. V. de Silva, J. B. Tenenbaum (2002). Advances in Neural Information Processing Systems 15. S. Becker, S., Thrun, S., and Obermayer, K. (eds). Cambridge, MIT Press, 2002, 705-712.

• Unsupervised learning of curved manifolds. V. de Silva, J.B. Tenenbaum (2002). In D.D. Denison, M. H. Hansen, C. C. Holmes, B. Mallick and B. Yu (eds.), Nonlinear Estimation and Classification , Springer-Verlag, New York, 453-466.

• A global geometric framework for nonlinear dimensionality reduction. J. B. Tenenbaum, V. De Silva and J. C. Langford (2000). Science 290 (5500), 2319-2323. Website

• Mapping a manifold of perceptual observations. J. B. Tenenbaum (1998). Advances in Neural Information Processing Systems 10. Jordan, M., Kearns, M., and Solla, S. (eds). Cambridge, MIT Press, 1998, 682-688. (postscript)
  
  

Separating style and content

• Learning style translation for the lines of a drawing. W.T. Freeman, J.B. Tenenbaum, E. Pasztor (2003). ACM Transactions on Graphics 22 (1), January 2003, 33-46. (uncorrected proofs - SMALL pdf)

• Separating style and content with bilinear models. J. B. Tenenbaum, W. T. Freeman (2000). Neural Computation 12 (6), 1247-1283. [Conference version received the Outstanding Paper Award at IEEE CVPR 1997.]
  
  

Concept learning and generalization

• Context-sensitive induction. Shafto, P., Kemp, C., Baraff, L., Coley, J., and Tenenbaum, J. B. (2005). Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society.
  
• Learning domain structures. Kemp, C. S., Perfors, A., and Tenenbaum, J. B. (2004). Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society.
  
• Discovering latent classes in relational data. C. Kemp, T. L. Griffiths, & J. B. Tenenbaum (2004). MIT AI Memo 2004-019.
• Semi-supervised learning with trees. Kemp, C., Griffiths, T. L, Stromsten, S., and Tenenbaum, J. B. (2004). Advances in Neural Information Processing Systems 16.
• Theory-based induction. Kemp, C. S. and Tenenbaum, J. B. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.
  

• Bayesian models of inductive generalization. N. Sanjana, J. B. Tenenbaum (2002), Advances in Neural Information Processing Systems 15. Becker, S., Thrun, S., Obermayer, K. (eds). Cambridge, MIT Press, 2002, 51-58. [Best Student Paper, Honorable Mention, NIPS 2001.]

• Rules and similarity in concept learning. J. B. Tenenbaum (2000), Advances in Neural Information Processing Systems 12. Solla, S., Leen, T. and Muller, K. (eds). Cambridge, MIT Press, 2000, 59-65. (postscript)
• Bayesian modeling of human concept learning. J. B. Tenenbaum (1999), Advances in Neural Information Processing Systems 11. Kearns, M., Solla, S., and Cohn, D. (eds). Cambridge, MIT Press, 1999, 59-65. (postscript)
• A Bayesian Framework for Concept Learning. J. B. Tenenbaum, Ph.D. Thesis, MIT, 1999
  
  

Learning and Representing Word Meanings

• Word learning as Bayesian inference: Evidence from preschoolers. Xu, F. and Tenenbaum, J. B. (2005). Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society.
  
• Integrating topics and syntax. T. L. Griffiths, M. Steyvers, D. Blei, and J. B. Tenenbaum (2005). Advances in Neural Information Processing Systems 17.
  

• The large-scale structure of semantic networks: statistical analyses and a model of semantic growth. M. Steyvers, J. B. Tenenbaum (2005), Cognitive Science, 29(1).

• Hierarchical topic models and the nested Chinese restaurant process. D. Blei, T. L. Griffiths, M. I. Jordan, and J. B. Tenenbaum (2004). Advances in Neural Information Processing Systems 16. [Best Student Paper, NIPS 2003, Synthetic Systems Category.]
  

• Word learning as Bayesian inference. J. B. Tenenbaum, F. Xu (2000), Proceedings of the 22nd Annual Conference of the Cognitive Science Society (postscript)
  
  

Learning and similarity

• A generative theory of similarity. Kemp, C., Bernstein, A., and Tenenbaum, J. B. (2005). Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society.
  
• Generalization, similarity, and Bayesian inference. J. B. Tenenbaum, T. L. Griffiths (2001), Behavioral and Brain Sciences, 24 pp. 629-641.

