Skip to main content Accessibility help
×
Home
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 70
  • Print publication year: 2008
  • Online publication date: June 2012

3 - Bayesian Models of Cognition

from Part II - Cognitive Modeling Paradigms

References

Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985). A learning algorithm for Boltzmann machines. Cognitive Science, 9, 147–169.
Anderson, J. R. (1990). The adaptive character of thought. Hillsdale, NJ: Lawrence Erlbaum.
Ashby, F. G., & Alfonso-Reese, L. A. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39, 216–233.
Atran, S. (1998). Folk biology and the anthropology of science: Cognitive universals and cultural particulars. Behavioral and Brain Sciences, 21, 547–609.
Baker, C. L., Tenenbaum, J. B., & Saxe, R. R. (2007). Goal inference as inverse planning. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Meeting of the Cognitive Science Society (pp. 779–784). Austin, TX: Cognitive Science Society
Bayes, T. (1763/1958). Studies in the history of probability and statistics: IX. Thomas Bayes’s essay towards solving a problem in the doctrine of chances. Biometrika, 45, 296–315.
Bernardo, J. M., & Smith, A. F. M. (1994). Bayesian theory. New York: Wiley.
Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.
Blei, D., Griffiths, T., Jordan, M., & Tenenbaum, J. (2004). Hierarchical topic models and the nested Chinese restaurant process. In S. Thrun, L. K. Saul & B. Schölkopt (Eds.), Advances in Neural Information Processing Systems 16 (pp. 17–24). Cambridge, MA: MIT Press.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Boas, M. L. (1983). Mathematical methods in the physical sciences (2nd ed.). New York: Wiley.
Brainard, D. H., & Freeman, W. T. (1997). Bayesian color constancy. Journal of the Optical Society of America A, 14, 1393–1411.
Buehner, M., & Cheng, P. W. (1997). Causal induction: The Power PC theory versus the Rescorla-Wagner theory. In M. Shafto & P. Langley (Eds.), Proceedings of the 19th Annual Conference of the Cognitive Science Society (pp. 55–61). Hillsdale, NJ: Lawrence Erlbaum.
Buehner, M. J., Cheng, P. W., & Clifford, D. (2003). From covariation to causation: A test of the assumption of causal power. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 1119–1140.
Carey, S. (1985). Conceptual change in childhood. Cambridge, MA: MIT Press.
Charniak, E. (1993). Statistical language learning. Cambridge, MA: MIT Press.
Chater, N., & Manning, C. D. (2006). Probabilistic models of language processing and acquisition. Trends in Cognitive Sciences, 10, 335–344.
Cheng, P. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367–405.
Chomsky, N. (1988). Language and problems of knowledge: The Managua lectures. Cambrigde, MA: MIT Press.
Collins, A. M., & Loftus, E. F. (1975). A spreading activation theory of semantic processing. Psychological Review, 82, 407–428.
Collins, A. M., & Quillian, M. R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behaviour, 8, 240–247.
Courville, A. C., Daw, N. D., & Touretzky, D. S. (2006). Bayesian theories of conditioning in a changing world. Trends in Cognitive Sciences, 10, 294–300.
Danks, D., Griffiths, T. L., & Tenenbaum, J. B. (2003). Dynamical causal learning. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances Neural Information Processing Systems 15 (pp. 67–74). Cambridge, MA: MIT Press.
Doya, K., Ishii, S., Pouget, A., & Rao, R. P. N. (Eds.). (2007). The Bayesian brain: Probabilistic approaches to neural coding. Cambridge, MA: MIT Press.
Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification. New York: Wiley.
Friedman, N., & Koller, D. (2000). Being Bayesian about network structure. In Proceedings of the 16th Annual Conference on Uncertainty in AI (pp. 201–210). San Francisco, CA: Morgan Kaufmann.
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995). Bayesian data analysis. New York: Chapman & Hall.
Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741.
