2 - The generalized context model: an exemplar model of classification  pp. 18-39


By Robert M. Nosofsky

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Model description

Conceptual overview

According to the generalized context model (GCM) (Nosofsky, 1986), people represent categories by storing individual exemplars (or examples) in memory, and classify objects based on their similarity to these stored exemplars. For example, the model assumes that people represent the category of ‘birds’ by storing in memory the vast collection of different sparrows, robins, eagles, ostriches (and so forth) that they have experienced. If an object is sufficiently similar to some of these bird exemplars, then the person would tend to classify the object as a ‘bird’. This exemplar view of categorization contrasts dramatically with major alternative approaches that assume that people form abstract summary representations of categories, such as rules or idealized prototypes.

The standard version of the GCM adopts a multidimensional scaling (MDS) approach to modelling similarity relations among exemplars (Shepard, 1958, 1987). In this approach, exemplars are represented as points in a multidimensional psychological space. Similarity between exemplars is a decreasing function of their distance in the space. In many applications, a first step in the modelling is to conduct similarity-scaling studies to derive MDS solutions for the exemplars and to discover their locations in the multidimensional similarity space (Nosofsky, 1992b).

A crucial assumption in the modelling, however, is that similarity is not an invariant relation, but a highly context-dependent one. To take an example from Medin and Schaffer (1978), humans and mannequins may be judged as highly similar in a context that emphasizes structural appearance, but would be judged as highly dissimilar in a context that emphasizes vitality.

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Reference Title: REFERENCES

Reference Type: reference-list

Anderson, J.R. (1991). The adaptive nature of human categorization. Psychological Review, 98, 409–429.
Ashby, G.F. , & Alfonso-Reese, A. L. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39, 216–233.
Ashby, G.F. , Alfonso-Reese, L.A. , Turken, A.U. , & Waldron, E.M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105, 442–481.
Busemeyer, J.R. , Wang, Z. , & Townsend, J.T. (2006). Quantum dynamics of human decision making. Journal of Mathematical Psychology, 50, 220–241.
Chater, N. (1996). Reconciling simplicity and likelihood principles in perceptual organization. Psychological Review, 103, 566–591.
Corter, J.E. , & Gluck, M.A. (1992). Explaining basic categories: feature predictability and information. Psychological Bulletin, 2, 291–303.
Fisher, D. (1996). Iterative optimization and simplification of hierarchical clusterings. Journal of Artificial Intelligence, 4, 147–179.
Fodor, J.A. (1983). The Modularity of Mind. Cambridge, MA: MIT Press.
Fraboni, M. , & Cooper, D. (1989). Six clustering algorithms applied to the WAIS-R: the problem of dissimilar cluster analysis. Journal of Clinical Psychology, 45, 932–935.
Griffiths, T.L. , Steyvers, M. , & Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114, 211–244.
Hampton, J.A. (2000). Concepts and prototypes. Mind and Language, 15, 299–307.
Heit, E. (1997). Knowledge and concept learning. In K. Lamberts & D. Shanks (eds.), Knowledge, Concepts, and Categories (pp. 7–41). London: Psychology Press.
Herrnstein, R.J. , & Loveland, D.H. (1964). Complex visual concept in the pigeon. Science, 146, 549–551.
Hull, C.L. (1920). Quantitative aspects of the evolution of concepts: an experimental study. Psychological Monographs, 28 (1), Whole No. 123.
Kurtz, K.J. (2007). The divergent autoencoder (DIVA) model of category learning. Psychonomic Bulletin & Review, 14, 560–576.
Lamberts, K. (2000). Information-accumulation theory of speeded categorization. Psychological Review, 107, 227–260.
