Source: http://web.mit.edu/cocosci/josh.html
Timestamp: 2019-04-25 22:23:52+00:00

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If you are an undergraduate at MIT interested in research with our group, please send a short email about your interests and background (as specific as possible), with resume/CV and any previous research papers, to cocosci-urop@mit.edu. Thanks!
My colleagues and I in the Computational Cognitive Science group want to understand that most elusive aspect of human intelligence: our ability to learn so much about the world, so rapidly and flexibly. I like to ask, "How do we humans get so much from so little?" and by that I mean how do we acquire our commonsense understanding of the world given what is clearly by today's engineering standards so little data, so little time, and so little energy.
Consider how given just a few relevant experiences, even young children can infer the meaning of a new word, the hidden properties of an object or substance, or the existence of a new causal relation or social rule. These inferences go far beyond the data given: after seeing three or four examples of "horses", a two-year-old will confidently judge whether any new entity is a horse or not, and she will be mostly correct, except for the occasional donkey or camel.
How to Grow a Mind: Statistics, Structure, and Abstraction. Tenenbaum, J. B., Kemp, C., Griffiths, T. L., and Goodman, N. D. (2011). Science 331 (6022), 1279-1285. Supporting Online Material.
Video of NIPS 2010 Posner Lecture.
Video of MIT BCS 2010 Seminar.
The discovery of structural form. Kemp, C. and Tenenbaum, J. B. (2008). Proceedings of the National Academy of Sciences. 105(31), 10687-10692. Supporting information. Commentary by K. J. Holyoak. Code and data sets.
Bayesian models of cognition. Griffiths, T. L., Kemp, C., and Tenenbaum, J. B. (2008). In Ron Sun (ed.), Cambridge Handbook of Computational Cognitive Modeling. Cambridge University Press.
Pure reasoning in 12-month-old infants as probabilistic inference. Teglas, E., Vul, E., Girotto, V., Gonzalez, M., Tenenbaum, J. B., and Bonatti, L. L. (2011). Science 332, 1054-1059. Supporting Online Material.
Special issue of Trends in Cognitive Science, July 2006 (Vol. 10, Issue 7), on "Probabilistic Models of Cognition".
Optimal predictions in everyday cognition. Griffiths, T. L. and Tenenbaum, J. B. (2006). Psychological Science 17(9), 767-773. Article in The Economist.
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.
A tutorial introuction to Bayesian models of cognitive development. Perfors, A., Tenenbaum, J. B., Griffiths, T. L.,and Xu, F. (in press) Cognition.
The learnability of abstract syntactic principles. Perfors, A, Tenenbaum, J. B. and Regier, T. (in press). Cognition.
Learning to learn causal models. Kemp, C., Goodman, N. & Tenenbaum, J. (in press). Cognitive Science.
Learning a theory of causality. N. D. Goodman, T. D. Ullman, and J. B. Tenenbaum (in press). Psychological Review.
Three ideal observer models for rule learning in simple languages. Frank, M. C. & Tenenbaum, J. B. (in press). Cognition. Code package.
Probabilistic models of cognition: Exploring representations and inductive biases. Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (in press). Trends in Cognitive Sciences.
Theory acquisition as stochastic search. T. D. Ullman, N. D. Goodman and J. B. Tenenbaum (2010). Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society.
A probabilistic model of theory formation. Kemp, C., Tenenbaum, J. B., Niyogi, S. & Griffiths, T. L. (2010). Cognition. 114(2), 165-196. Code and data sets.
Variability, negative evidence, and the acquisition of verb argument constructions. . Perfors, A. F, Wonnacott, E., & Tenenbaum, J.B. (in press). Journal of Child Langauge.
Help or hinder: Bayesian models of social goal inference. Ullman, T.D., Baker, C.L., Macindoe, O., Evans, O., Goodman, N.D., & Tenenbaum, J.B. (2010). Advances in Neural Information Processing Systems (Vol. 22, pp. 1874-1882).
The structure and dynamics of scientific theories: a hierarchical Bayesian perspective. L. Henderson, N. D. Goodman, J. B. Tenenbaum and J. F. Woodward. Phil. Sci. 77 (2), 172-200 (2010).
Action understanding as inverse planning. Baker, C. L., Saxe, R., & Tenenbaum, J. B. (2009). Cognition, 113, 329-349. Supplementary material.
Theory-based causal induction. Griffiths, T. L., & Tenenbaum, J. B. (2009). Psychological Review, 116, 661-716.
Structured statistical models of inductive reasoning. Kemp, C. and Tenenbaum, J. B. (2009). Psychological Review, 116(1), 20-58.
Using speakers' referential intentions to model early cross-situational word learning. Frank, M. C., Goodman, N. D., and Tenenbaum, J. B. (2009). Psychological Science 20, 578-585.
