We will be reading the following articles, or using them as references:
Baker, C. L., Goodman, N. D., & Tenenbaum, J. B. 2008. Theory-based social goal inference. In Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society, 1447-1452.
Bergen, L., Goodman, N., & Levy, R. 2012. That's what she (could have) said: How alternative utterances affect language use. Proceedings of the 34th Annual Cognitive Science Society.
Bergen, L., R. Levy., & N. D. Goodman. Pragmatic Reasoning through Semantic Inference. Downloaded from here 12/18/14.
Bever, T. & Poeppel, D. 2010. Analysis by Synthesis: A (Re-)Emerging Program of Research for Language and Vision. Biolinguistics, 4:2-3, 174-200.
Bonawitz, E., Denison, S., Griffiths, T. L., & Gopnik, A. 2014. Probabilistic models, learning algorithms, and response variability: Sampling in cognitive development. Trends in Cognitive Sciences, 18, 597-500.
Bowers, J. S., & Davis, C. J. 2012. Bayesian just-so stories in psychology and neuroscience. Psychological Bulletin, 138(3), 389-414.
Bowers, J. & Davis, C. 2012. Is That What Bayesians Believe? Reply to Griffiths, Chater, Norris, and Pouget. Psychological Bulletin, 138(3), 423-426.
Fossum, V., & Levy, R. 2012. Sequential vs. hierarchical syntactic models of human incremental sentence processing. In Proceedings of the 3rd Workshop on Cognitive Modeling and Computational Linguistics, 61-69. Association for Computational Linguistics.
Frank, M. & Goodman, N. 2012. Predicting Pragmatic Reasoning in Language Games. Science, 336, 998.
Gigerenzer, G. 2008. Why heuristics work. Perspectives on Psychological Science, 3(1), 20-29.
Gigerenzer, G., & Brighton, H. 2009. Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1(1), 107-143.
Goldstein, D.G., & Gigerenzer, G. 2002. Models of ecological rationality: the Recognition Heuristic. Psychological Review, 109(1), 75-90
Goodman, N. & D. Lassiter. In press. Probabilistic Semantics and Pragmatics: Uncertainty in Language and Thought. In S. Lappin & C. Fox (Eds.) Handbook of Contemporary Semantics.
Goodman, N. & Stuhlmueller, A. 2013. Knowledge and Implicature: Modeling Language Understanding as Social Cognition. Topics in Cognitive Science, 5, 173-184.
Goodman, N., Ullman, T., & Tenenbaum, J. 2011. Learning a Theory of Causality. Psychological Review, 118(1), 110-119
Gopnik, A. and Bonawitz, E. 2014. Bayesian models of child development. WIREs Cogn Sci. doi:10.1002/wcs.1330
Gopnik, A. and Wellman, H. 2014 in press. Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory. Psych Bull & Rev.
Griffiths, T. L., Austerweil, J. L., & Berthiaume, V. G. 2012. Comparing the inductive biases of simple neural networks and Bayesian models. In Proc. the 34th Annual Conf. of the Cog. Sci. Society.
Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. 2010. Probabilistic models of cognition: exploring representations and inductive biases. Trends in Cognitive Sciences, 14(8), 357-364.
Griffiths, T., Chater, N., Norris, D., & Pouget, A. 2012. How the Bayesians Got Their Beliefs (and What Those Beliefs Actually Are): Comment on Bowers and Davis. Psychological Bulletin, 138(3), 415-422.
Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. 2008. Bayesian models of cognition.
Griffiths, T. L., Lieder, F., & Goodman, N. D. In press. Rational use of cognitive resources: Levels of analysis between the computational and the algorithmic. Topics in Cognitive Science.
Griffiths, T.L., & Tenenbaum, J.B. 2006. Optimal predictions in everyday cognition. Psychological Science, 17(9), 767-773
Hemmer, P. & Steyvers, M. 2009. A Bayesian Account of Reconstructive Memory. Topics in Cognitive Science, 1, 189-202.
Hinton, G. E. 2006. To recognize shapes, first learn to generate images. In P. Cisek, T. Drew and J. Kalaska (Eds.) Computational Neuroscience: Theoretical Insights into Brain Function. Elsevier.
Jern, A., & Kemp, C. 2011. Capturing mental state reasoning with influence diagrams. Cognitive Science Society.
