Categorisation

[pdf] Goldstone, R. L., Kersten, A., & Carvalho, P. F. (2017).  Categorization and Concepts.  In J. Wixted (Ed.) Stevens’ Handbook of Experimental Psychology and Cognitive neuroscience, Fourth Edition, Volume Three: Language & Thought.  New Jersey: Wiley: 275-317.

[pdf] Watanabe, T., & Sasaki, Y. (2015) Perceptual learning: Toward a comprehensive theory. Annual Review of Psychology 66: 197-221

[pdf] Nosofsky, R.M. (2014). The generalized context model: an exemplar model of classification. In M. Pothos and A. Wills (Eds.), Formal Approaches in Categorization. Cambridge University: 18-39

[pdf] Goldstone, R. L., & Day, S. B. (2013).  Similarity. In H. Pashler (Ed.) The Encyclopedia of Mind. SAGE Reference: Thousand Oaks, CA: 696-699

[pdf] Goldstone, R. L., Braithwaite, D.  W., & Byrge, L. A. (2012). Perceptual learning.  In N. M. Seel (Ed.) Encyclopedia of the Sciences of Learning. Heidelberg, Germany, Springer Verlag: 2616-2619.

[pdf] Krushke, J. (2011) Models of attentional learning. In E.M. Pothos & A.J. Wills (eds.) Formal approaches in categorisation. Cambridge University Press: 120-152.

[pdf] Perfors, A., Tenenbaum, J., Griffiths, T., & Xu, F. (2011). A tutorial introduction to Bayesian models of cognitive development. Cognition 120: 302-321

[pdf] Griffiths, T., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. (2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences, 14(8): 357-364.

[pdf] Griffiths, T., Sanborn, A., Canini, K., & Navarro, D. (2008). Categorization as nonparametric Bayesian density estimation. In M. Oaksford and N. Chater (Eds.). The probabilistic mind: Prospects for rational models of cognition. Oxford: Oxford University Press.

[pdf] Krushke, J. (2008) Models of categorisation. In Ron Sun (ed.) The Cambridge Handbook of Computational Psychology. New York: Cambridge University Press: 267-301

[pdf] Medin, D. (1989) Concepts and conceptual structure. American Psychologist 44(12): 1469-1481

[pdf] Rosch, E. (1978) Principles of categorisation. Rosch, E and Lloyd, B. (eds), Cognition and categorization. Hillsdale, NJ: Lawrence Erlbaum. 27-48.

[pdf] Shafto, P., Goodman, N. D., & Griffiths, T. L (2014). A rational account of pedagogical reasoning: Teaching by, and learning from, examples. Cognitive Psychology 71: 55-89.

[pdf] Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010). Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review 117(4): 1144-1167

[pdf] Xu, F., & Tenenbaum, J. (2007). Word learning as Bayesian inference. Psychological Review 114(2): 245

[pdf] Kemp, C., Perfors, A. & Tenenbaum, J. B. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science 10(3): 307-321

[pdf] Love, B., Medin, D., & Gureckis, T. (2004) SUSTAIN: A network model of human category learning. Psychological Review 111(2): 309-332.

[pdf] Tenenbaum, J., & Griffiths, T. (2001) Generalisation, similarity, and Bayesian inference. Behavioural and Brain Sciences 24: 629-640.

[pdf] Nosofsky, R., Palmeri, T., & McKinley, S. (1994). Rule-plus-exception model of classification learning. Psychological Review 101(1): 53-79

[pdf] Medin, D.L., Goldstone, R.L., & Gentner, D. (1993). Respects for similarity. Psychological Review 100: 254-278.

[pdf] Kruschke, J. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review 99(1): 22-44.

[pdf] Anderson, J. (1991). The adaptive nature of human categorisation. Psychological Review 98(3): 409-429

[pdf] Fodor, J., & Pylyshyn, Z. (1988). Connectionism and Cognitive Architecture: A Critical Analysis, Cognition 28: 3–71.

[pdf] Shepard, R. (1987) Toward a Universal Law of Generalization for Psychological Science. Science 237(4820): 1317-1323.

[pdf] Nosofsky, R. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General 115(1): 39-57

[pdf] Rumelhart, D., Hinton, G., & Williams, R. (1986) Learning representations by back-propagating errors. Nature 323(9): 533-536

[pdf] Medin, D., & Schaffer, M. (1978). Context theory of classification learning. Psychological Review 85(3): 207-328

[pdf] Rosch, E., Mervis, C., Gray, W., Johnson, D., & Boyes-Bream, P. (1976) Basic objects in natural categories.Cognitive Psychology 8(3): 382-439

[pdf] Rosch, E., & Mervis, C. (1975). Family Resemblances: Studies in the Internal Structure of Categories. Cognitive Psychology 7: 573-605

[pdf] Rosch, E. (1973).  Natural categories. Cognitive Psychology 4: 328-350

[pdf] Shepard, R., Hovland, C., & Jenkins, H. (1961). Learning and memorisation of classifications. Psychological Monographs: General and Applied 75(13)

[pdf] Thai, K-P., Son, J. Y., & Goldstone, R. L. (2016).  The simple advantage in perceptual and categorical generalization.  Memory & Cognition, 44: 292-306.

