Noah D. Goodman

Research

• Computational models of cognition, integrating logic and probability.
• Probabilistic programming languages.
• Concepts, categorization, and intuitive theories.
• Natural language semantics and pragmatics.
• Social cognition: reasoning about others' goals, beliefs, and actions.
• Causal learning and reasoning.
• Cognitive development, especially the acquisition of abstract knowledge.

Fall 2012

Spring 2012

Winter 2012

IPAM GSS 2011

Spring 2011

Winter 2011

Church

Manuscripts (available by request)

• Theory acquisition as stochastic search. T. D. Ullman, N. D. Goodman, and J. B. Tenenbaum (under review).
• Rational reasoning in pedagogical contexts. P. Shafto, N. D. Goodman, and T. L. Griffiths (under revision).
• One and done? Optimal decisions from very few samples. E. Vul, N. D. Goodman, T. L. Griffiths, J. B. Tenenbaum (under revision).
• Reasoning about Reasoning by Nested Conditioning: Modeling Theory of Mind with Probabilistic Programs. A. Stuhlmueller and N. D. Goodman (under review).
• Ad-hoc scalar implicature in preschool children. A. J. Stiller, N. D. Goodman, M. C. Frank (in prep.).

In Press

• Burn-in, bias, and the rationality of anchoring. F. Lieder, T. L. Griffiths, and N. D. Goodman (in press). NIPS 2012.
• Knowledge and implicature: Modeling language understanding as social cognition. N. D. Goodman, and A. Stuhlmueller (in press). Topics in Cognitive Science.

2012

• A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs. A. Stuhlmueller and N. D. Goodman (2012). Second Statistical Relational AI workshop at UAI 2012 (StaRAI-12).
• The mentalistic basis of core social cognition: experiments in preverbal infants and a computational model. J. K. Hamlin, T. Ullman, J. B. Tenenbaum, N. D. Goodman, C. L. Baker (2012). Developmental Science.
• Learning design patterns with bayesian grammar induction. J. Talton, L. Yang, R. Kumar, M. Lim, N. D. Goodman, R. Mech (2012). UIST 12.
• Quantifying pragmatic inference in language games. M. C. Frank and N. D. Goodman (2012). Science.
  (Journal formatted version. Comentary by Levinson.)
• Learning from human action: The consequences of social context for learners. P. Shafto, N. D. Goodman, and M. C. Frank (2012). Pespectives in Psychological Science
• Synthesizing open worlds with constraints using locally-annealed reversible-jump MCMC. Y. Yeh, L. Yang, M. Watson, N. D. Goodman, P. Hanrahan (2012). SIGGRAPH 2012.
• Knowledge and implicature: Modeling language understanding as social cognition. N. D. Goodman, and A. Stuhlmueller (2012). Proceedings of the Thirty-Fourth Annual Conference of the Cognitive Science Society. [2012 Cognitive Science Society computational modeling prize for Language.]
• That's what she (could have) said: How alternative utterances affect language use. L. Bergen, N. D. Goodman, and R. Levy (2012). Proceedings of the Thirty-Fourth Annual Conference of the Cognitive Science Society.
• Ping Pong in Church: Productive use of concepts in human probabilistic inference. T. Gerstenberg, and N. D. Goodman (2012). Proceedings of the Thirty-Fourth Annual Conference of the Cognitive Science Society.
• Noisy Newtons: Unifying process and dependency accounts of causal attribution. T. Gerstenberg, N. D. Goodman, D. A. Lagnado, and J. B. Tenenbaum (2012). Proceedings of the Thirty-Fourth Annual Conference of the Cognitive Science Society.
• How many kinds of reasoning? Inference, probability, and natural language semantics D. Lassiter, and N. D. Goodman (2012). Proceedings of the Thirty-Fourth Annual Conference of the Cognitive Science Society.
• Did she jump because she was the big sister or because the trampoline was safe? Causal inference and the development of social attribution. E. Seiver, N. D. Goodman, A. Gopnik (2012). Child Development.
• Bootstrapping in a language of thought: a formal model of numerical concept learning. S. T. Piantadosi, J. B. Tenenbaum, and N. D. Goodman (2012). Cognition.
• Comparing pluralities. G. Scontras, P. Graff, and N. D. Goodman (2012). Cognition.