Ghahramani, Z. (2004). Unsupervised learning. In O. Bousquet, G. Raetsch, & U. von Luxburg1 (Eds.), Advanced lectures on machine learning (pp. 72–122). Berlin: Springer-Verlag.
Gigerenzer, G., Swijtink, Z., Porter, T., Daston, L., Beatty, J., & Kruger, L. (1989). The empire of chance. Cambridge, UK: Cambridge University Press.
Gilks, W., Richardson, S., & Spiegelhalter, D. J. (Eds.). (1996). Markov chain Monte Carlo in practice. Suffolk, UK: Chapman and Hall.
Glymour, C. (2001). The mind’s arrows: Bayes nets and graphical causal models in psychology. Cambridge, MA: MIT Press.
Glymour, C., & Cooper, G. (1999). Computation, causation, and discovery. Cambridge, MA: MIT Press.
Goldstein, H. (2003). Multilevel statistical models (3rd ed.). London: Hodder Arnold.
Good, I. J. (1980). Some history of the hierarchical Bayesian methodology. In J. M. Bernardo, M. H. DeGroot, D. V. Lindley, & A. F. M. Smith (Eds.), Bayesian statistics (pp. 489–519). Valencia, Spain: Valencia University Press.
Gopnik, A., & Meltzoff, A. N. (1997). Words, thoughts, and theories. Cambridge, MA: MIT Press.
Gopnik, A., & Tenenbaum, J. B. (2007). Bayesian networks, Bayesian learning, and cognitive development. Developmental Science, 10, 281–287.
Griffiths, T. L. (2005). Causes, coincidences, and theories. Unpublished doctoral dissertation, Stanford University, Stanford, CA.
Griffiths, T. L., Baraff, E. R., & Tenenbaum, J. B. (2004). Using physical theories to infer hidden causal structure. In K. Forbus, D. Gentner, & T. Regier (Eds.), Proceedings of the 26th Annual Meeting of the Cognitive Science Society (pp. 446–451). Mahwah, NJ: Lawrence, Erlbaum.
Griffiths, T. L., & Ghahramani, Z. (2005). Infinite latent feature models and the Indian buffet process (Tech. Rep. No. 2005-001). London, UK: Gatsby Computational Neuroscience Unit.
Griffiths, T. L., & Steyvers, M. (2002). A probabilistic approach to semantic representation. In W. D. Gray & C. Schumn (Eds.), Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society (pp. 381–386). Hillsdale, NJ: Lawrence Erlbaum.
Griffiths, T. L., & Steyvers, M. (2003). Prediction and semantic association. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Neural information processing systems 15 (pp. 11–18). Cambridge, MA: MIT Press.
Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Science, 101, 5228–5235.
Griffiths, T. L., Steyvers, M., Blei, D. M., & Tenenbaum, J. B. (2005). Integrating topics and syntax. In L. K. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in Neural Information Processing Systems 17 (pp. 537–544). Cambridge, MA: MIT Press.
Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007). Topics in semantic association. Psychological Review, 114, 211–244.
Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51, 354–384.
Griffiths, T. L., & Tenenbaum, J. B. (2007a). From mere coincidences to meaningful discoveries. Cognition, 103, 180–226.
Griffiths, T. L., & Tenenbaum, J. B. (2007b). Two proposals for causal grammars. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 323–345). Oxford, UK: Oxford University Press.
Hacking, I. (1975). The emergence of probability. Cambridge, UK: Cambridge University Press.
Hagmayer, Y., Sloman, S. A., Lagnado, D. A., & Waldmann, M. R. (in press). Causal reasoning through intervention. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation. (pp. 323–345). Oxford, UK: Oxford University Press.
Hastings, W. K. (1970). Monte Carlo methods using Markov chains and their applications. Biometrika, 57, 97–109.
Heckerman, D. (1998). A tutorial on learning with Bayesian networks. In M. I. Jordan (Ed.), Learning in graphical models (pp. 301–354). Cambridge, MA: MIT Press.