[pdf] Goldstone, R. L., de Leeuw, J. R., & Landy, D. H. (2015).  Fitting perception in and to cognition. Cognition, 135: 24-29.

[pdf] Jara-Ettinger, J., Gweon, H., Tenenbaum, J., & Schulz, L. (2015). Children's understanding of the costs and rewards underlying rational action. Cognition 140: 14-23

[pdf] Bonawitz, E., Denison, S., Griffiths, T., & Gopnik, A. (2014). Probabilistic models, learning algorithms, and response variability: sampling in cognitive development. Trends in Cognitive Sciences 18(10): 497-500.

[pdf] Bonawitz, E., & Lombrozo, T. (2012). Occam's rattle: Children's use of simplicity and probability to constrain inference. Developmental Psychology 48(4): 1156-1164.

[pdf] Gweon, H., Tenenbaum, J., & Schulz, L. (2010). Infants consider both the sample and the sampling process in inductive generalization. Proceedings of the National Academy of Sciences 107(20): 9066-9071

[pdf] Murphy, G., & Ross, B. (2010). Category vs. object knowledge in category-based induction. Journal of Memory and Language 63: 1–17

[pdf] Son, J. Y., Smith, L. B., & Goldstone, R. L. (2008).  Simplicity and generalization: Short-cutting abstraction in children’s object categorizations. Cognition 108, 626-638.

[pdf] Kalish, M., Lewandowsky, S., & Kruschke, J. (2004). Population of linear experts: Knowledge partitioning and function learning. Psychological Review 111: 1072–1099.

[pdf] Ashby, F., Ell, S., &Waldron, E. (2003). Procedural learning in perceptual categorization. Memory & Cognition 31: 1114–1125.

[pdf] Yang, L.-X., & Lewandowsky, S. (2003). Context-gated knowledge partitioning in categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(4): 663-679.

[pdf] Klein, D., & Murphy, G. (2002). Paper has been my ruin: conceptual relations of polysemous senses. Journal of Memory and Language 47: 548–570

[pdf] Goldstone, R. L. (2000). Unitization during category learning.  Journal of Experimental Psychology: Human Perception and Performance 26: 86-112

[pdf] Ross, B., & Murphy, G. (1999). Food for thought: Cross-classification and category organisation in a complex real-world domain. Cognitive Psychology 38: 495-553.

[pdf] Ashby, F., Alfonso-Reese, L., Turken, A., &Waldron, E. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review 105: 442–481.

[pdf] Goldstone, R. L. (1994). Influences of categorization on perceptual discrimination. Journal of Experimental Psychology: General 123: 178-200.

[pdf] Osherson, D., Wilkie, O., Smith, E., Lopez, A., & Shafir, E. (1990) Category-based induction. Psychological Review 97(2): 185-200.

[pdf] Barsalou, L. (1983). Ad hoc categories. Memory & Cognition 11(3): 211-227

[pdf] Lake, B., Ullman, T., Tenenbaum, J., & Gershman, S. (2017). Building machines that learn and think like people. Behavioural and Brain Sciences 40

[pdf] De Deyne, S., Navarro, D., Perfors, A., & Storms, G. (2016). Structure at every scale: A semantic network account of the similarities between very unrelated concepts. Journal of Experimental Psychology: General, 145(9): 1228-54

[pdf] Goodman, N. D., & Frank, M. C. (2016). Pragmatic language interpretation as probabilistic inference. Trends in Cognitive Sciences, 20(11): 818-829.

[pdf] Peterson, J., Abbott, J., & Griffiths, T. L. (2016). Adapting deep network features to capture psychological representations. Proceedings of the 38th Annual Conference of the Cognitive Science Society.

[pdf] Piantadosi, S., Tenenbaum, J., & Goodman, D. (2016). The logical primitives of thought: Empirical foundations for compositional cognitive models. Psychological Review 123(4): 392-424.

[pdf] Ransom, K., Perfors, A., & Navarro, D. (2016). Leaping to conclusions: Why premise relevance affects argument strength. Cognitive Science 40(7): 1775-1796.

[pdf] Vong, W.K., Perfors, A., & Navarro, D. (2016). The helpfulness of category labels in semi-supervised learning depends on category structure. Psychonomic Bulletin and Review 23: 230-238

[pdf] Lake, B., Salakhutdinov, R., & Tenenbaum, J. (2015). Human-level concept learning through probabilistic program induction. Science 350(6266): 1332-1338

[pdf] Voorspoels, W., Navarro, D., Perfors, A., Ransom, K., & Storms, G. (2015). How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning. Cognitive Psychology 81: 1-25

[pdf] Frank, M., & Goodman, N. (2014). Inferring word meanings by assuming that speakers are informative. Cognitive Psychology.