2011

• Nonstandard interpretations of probabilistic programs for efficient inference. D. Wingate, A. Stuhlmueller, J. Sisskind, N. D. Goodman (2011). Advances in Neural Information Processing Systems 23.
• Inducing probabilistic programs by bayesian program merging. I. Hwang, A. Stuhlmueller, and N. D. Goodman. (2011). Technical report: arXiv:1110.5667
• The imaginary fundamentalists: the unshocking truth about Bayesian cognitive science. N. Chater, N. D. Goodman, T. L. Griffiths, C. Kemp, M. Oaksford, and J. B. Tenenbaum (2011). Brain and Behavioral Sciences. (Commentary on Jones and Love.)
• Ad-hoc scalar implicature in adults and children. A. Stiller, N. D. Goodman, and M. C. Frank (2011). Proceedings of the Thirty-Third Annual Conference of the Cognitive Science Society.
• Productivity and reuse in language. T. J. O'Donnell, J. Snedeker, J. B. Tenenbaum, and N. D. Goodman (2011). Proceedings of the Thirty-Third Annual Conference of the Cognitive Science Society. [2011 Cognitive Science Society computational modeling prize for Language.]
• Bayesian Policy Search with Policy Priors. D. Wingate, N. D. Goodman, D. Roy, L. Kaelbling, and J. B. Tenenbaum (2011). Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI 11). [Best poster award.]
• Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation. D. Wingate, A. Stuhlmueller, N. D. Goodman (2011). Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AI-STATS 11).
• Where science starts: Spontaneous experiments in preschoolers' exploratory play. C. Cook, N. D. Goodman, and L. Schulz (2011). Cognition.
• The double-edged sword of pedagogy: Instruction limits spontaneous exploration and discovery. P. Shafto, E. Bonawitz, H. Gweon, N. D. Goodman, E. Spelke, and L. Schulz (2011). Cognition.
• How to grow a mind: structure, statistics, and abstraction. J. B. Tenenbaum, C. Kemp, T. L. Griffiths, and N. D. Goodman (2011). Science.
• Learning a theory of causality. N. D. Goodman, T. D. Ullman, and J. B. Tenenbaum (2011). Psych. Review. (Matlab code for the model.)

2010

• Learning to learn causal models. C. Kemp, N. D. Goodman, and J. B. Tenenbaum (2010). Cognitive Science.
• Optimal habits can develop spontaneously through sensitivity to local cost. T. M. Desrochers, D. Z. Jin , N. D. Goodman, and A. M. Graybiel (2010). Proceedings of the National Academy of Sciences. (See also commentary by T. J. Sejnowski.)
• Beyond Boolean logic: exploring representation languages for learning complex concepts. S. T. Piantadosi, J. B. Tenenbaum, and N. D. Goodman (2010). Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society.
• Learning Structured Generative Concepts. A. Stuhlmueller, J. B. Tenenbaum, and N. D. Goodman (2010). Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society.
• Prior expectations in pedagogical situations. P. Shafto, N. D. Goodman, B. Gerstle, and F. Ladusaw (2010). Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society.
• 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.
• The structure and dynamics of scientific theories: a hierarchical Bayesian perspective. L. Henderson, N. D. Goodman, J. B. Tenenbaum, and J. Woodward (2010). Philosophy of Science.
• Help or hinder: Bayesian models of social goal inference. T. Ullman, C. L. Baker, O. Macindoe, O. Evans, N. D. Goodman, and J. B. Tenenbaum (2010). Advances in Neural Information Processing Systems 22.

2009

• The infinite latent events model. D. Wingate, N. D. Goodman, D. M. Roy, and J. B. Tenenbaum (2009). Uncertainty in Artificial Intelligence 2009.
• 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.
• Cause and intent: Social reasoning in causal learning. N. D. Goodman, C. L. Baker, 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.
• Using speakers' referential intentions to model early cross-situational word learning. M. C. Frank, N. D. Goodman, and J. B. Tenenbaum (2009). Psychological Science.

2008

• Going beyond the evidence: Abstract laws and preschoolers' responses to anomalous data. L. E. Schulz, N. D. Goodman, J. B. Tenenbaum, and A. Jenkins (2008). Cognition.
• Church: a language for generative models. N. D. Goodman, V. K. Mansighka, D. Roy, K. Bonawitz, J. B. Tenenbaum (2008). Uncertainty in Artificial Intelligence 2008.
• Random-World Semantics and Syntactic Independence for Expressive Languages. D. McAllester, B. Milch, N. D. Goodman (2008). Technical Report MIT-CSAIL-TR-2008-025, Massachusetts Institute of Technology.
• Teaching games: statistical sampling assumptions for learning in pedagogical situations. P. Shafto, and N. D. Goodman (2008). Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society.
• 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 induction. C. L. Baker, N. D. Goodman, and J. B. Tenenbaum (2008). Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society.
• 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.
• 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. N. D. Goodman, J. B. Tenenbaum, T. L. Griffiths, and J. Feldman (2008). In M. Oaksford and N. Chater (Eds.). The probabilistic mind: Prospects for Bayesian cognitive science.

2007

• Frameworks in science: a Bayesian approach. L. Henderson, N. D. Goodman, J. B. Tenenbaum, and J. Woodward. (This version was presented at the conference "Confirmation, Induction and Science", London School of Economics, March 2007.)
• A rational analysis of rule-based concept learning. N. D. Goodman, T. L. Griffiths, J. Feldman, and J. B. Tenenbaum (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society.
• Learning grounded causal models. N. D. Goodman, V. K. Mansinghka, and J. B. Tenenbaum (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. (The experiment demo is here.) [2007 Cognitive Science Society computational modeling prize for Perception and Action.]
• Learning causal schemata. C. Kemp, N. D. Goodman, and J. B. Tenenbaum (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. [2007 Cognitive Science Society computational modeling prize for Higher-level Cognition.]

2006

• 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.