Heibeck, T., & Markman, E. (1987). Word learning in children: An examination of fast mapping. Child Development, 58, 1021–1024.
Hofmann, T. (1999). Probablistic latent semantic indexing. In Proceedings of the Twenty-Second Annual International SIGIR Conference (pp. 50–57). New York: ACM Press.
Huelsenbeck, J. P., & Ronquist, F. (2001). MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics, 17(8), 754–755.
Jeffreys, W. H., & Berger, J. O. (1992). Ockham’s razor and Bayesian analysis. American Scientist, 80(1), 64–72.
Jenkins, H. M., & Ward, W. C. (1965). Judgment of contingency between responses and outcomes. Psychological Monographs, 79.
Jurafsky, D., & Martin, J. H. (2000). Speech and language processing. Upper Saddle River, NJ: Prentice Hall.
Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773–795.
Kemp, C., Perfors, A., & Tenenbaum, J. B. (2004). Learning domain structures. In R. Alterman & D. Kirsh (Eds.), Proceedings of the 26th Annual Conference of the Cognitive Science Society (pp. 658–683). Hillsdale, NJ: Lawrence Erlbaum.
Kemp, C., Perfors, A., & Tenenbaum, J. B. (2007). Learning overhypotheses with hierarchical bayesian models. Developmental Science, 10, 307–321.
Kemp, C., & Tenenbaum, J. B. (2003). Theory-based induction. In Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.
Korb, K., & Nicholson, A. (2003). Bayesian artificial intelligence. Boca Raton, FL: Chapman and Hall/CRC.
Kording, K. P., & Wolpert, D. M. (2006). Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences, 10, 319–326.
Lagnado, D., & Sloman, S. A. (2004). The advantage of timely intervention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 856–876.
Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: the Latent Semantic Analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104, 211–240.
Lee, M. D. (2006). A hierarchical Bayesian model of human decision-making on an optimal stopping problem. Cognitive Science, 30, 555–580.
Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical cooccurrence. Behavior Research Methods, Instrumentation, and Computers, 28, 203–208.
Mackay, D. J. C. (2003). Information theory, inference, and learning algorithms. Cambridge, UK: Cambridge University Press.
Manning, C., & Schütze, H. (1999). Foundations of statistical natural language processing. Cambridge, MA: MIT Press.
Mansinghka, V. K., Kemp, C., Tenenbaum, J. B., & Griffiths, T. L. (2006). Structured priors for structure learning. In R. Dechter & T. S. Richardson (Eds.), Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (UAI) Arlington, VA: ANAI Press.
Marr, D. (1982). Vision. San Francisco, CA: W. H. Freeman.
Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.
Metropolis, A. W., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equations of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092.
Minka, T., & Lafferty, J. (2002). Expectation-Propagation for the generative aspect model. In A. Darwiche & N. Frichman (Eds.), Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence (UAI) (pp. 352–359). San Francisco, CA: Morgan Kaufmann.
Myung, I. J., Forster, M. R., & Browne, M. W. (2000). Model selection [Special issue]. Journal of Mathematical Psychology, 44.
Myung, I. J., & Pitt, M. A. (1997). Applying Occam’s razor in modeling cognition: A Bayesian approach. Psychonomic Bulletin and Review, 4, 79–95.
Neal, R. M. (1992). Connectionist learning of belief networks. Artificial Intelligence, 56, 71–113.
Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo methods (Tech. Rep. No. CRG-TR-93-1). Toronto, Canada: University of Toronto.
Neal, R. M. (1998). Markov chain sampling methods for Dirichlet process mixture models (Tech. Rep. No. 9815). Toronto, Canada: Department of Statistics, University of Toronto.