[pdf] Austerweil, J., & Griffiths, T. L. (2013). A nonparametric Bayesian framework for constructing flexible feature representations. Psychological Review 120: 817-851.

[pdf] Jern, A. & Kemp, C. (2013). A probabilistic account of exemplar and category generation. Cognitive Psychology 66(1): 85-125.

[pdf] Navarro, D., Perfors, A., & Vong, W.K. (2013). Learning time-varying categories. Memory and Cognition 41: 917-927

[pdf] Griffiths, T. L., Vul, E., & Sanborn, A. N. (2012). Bridging levels of analysis for probabilistic models of cognition. Current Directions in Psychological Science, 21(4): 263-268.

[pdf] Kemp, C. (2012). Exploring the conceptual universe. Psychological Review 119(4): 685-722.

[pdf] Kemp, C., Shafto, P., & Tenenbaum, J. B. (2012) An integrated account of generalization across objects and features. Cognitive Psychology 64 (1-2): 35-73.

[pdf] Little, D., Nosofsky, R., Donkin, C., & Denton, S. (2012). Logical rules and the classification of integral-dimension stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition 39(3):801-20

[pdf] Shafto, P., Goodman, N. D., & Frank, M. C. (2012). Learning from others: The consequences of psychological reasoning for human learning. Perspectives on Psychological Science 7(4): 341-351.

[pdf] Tenenbaum, J. B., Kemp, C., Griffiths, T. L. & Goodman, N. D. (2011). How to grow a mind: statistics, structure and abstraction. Science 331(6022): 1279-1285

[pdf] Pothos, E., Perlman, A., Bailey, T., Kurtz, K., Edwards, D., Hines, P., & McDonnell, J. (2011). Measuring category intuitiveness in unconstrained categorisation tasks. Cognition 121(1): 83-100.

[pdf] Shi, L., Griffiths, T. L., Feldman, N. H, & Sanborn, A. N. (2010). Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review, 17 (4): 443-464.

[pdf] Kemp, C., & Tenenbaum, J. B. (2009). Structured statistical models of inductive reasoning. Psychological Review 116(1): 20-58

[pdf] Goodman, N., Tenenbaum, J., Feldman, J., & Griffiths, T. (2008). A rational analysis of rule‐based concept learning. Cognitive Science 32(1): 108-154

[pdf] Griffiths, T. L., Christian, B. R., & Kalish, M. L. (2008). Using category structures to test iterated learning as a method for revealing inductive biases. Cognitive Science 32: 68-107.

[pdf] Kemp, C., & Tenenbaum, J. B. (2008). The discovery of structural form. Proceedings of the National Academy of Sciences 105(31): 10687-10692.

[pdf] Rogers, T., & McClelland, J. (2008) Precis of Semantic Cognition: A parallel distributed processing approach. Behavioural and Brain Sciences 31: 689-749

[pdf] Griffiths, T., Steyvers, M., & Tenenbaum, J. (2007).Topics in semantic representation. Psychological Review 114(2): 211

[pdf] Xu, F., & Tenenbaum, J. (2007). Sensitivity to sampling in Bayesian word learning. Developmental Science 10(3): 288-297

[pdf] Griffiths, T., & Tenenbaum, J. (2006). Optimal predictions in everyday cognition. Psychological Science 17(9): 767-773.

[pdf] Steyvers, M., & Tenenbaum, J. (2005). The Large‐scale structure of semantic networks: Statistical analyses and a model of semantic growth. Cognitive Science 29 (1): 41-78

[pdf] Colunga, E., & Smith, L. (2005). From the lexicon to expectations about kinds: A role for associative learning. Psychological Review 112(2): 347-382.

[pdf] Schyns, P. G., Goldstone, R. L., & Thibaut, J-P (1998). Development of features in object concepts.  Behavioral and Brain Sciences 21: 1-54.

[pdf] Krushke, J. (1996) Base rates in category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition 22(1): 3-26

[pdf] Nosofsky, R.,& Palmeri, T. (1996). Learning to classify integral-dimension stimuli. Psychonomic Bulletin & Review 3(2): 222–226

[pdf] Lewandowsky, S. (1995). Base-rate neglect in ALCOVE: A critical reevaluation. Psychological Review 102(1): 185-191.

[pdf] Ericsson, M., & Krushke, J. (1998) Rules and exemplars in category learning. Journal of Experimental Psychology: General 127(2): 107-140

[pdf] Nosofsky, R. (1991). Relation between the rational model and the context model of categorisation. Psychological Science 2(6): 416-421.

[pdf] Nosofsky, R. (1988). Similarity, frequency, and category representations. Journal of Experimental Psychology: Learning, Memory, and Cognition 14(1): 54-65

[pdf] Nosofsky, R. (1988). Exemplar-based accounts of relations between classification, recognition, and typicality. Journal of Experimental Psychology: Learning, Memory, and Cognition 14(4): 700-708

[pdf] Shepard, R. (1980) Multidimensional scaling, tree-fitting, and clustering. Science 210(4468): 390-398.