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  • Anderson, J. R. The adaptive character of thought 1990 Hillsdal, NJ: Lawrence Erlbaum Associates, Inc
  • Anderson, J. R. The adaptive nature of human categorization. Psychological Review 1991 98 409-429
  • Armstrong, S. L., Gleitman, L. R., Gleitman, H. What some concepts might not be. Cognition 1983 13 263-308
  • Briscoe, E., Feldman, J. Conceptual complexity and the bias-variance tradeoff In Sun, R. (Ed.), 2006 Proceedings of the Conference of the Cognitive Science Society Mahwa, NJ: Lawrence Erlbaum Associates, Inc 1038-1043
  • Bruner, J. S., Goodnow, J. J., Austin, G. A. A study of thinking 1956 New York: Wiley
  • Cassirer, E. Language and myth Langer, Suzanne K. 1946 New York: Harper & Row
  • Chater, N., Oaksford, M. Ten years of the rational analysis of cognition. Trends in Cognitive Science 1999 3 2 57-65
  • Chopin, N. A sequential particle filter method for static models. Biometrika 2002 89 539-552
  • Doucet, A., De Freitas, N., Gordon, N. Sequential Monte Carlo methods in practice 2001 New York: Springer
  • Enderton, H. B. A mathematical introduction to logic 1972 New York: Academic
  • Erickson, M. A., Kruschke, J. K. Rules and exemplars in category learning. Journal of Experimental Psychology: General. 1998 127 107-140
  • Feldman, J. Minimization of Boolean complexity in human concept learning. Nature 2000 407 630-633
  • Feldman, J. The simplicity principle in human concept learning. Current Directions in Psychological Science 2003 12 227-232
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  • Feldman, J. How surprising is a simple pattern? Quantifying “Eureka!”. Cognition 2004 93 199-224
  • Feldman, J. An algebra of human concept learning. Journal of Mathematical Psychology 2006 50 339-368
  • Fodor, J. A. The language of thought 1975 Cambridg, MA: Harvard University Press
  • Fodor, J. A. Concepts: Where cognitive science went wrong 1998 New York: Oxford University Press
  • Geisler, W. S. Ideal observer analysis In Chalupa, L., Werner, J. (Eds.), The visual neurosciences 2003 Cambridg, MA: MIT Press 825-837
  • Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B. Bayesian data analysis 1995 New York: Chapman & Hall
  • Gentner, D., Kurtz, K. Categorization inside and outside the lab In Ahn, W. K., Goldstone, R. L., Love, B. C., Markman, A. B., Wolff, P. W. (Eds.), Learning and using relational categories 2005 Washingto, DC: American Psychological Association 151-175
  • Goldstone, R. Isolated and interrelated concepts. Memory Cognition 1996 24 608-628
  • Goodman, N. Fact, fiction, and forecast 1955 Cambridg, MA: Harvard University Press
  • Goodman, N. D., Tenenbaum, J. B., Griffiths, T. L., Feldman, J. Compositionality in rational analysis: Grammar-based induction for concept learning In Oaksford, M., Chater, N. (Eds.), The probabilistic mind: Prospects for Bayesian cognitive science Oxford England: Oxford University Press (in press)
  • Jaynes, E. T. Probability theory: The logic of science 2003 Cambridge, England: Cambridge University Press
  • Johansen, M., Palmeri, T. Are there representational shifts during category learning Cognitive Psychology 2002 45 482-553
  • Kruschke, J. K. ALCOVE: An exemplarbased connectionist model of category learning. Psychological Review 1992 99 22-44
  • Kruschke, J. K. Human category learning: Implications for backpropagation models. Connection Science 1993 5 3-36
  • Kruschke, J. K. Locally Bayesian learning with applications to retrospective revaluation and highlighting. Psychological Review 2006 113 677-699
  • Lakoff, G. Women, fire, and dangerous things: What categories reveal about the mind 1987 Chicago: University of Chicago Press
  • Lamberts, K. Information-accumulation theory of speeded categorization. Psychological Review 2000 107 227-260
  • Love, B. C. Comparing supervised and unsupervised category learning. Psychonomic Bulletin & Review 2002 9 829-835
  • Love, B. C., Gureckis, T. M., Medin, D. L. SUSTAIN: A network model of category learning. Psychological Review 2004 111 309-332
  • Luce, R. D. Individual choice behavior 1959 New York: Wiley
  • Luce, R. D. Response times: Their role in inferring elementary mental organization 1986 Oxford, England: Oxford University Press
  • Maddox, W. T., Ashby, F. G. Comparing decision bound and exemplar models of categorization. Perception and Psychophysics 1993 53 49-70
  • Manning, C., Schütze, H. Foundations of statistical natural language processing 1999 Cambridg, MA: MIT Press
  • Markman, A. B., Stilwell, C. H. Rolegoverned categories. Journal of Experimental and Theoretical Artificial Intelligence 2001 13 329-358
  • Marr, D. Vision 1982 San Francisco: Freeman
  • McKinley, S. C., Nosofsky, R. M. Attention learning in models of classification 1993 unpublished manuscript
  • Medin, D. L., Altom, M. W., Edelson, S. M., Freko, D. Correlated symptoms and simulated medical classification. Journal of Experimental Psychology: Learning, Memory, and Cognition 1982 8 37-50
  • Medin, D. L., Schaffer, M. M. Context theory of classification learning. Psychological Review 1978 85 207-238
  • Medin, D. L., Schwanenflugel, P. J. Linear separability in classification learning. Journal of Experimental Psychology: Human Learning and Memory 1981 7 355-368