Nelson, D. L., McEvoy, C. L., & Schreiber, T. A. (1998). The University of South Florida word association, rhyme, and word fragment norms. Retrieved month, day, year, from http://w3.usf.edu/FreeAssociation/
Newman, M. E. J., & Barkema, G. T. (1999). Monte carlo methods in statistical physics. Oxford, UK: Clarendon Press.
Nisbett, R. E., Krantz, D. H., Jepson, C., & Kunda, Z. (1983). The use of statistical heuristics in everyday inductive reasoning. Psychological Review, 90(4), 339–363.
Norris, J. R. (1997). Markov chains. Cambridge, UK: Cambridge University Press.
Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39–57.
Nosofsky, R. M. (1998). Optimal performance and exemplar models of classification. In M. Oaksford & N. Chater (Eds.), Rational models of cognition (pp. 218–247). Oxford, UK: Oxford University Press.
Oaksford, M., & Chater, N. (2001). The probabilistic approach to human reasoning. Trends in Cognitive Sciences, 5, 349–357.
Osherson, D. N., Smith, E. E., Wilkie, O., Lopez, A., & Shafir, E. (1990). Category-based induction. Psychological Review, 97(2), 185–200.
Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Francisco, CA: Morgan Kaufmann.
Pearl, J. (2000). Causality: Models, reasoning and inference. Cambridge, UK: Cambridge University Press.
Pitman, J. (1993). Probability. New York: Springer-Verlag.
Reed, S. K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3, 393–407.
Rice, J. A. (1995). Mathematical statistics and data analysis (2nd ed.). Belmont, CA: Duxbury.
Rips, L. J. (1975). Inductive judgments about natural categories. Journal of Verbal Learning and Verbal Behavior, 14, 665–681.
Russell, S. J., & Norvig, P. (2002). Artificial intelligence: A modern approach (2nd ed.). Englewood Cliffs, NJ: Prentice Hall.
Sloman, S. (2005). Causal models: How people think about the world and its alternatives. Oxford, UK: Oxford University Press.
Smith, L. B., Jones, S. S., Landau, B., Gershkoff-Stowe, L., & Samuelson, L. (2002). Object name learning provides on-the-job training for attention. Psychological Science, 13(1), 13–19.
Spirtes, P., Glymour, C., & Schienes, R. (1993). Causation prediction and search. New York: Springer-Verlag.
Steyvers, M., Griffiths, T. L., & Dennis, S. (2006). Probabilistic inference in human semantic memory. Trends in Cognitive Sciences, 10, 327–334.
Steyvers, M., Tenenbaum, J. B., Wagenmakers, E. J., & Blum, B. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27, 453–489.
Tenenbaum, J. B., & Griffiths, T. L. (2001). Structure learning in human causal induction. In T. Leen, T. Dietterich, & V. Tresp (Eds.), Advances in neural information processing systems 13 (pp. 59–65). Cambridge, MA: MIT Press.
Tenenbaum, J. B., & Griffiths, T. L. (2003). Theory-based causal induction. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems 15 (pp. 35–42). Cambridge, MA: MIT Press.
Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Science, 10, 309–318.
Tenenbaum, J. B., Griffiths, T. L., & Niyogi, S. (2007). Intuitive theories as grammars for causal inference. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 301–322). Oxford, UK: Oxford University Press.
Tenenbaum, J. B., & Niyogi, S. (2003). Learning causal laws. In R. Alterman & D. Kirsh (Eds.), Proceedings of the 25th Annual Meeting of the Cognitive Science Society (pp. 1152–1157). Hillsdale, NJ: Erlbaum.
Wellman, H. M., & Gelman, S. A. (1992). Cognitive development: Foundational theories of core domains. Annual Review of Psychology, 43, 337–375.
Xu, F., & Tenenbaum, J. B. (2007). Word learning as Bayesian inference. Psychological Review, 114, 245–272.
Yuille, A., & Kersten, D. (2006). Vision as Bayesian inference: analysis by synthesis? Trends in Cognitive Sciences, 10, 301–308.