• Some specifics about generalization. J. B. Tenenbaum, T. L. Griffiths (2001), Behavioral and Brain Sciences, 24, pages 772-778.

• Learning the structure of similarity. J. B. Tenenbaum (1995), Advances in Neural Information Processing Systems 8. Toretzky, D., Mozer, M., and Hasselmo, M. (eds). Cambridge, MIT Press, 1995, 3-9. (postscript)
  
  

Probabilistic reasoning

• From mere coincidences to meaningful discoveries. Griffiths, T. L. and Tenenbaum, J. B. (in press). Cognition.
  
• Optimal predictions in everyday cognition. Griffiths, T. L. and Tenenbaum, J. B. (in press). Psychological Science. Article in The Economist

• From algorithmic to subjective randomness. Griffiths, T. L. and Tenenbaum, J. B. (2004). Advances in Neural Information Processing Systems 16. [Best Student Paper, NIPS 2003, Natural Systems Category.]

• The role of causal models in reasoning under uncertainty. Krynski, T. R. and Tenenbaum, J. B. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.
  

• V1 neurons signal acquisition of an internal representation of stimulus location. Sharma, J., Dragoi, V., Tenenbaum, J. B., Miller, E. K., and Sur, M. (2003). Science, 300, 1758-1763.
  

• Probability, algorithmic complexity, and subjective randomness. Griffiths, T. L. and Tenenbaum, J. B. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.

• The rational basis of representativeness. J. B. Tenenbaum, T. L. Griffiths (2001), 23rd Annual Conference of the Cognitive Science Society. 1036-1041.
  

• Randomness and coincidences: Reconciling intuition and probability theory. T. L. Griffiths, J. B. Tenenbaum (2001), 23rd Annual Conference of the Cognitive Science Society. 370-375. Article in Psychology Today

• Teacakes, trains, toxins, and taxicabs: A Bayesian account of predicting the future. T. L. Griffiths, J. B. Tenenbaum (2000), Proceedings of the 22nd Annual Conference of the Cognitive Science Society. 202-207. (postscript)

• Intuitive theories as grammars for causal inference. Tenenbaum, J.B., Griffiths, T. L., and Niyogi, S. (in press). To appear in Gopnik, A., & Schulz, L. (Eds.), Causal learning: Psychology, philosophy, and computation. Oxford University Press.
  
• Two proposals for causal grammars. Griffiths, T. L. and Tenenbaum, J. B. (in press). To appear in Gopnik, A., & Schulz, L. (Eds.), Causal learning: Psychology, philosophy, and computation. Oxford University Press.
  
• Secret agents: inferences about hidden causes by 10- and 12-month-old infants. Saxe, R., Tenenbaum, J.B., and Carey, S. (2005). Psychological Science 16(12), 995-1001.
  
• Structure and strength in causal induction. Griffiths, T.L., & Tenenbaum, J.B. (2005). Cognitive Psychology 51(4), 285-386.
  (MATLAB code for computing causal support)
• Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers. D. Sobel, J. B. Tenenbaum, A. Gopnik (2004), Cognitive Science, 28, 303-333.
  

• Using physical theories to infer hidden causal structure. Griffiths, T.L., Baraff, E.R., & Tenenbaum, J.B. (2004). Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society. [Marr Prize for Best Student Paper, Honorable Mention, Cognitive Science 2004.]
  
• Learning causal laws. Tenenbaum, J. B. and Niyogi, S. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.
  
• Dynamical causal learning. Danks, D., Griffiths, T.L., & Tenenbaum, J.B. (2003). Advances in Neural Information Processing Systems 15. Becker, S., Thrun, S., and Obermayer. (eds). Cambridge, MIT Press, 2003, 67-74.
  

• Inferring causal networks from observations and interventions. M. Steyvers, J. B. Tenenbaum, E. J. Wagenmakers, B. Blum (2003), Cognitive Science 27: 453-489.

• Theory-based causal inference. J. B. Tenenbaum, T. L. Griffiths (2003), Advances in Neural Information Processing Systems 15. Becker, S., Thrun, S., and Obermayer. (eds). Cambridge, MIT Press, 2003, 35-42.

• Structure learning in human causal induction. J. B. Tenenbaum, T. L. Griffiths (2001), Advances in Neural Information Processing Systems 13. Leen, T., Dietterich, T., and Tresp, V., Cambridge, MIT Press, 2001, 59-65. (postscript)

• Bayesian models of human action understanding. C. L. Baker, J. B. Tenenbaum, R. R. Saxe (in press). Advances in Neural Information Processing Systems 18.