Here are a bunch of papers and resources on the general topic of categorisation. Please don't share this page widely because if it gets linked to I'm violating copyright for the papers. Feel free to use this as a general resource for yourself though!

  • Overview

    [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.

  • Foundational

    [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)

  • Empirical

    [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

  • Computational

    [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.