Love, B.C. , Medin, D.L. , & Gureckis, T.M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111, 309–332.
McClelland, J.L. , & Rumelhart, D.E. (eds.) (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge, MA: MIT Press.
Medin, D.L. , & Schaffer, M.M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.
Medin, D.L. , & Schwanenflugel, P.J. (1981). Linear separability in classification learning. Journal of Experimental Psychology: Human Learning and Memory, 75, 355–368.
Minda, J.P. , & Smith, J.D. (2000). Prototypes in category learning: the effects of category size, category structure, and stimulus complexity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 775–799.
Murphy, G.L. , & Medin, D.L. (1985). The role of theories in conceptual coherence. Psychological Review, 92, 289–316.
Navarro, D.J. (2007). Similarity, distance, and categorization: a discussion of Smith's (2006) warning about ‘colliding parameters’. Psychonomic Bulletin & Review, 14, 823–833.
Nomura, E.M. , Maddox, W.T. , Filoteo, J.V. , Ing, A.D. , Gitelman, D.R. , Parrish, T.B. , Mesulam, M. M. , & Reber, P.J. (2007). Neural correlates of rule-based and information-integration visual category learning. Cerebral Cortex, 17, 37–43.
Nosofsky, R.M. (1988). Similarity, frequency, and category representation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 54–65.
Nosofsky, R. M. (1990). Relations between exemplar-similarity and likelihood models of classification. Journal of Mathematical Psychology, 34, 393–418.
Nosofsky, R.M. , & Kruschke, J.K. (2002). Single-system models and interference in category learning: commentary on Waldron and Ashby (2001). Psychonomic Bulletin & Review, 9, 169–174.
Plaut, D.C. , & Shallice, T. (1993). Deep dyslexia: a case study of connectionist neuropsychology. Cognitive Neuropsychology, 10, 377–500.
Plunkett, K. , & Bandelow, S. (2006). Stochastic approaches to understanding dissociations in inflectional morphology. Brain and Language, 98, 194–209.
Plunkett, K. , Karmiloff-Smith, A. , Bates, E. , & Elman, J.L. (1997). Connectionism and developmental psychology. Journal of Child Psychology & Psychiatry & Allied Disciplines, 38, 53–80.
Pothos, E.M. , & Bailey, T.M. (2009). Predicting category intuitiveness with the rational model, the simplicity model, and the Generalized Context Model. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 1062–1080.
Pothos, E.M. , & Chater, N. (2002). A simplicity principle in unsupervised human categorization. Cognitive Science, 26, 303–343.
Rehder, B. (2003). Categorization as causal reasoning. Cognitive Science, 27, 709–748.
Roberson, D. , Davidoff, J. , Davies, I.R.L. , & Shapiro, L.R. (2005). Color categories: evidence for the cultural relativity hypothesis. Cognitive Psychology, 50, 378–411.
Schyns, P.G. (1991). A modular neural network model of concept acquisition. Cognitive Science, 15, 461–508.
Shepard, R.N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317–1323.
Smith, J.D. (2007). When parameters collide: a warning about categorization models. Psychonomic Bulletin & Review, 13, 743–751.
Tenenbaum, J. , & Griffiths, T.L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24, 629–641.
Tyler, L.K. , Bright, P. , Dick, E. , Tavares, P. , Pilgrim, L. , Fletcher, P. , Greer, M. , & Moss, H. (2003). Do semantic categories activate distinct cortical regions? Evidence for a distributed neural semantic system. Cognitive Neuropsychology, 20, 541–559.
Vanpaemel, W. , & Storms, G. (2008). In search of abstraction: the varying abstraction model of categorization. Psychonomic Bulletin & Review, 15, 732–749.
van Rijsbergen, K. (2004). The Geometry of Information Retrieval. Cambridge: Cambridge University Press.
Zeithamova, D. , & Maddox, W.T. (2006). Dual-task interference in perceptual category learning. Memory & Cognition, 34, 387–398.
Zwickel, J. , & Wills, A.J. (2005). Integrating associative models of supervised and unsupervised categorization. In A.J. Wills (ed.), New Directions in Human Associative Learning. London: LEA.