Kemp, C., Goodman, N. D., & Tenenbaum, J. B. 2010. Learning to learn causal models. Cognitive Science, 34(7), 1185-1243.
Kemp, C., & Tenenbaum, J. B. 2009. Structured statistical models of inductive reasoning. Psychological Review, 116(1), 20-58.
Kao, J., J. Wu., L. Bergen., & N. D. Goodman. 2014. Proceedings of the National Academy of Sciences. www.pnas.org/cgi/doi/10.1073/pnas.1407479111.
Lee, M.D., Steyvers, M., de Young, M., & Miller. B.J. 2012. Inferring expertise in knowledge and prediction ranking tasks. Topics in Cognitive Science, 4, 151-163.
Lee, M.D., Steyvers, M., and Miller, B.J. 2014. A cognitive model for aggregating people's rankings. PLoS ONE, 9.
Lieder, F., Hsu, M., & Griffiths, T.L. 2014. The high availability of extreme events serves resource-rational decision-making. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Levy, R. 2011. Integrating surprisal and uncertain-input models in online sentence comprehension: Formal techniques and empirical results. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 1, 1055-1065.
Levy, R. 2013. Memory and Surprisal in Human Sentence Comprehension In van Gompel, Roger P. G., (eds), Sentence Processing, 78–114.
Marr, D. 1982. Vision. San Francisco: W.H. Freeman, pp. 3-43.
McClelland, J. L., Botvinick, M. M., Noelle, D. C., Plaut, D. C., Rogers, T. T., Seidenberg, M. S., & Smith, L. B. 2010. Letting structure emerge: connectionist and dynamical systems approaches to cognition. Trends in Cognitive Sciences, 14(8), 348-356.
Myslin, M. and Levy, R. In press. Codeswitching and predictability of meaning in discourse. Language.
Pearl, L. & Goldwater, S. In press. Statistical Learning, Inductive Bias, and Bayesian Inference in Language Acquisition. In J. Lidz, W. Snyder, & J. Pater (eds), The Oxford Handbook of Developmental Linguistics. Oxford University Press.
Perfors, A. 2012. Bayesian Models of Cognition: What's Built in After All? Philosophy Compass, 7(2), 127-138.
Perfors, A. 2014a. Bayesian inference in word learning. In PJ Brooks and V Kempe (Ed.) Encyclopedia of Language Development (pp. 46-49).
Perfors, A. 2014b. Induction in language learning. In PJ Brooks and V Kempe (Ed.) Encyclopedia of Language Development (pp. 281-283)
Perfors, A., Tenenbaum, J. B., Griffiths, T. L., & Xu, F. 2011. A tutorial introduction to Bayesian models of cognitive development. Cognition, 120(3), 302-321.
Piantadosi, S., Tenenbaum, J., & Goodman, N. 2012. Bootstrapping in a language of thought: A formal model of numerical concept learning. Cognition, 123, 199-217.
Saxe, A. M., McClelland, J. L., & Ganguli, S. 2013. Learning hierarchical category structure in deep neural networks. In Proceedings of the 35th Annual Conference of the Cognitive Science Society.
Steyvers, M. & Hemmer, P. 2012. Reconstruction from Memory in Naturalistic Environments. In Brian H. Ross (ed.) The Psychology of Learning and Motivation, Vol 56. Elsevier Publishing, pp. 126-144.
Steyvers, M., Miller, B. In press. Cognition and Collective Intelligence. In T.W. Malone and M.S. Bernstein (Eds.) The Collective Intelligence Handbook.
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199.
Tenenbaum, J. B., & Griffiths, T. L. 2001. Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24(4), 629-640.
Turner, B.M., Forstmann, B.U., Wagenmakers, E.J., Brown, S.D., Sederberg, P.B., and Steyvers, M. 2013. A Bayesian framework for simultaneously modeling neural and behavioral data. NeuroImage, 72, 193-206.
Xu, F., Dewar, K., & Perfors, A. 2009. Induction, overhypotheses, and the shape bias: Some arguments and evidence for rational constructivism. The origins of object knowledge, 263-284.
Yi, S.K.M., Steyvers, M., & Lee, M.D. 2012. The Wisdom of Crowds in Combinatorial Problems. Cognitive Science, 36(3), 452-470.