Exact and approximate sampling by systematic stochastic search. Mansinghka, V. K., Roy, D. M., Jonas, E., and Tenenbaum, J. B. (2009). AISTATS 2009.
Cause and Intent: Social Reasoning in Causal Learning. Goodman, N.D., Baker, C.L., & Tenenbaum, J.B. (2009). In Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society (pp. 2759-2764).
The infinite latent events model. D. Wingate, N. D. Goodman, D. M. Roy, and J. B. Tenenbaum (2009). Uncertainty in Artificial Intelligence 2009.
Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model. . E. Vul, M. C. Frank, J. B. Tenenbaum, and G. Alvarez (2009). Advances in Neural Information Processing Systems 21.
Fragment grammars: Exploring computation and reuse in language T O'Donnell, N. D. Goodman, J. B. Tenenbaum (2009). Technical Report MIT-CSAIL-TR-2009-013, Massachusetts Institute of Technology.
Learning a theory of causality. N. D. Goodman, T. Ullman, and J. B. Tenenbaum (2009). Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society.
How tall Is tall? Compositionality, statistics, and gradable adjectives. L. Schmidt, N. D. Goodman, D. Barner, and J. B. Tenenbaum (2009). Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society.
One and done: Globally optimal behavior from locally suboptimal decisions. E. Vul, N. D. Goodman, T. L. Griffiths, J. B. Tenenbaum (2009). Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society.
Informative communication in word production and word learning. M. C. Frank, N. D. Goodman, P. Lai, and J. B. Tenenbaum (2009). Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society.
Continuity of discourse provides information for word learning. M. C. Frank, N. D. Goodman, J. B. Tenenbaum, and A. Fernald (2009). Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society.
Inductive reasoning about causally transmitted properties. Shafto, P., Kemp, C., Baraff, E. R., Coley, J., and Tenenbaum, J. B. (2008). Cognition, 109, 175-192.
A rational analysis of rule-based concept learning. N. D. Goodman, J. B. Tenenbaum, J. Feldman, and T. L. Griffiths (2008). Cognitive Science, 32:1, 108-154.
Compositionality in rational analysis: Grammar-based induction for concept learning. In M. Oaksford and N. Chater (Eds.). Goodman, N. D., Tenenbaum, J. B., Griffiths, T. L., & Feldman, J. (2008). The probabilistic mind: Prospects for rational models of cognition. Oxford: Oxford University Press.
Church: a language for generative models. Goodman, N. D., Mansinghka, V. K., Roy, D., Bonawitz, K., and Tenenbaum, J. B. (2008). Uncertainty in Artificial Intelligence 2008.
A Bayesian Model of the Acquisition of Compositional Semantics. S. T. Piantadosi, N. D. Goodman, B. A. Ellis, and J. B. Tenenbaum (2008). Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society.
Theory acquisition and the language of thought. C. Kemp, N. D. Goodman, and J. B. Tenenbaum (2008). Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society.
Structured correlation from the causal background. R. Mayrhofer, N. D. Goodman, M. Waldmann, and J. B. Tenenbaum (2008). Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society.
Modeling semantic cognition as logical dimensionality reduction. Y. Katz, N. D. Goodman, K. Kersting, C. Kemp, and J. B. Tenenbaum (2008). Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society.
Theory-based social goal inference. Baker, C.L., Goodman, N.D., & Tenenbaum, J.B. (2008). In Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society (pp. 1447-1452).
A Bayesian framework for cross-situational word-learning. M. C. Frank, N. D. Goodman, and J. B. Tenenbaum (2008). Advances in Neural Information Processing Systems 20.
Learning and using relational theories. C. Kemp, N. D. Goodman, and J. B. Tenenbaum (2008). Advances in Neural Information Processing Systems 20.
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 (pp. 779-784).
Modeling human performance in statistical word segmentation. Frank, M., Goldwater, S., Griffiths, T. L., Mansinghka, 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.
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.
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.
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 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., Baker, C. L., Bonawitz, E. B., Mansinghka, V. K, Gopnik, A., Wellman, H., Schulz, L. & Tenenbaum, J. B. (2006). Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society (pp. 1382-1387).
Bayesian models of human action understanding. C. L. Baker, J. B. Tenenbaum, R. R. Saxe (2006). Advances in Neural Information Processing Systems 18 (pp. 99-106).
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.
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.
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.
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.
A Bayesian Framework for Concept Learning. J. B. Tenenbaum, Ph.D. Thesis, MIT, 1999.
From mere coincidences to meaningful discoveries. Griffiths, T. L. and Tenenbaum, J. B. (in press). Cognition.
Structure and strength in causal induction. Griffiths, T.L., & Tenenbaum, J.B. (2005). Cognitive Psychology 51(4), 285-386.
Bayesian models of human action understanding. Baker, C. L, Tenenbaum, J. B. & Saxe, R. R. (2006). Advances in Neural Information Processing Systems 18 (pp. 99-106).

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