  • Medin, D. L., Wattenmaker, W. D., Hampson, S. E. Family resemblance, conceptual cohesiveness, and category construction. Cognitive Psychology 1987 19 242-279
  • Mervis, C. B., Rosch, E. H. Categorization of natural objects. Annual Review of Psychology 1981 32 89-115
  • Metropolis, A. W., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., Teller, E. Equations of state calculations by fast computing machines. Journal of Chemical Physics 1953 21 1087-1092
  • Murphy, G. L. The big book of concepts 2002 Cambridg, MA: MIT Press
  • Navarro, D. J. From natural kinds to complex categories In Sun, R., Miyake, N. (Eds.), 2006 Proceedings of the 28th Annual Conference of the Cognitive Science Society Mahwa, NJ: Lawrence Erlbaum Associates, Inc 621-626
  • Nosofsky, R. M. Attention, similarity, and the identification–categorization relationship. Journal of Experimental Psychology: General. 1986 115 39-61
  • Nosofsky, R. M., Clark, S. E., Shin, H. J. Rules and exemplars in categorization, identification, and recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition. 1989 15 282-304
  • Nosofsky, R. M., Palmeri, T. J. Learning to classify integral-dimension stimuli. Psychonomic Bulletin & Review 1996 3 222-226
  • Nosofsky, R. M., Palmeri, T. J. A rule-plus-exception model for classifying objects in continuous-dimension spaces. Psychonomic Bulletin & Review 1998 5 345-369
  • Nosofsky, R. M., Palmeri, T. J., McKinley, S. C. Rule-plus-exception model of classification learning. Psychological Review 1994 101 53-79
  • Oaksford, M., Chater, N. Rational models of cognition 1998 Oxford England: Oxford University Press
  • Osherson, D. N., Smith, E. E. On the adequacy of prototype theory as a theory of concepts. Cognition 1981 9 35-58
  • Pinker, S. How the mind works 1997 New York: Norton
  • Posner, M. I., Keele, S. W. On the genesis of abstract ideas. Journal of Experimental Psychology 1968 77 353-363
  • Pothos, E. M. The rules versus similarity distinction. Behavioral Brain Sciences 2005 28 1-49
  • Ratcliff, R., Zandt, T. V., McKoon, G. Connectionist and diffusion models of reaction time. Psychological Review 1999 106 261-300
  • Reed, S. K. Pattern recognition and categorization. Cognitive Psychology 1972 3 393-407
  • Rehder, B. A causal-model theory of conceptual representation and categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition 2003 29 1141-1159
  • Russell, S. J., Norvig, P. Artificial intelligence: A modern approach, 2002 2nd ed. Englewood Cliff, NJ: Prentice Hall
  • Sanborn, A., Griffiths, T., Navarro, D. A more rational model of categorization In Sun, R. (Ed.), 2006 Proceedings of the 28th Annual Conference of the Cognitive Science Society Mahwa, NJ: Lawrence Erlbaum Associates, Inc.
  • Schyns, P. G., Goldstone, R. L., Thibaut, J. P. The development of features in object concepts (with commentary). Behavioral and Brain Sciences 1998 21 1-54
  • Schyns, P. G., Rodet, L. Categorization creates functional features. Journal of Experimental Psychology: Learning, Memory, and Cognition 1997 23 681-696
  • Shepard, R. N. Towards a universal law of generalization for psychological science. Science 1987 237 1317-1323
  • Shepard, R. N., Chang, J. J. Stimulus generalization in the learning of classifications. Journal of Experimental Psychology 1963 65 94-102
  • Shepard, R. N., Hovland, C. L., Jenkins, H. M. Learning and memorization of classifications. Psychological Monographs: General and Applied 1961 75 13 1-42
  • Sloman, S. A. The empirical case for two systems of reasoning. Psychological Bulletin 1996 119 3-22
  • Sloman, S. A., Love, B. C., Ahn, W.-K. Feature centrality and conceptual coherence. Cognitive Science 1998 22 189-228
  • Sloman, S. A., Rips, L. J. Similarity as an explanatory construct. Cognition. 1998 65 87-101
  • Smith, J. D., Minda, J. P. Prototypes in the mist: The early epochs of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition 1998 24 1411-1436
  • Smith, J. D., Minda, J. P., Washburn, D. A. Category learning in rhesus monkeys: A study of the Shepard, Hovland, and Jenkins (1961) tasks. Journal of Experimental Psychology: General. 2004 133 398-414
  • Tenenbaum, J. B. A Bayesian framework for concept learning 1999a Cambridg, MA: Massachussets Institute of Technology Unpublished doctoral dissertation
  • Tenenbaum, J. B. Bayesian modeling of human concept learning In Kearns, M. S., Solla, S. A., Cohn, D. A. (Eds.), Advances in neural information processing systems 11 1999b Cambridg, MA: MIT Press 59-65
  • Tenenbaum, J. B. Rules and similarity in concept learning In Solla, S. A., Leen, T. K., Muller, K. R. (Eds.), Advances in neural information processing systems 12 2000 Cambridg, MA: MIT Press 59-65
  • Tenenbaum, J. B., Griffiths, T. L. Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences 2001 24 629-641
  • Tenenbaum, J. B., Xu, F. Word learning as Bayesian inference In Gleitman, L. R., Joshi, A. K. (Eds.), 2000 Proceedings of the 22nd Annual Conference of the Cognitive Science Society Hillsdal, NJ: Lawrence Erlbaum Associates, Inc 517-522
  • Von Humboldt, W. On language 18631988 New York: Cambridge University Press
  • Wittgenstein, L. Philosophical investigations 1953 New York: Macmillan
  • Xu, F., Tenenbaum, J. B. Word learning as Bayesian inference. Psychological Review 2007 114 245-272