Reference Title: REFERENCES

Reference Type: reference-list

Anderson, J.R. (1990). The Adaptive Character of Thought. Hillsdale, NJ: LEA.
Ashby, F.G. , & Alfonso-Reese, L. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39, 216–233.
Ashby, F.G. , & Maddox, W.T. (1993). Relations between exemplar, prototype, and decision bound models of categorization. Journal of Mathematical Psychology, 37, 372–400.
Busemeyer, J.R. , Dewey, G.I. , & Medin, D.L. (1984). Evaluation of exemplar- based generalization and the abstraction of categorical information. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 638–648.
Carroll, J.D. , & Wish, M. (1974). Models and methods for three-way multidimensional scaling. In D.H. Krantz , R.C. Atkinson , R.D. Luce , & P. Suppes (eds.), Contemporary Developments in Mathematical Psychology (Vol. 2). San Francisco, CA: W.H. Freeman.
Cohen, A.L. , Nosofsky, R.M. , & Zaki, S.R. (2001). Category variability, exemplar similarity, and perceptual classification. Memory & Cognition, 29, 1165–1175.
De Schryver, M. , Vandist, K. , & Rosseel, Y. (2009). How many exemplars are used? Explorations with the Rex Leopold I model. Psychonomic Bulletin & Review, 16, 337–343.
Ennis, D.M. (1988). Confusable and discriminable stimuli: comment on Nosofsky (1986) and Shepard (1986). Journal of Experimental Psychology: General, 117, 408–411.
Garner, W.R. (1974). The Processing of Information and Structure. New York: Wiley.
Jakel, F. , Scholkopf, B. , & Wichman, F.A. (2008). Generalization and similarity in exemplar models of categorization: insights from machine learning. Psychonomic Bulletin & Review, 15, 256–271.
Kruschke, J.K. (1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological Review, 99, 22–44.
Lamberts, K. (2000). Information accumulation theory of categorization response times. Psychological Review, 107, 227–260.
Lee, M.D. (2008). Three case studies in the Bayesian analysis of cognitive models. Psychonomic Bulletin & Review, 15, 1–15.
Luce, R.D. (1963). Detection and recognition. In R.D. Luce , R.R. Bush , & E. Galanter (eds.), Handbook of Mathematical Psychology (pp. 103–189). New York: Wiley.
McKinley, S.C. , & Nosofsky, R.M. (1995). Investigations of exemplar and decision bound models in large, ill-defined category structures. Journal of Experimental Psychology: Human Perception and Performance, 21, 128–148.
Medin, D.L. , & Schaffer, M.M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.
Nosofsky, R.M. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 104–114.
Nosofsky, R. M. (1985). Overall similarity and the identification of separable-dimension stimuli: a choice model analysis. Perception & Psychophysics, 38, 415–432.
Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39–57.
Nosofsky, R. M. (1987). Attention and learning processes in the identification and categorization of integral stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 87–109.
Nosofsky, R. M. (1988). Exemplar-based accounts of relations between classification, recognition, and typicality. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 700–708.
Nosofsky, R. M. (1989). Further tests of an exemplar-similarity approach to relating identification and categorization. Perception & Psychophysics, 45, 279–290.
Nosofsky, R. M. (1990). Relations between exemplar-similarity and likelihood models of classification. Journal of Mathematical Psychology, 34, 393–418.
Nosofsky, R. M. (1991a). Relation between the rational model and the context model of categorization. Psychological Science, 2, 416–421.
Nosofsky, R. M. (1991b). Tests of an exemplar model for relating perceptual classification and recognition memory. Journal of Experimental Psychology: Human Perception and Performance, 17, 3–27.
Nosofsky, R. M. (1992a). Exemplars, prototypes, and similarity rules. In A. F. Healy & S.M. Kossyln (eds.), Essays in Honor of William K. Estes, Vol. 1: From Learning Theory to Connectionist Theory (pp. 149–167). Hillsdale, NJ: LEA.
Nosofsky, R. M. (1992b). Similarity scaling and cognitive process models. Annual Review of Psychology, 43, 22–53.
Nosofsky, R. M. (2000). Exemplar representation without generalization: comment on Smith and Minda's (2000) ‘Thirty categorization results in search of a model’. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 1735–1743.
Nosofsky, R.M. , & Palmeri, T.J. (1997). An exemplar-based random-walk model of speeded classification. Psychological Review, 104, 266–300.
Nosofsky, R.M. , & Stanton, R.D. (2005). Speeded classification in a probabilistic category structure: contrasting exemplar-retrieval, decision-boundary, and prototype models. Journal of Experimental Psychology: Human Perception and Performance, 31, 608–629.
Nosofsky, R. M. , (2006). Speeded old-new recognition of multidimensional perceptual stimuli: modeling performance at the individual-participant and individual-item levels. Journal of Experimental Psychology: Human Perception and Performance, 32, 314–334.
Nosofsky, R.M. , & Zaki, S.R. (1998). Dissociations between categorization and recognition in amnesic and normal individuals: an exemplar-based interpretation. Psychological Science, 9, 247–255.
Nosofsky, R. M. , (2002). Exemplar and prototype models revisited: response strategies, selective attention, and stimulus generalization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 924–940.
Palmeri, T.J. (1997). Exemplar similarity and the development of automaticity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 324–354.
Palmeri, T.J. , & Flanery, M.A. (2002). Memory systems and perceptual categorization. In B.H. Ross (ed.), The Psychology of Learning and Motivation: Advances in Research and Theory (pp. 141–189). San Diego, CA: Academic Press.
Pothos, E.M. , & Bailey, T.M. (2009). Predicting category intuitiveness with the rational model, the simplicity model, and the Generalized Context Model. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 1062–1080.
Rehder, B. , & Hoffman, A.B. (2005). Thirty-something categorization results explained: selective attention, eyetracking, and models of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 811–829.
Shepard, R.N. (1957). Stimulus and response generalization: a stochastic model relating generalization to distance in psychological space. Psychometrika, 22, 325–345.
Shepard, R. N. (1958). Stimulus and response generalization: tests of a model relating generalization to distance in psychological space. Journal of Experimental Psychology, 55, 509–523.
Shepard, R. N. (1964). Attention and the metric structure of the stimulus space. Journal of Mathematical Psychology, 1, 54–87.
Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317–1323.
Shepard, R.N. , Hovland, C.I. , & Jenkins, H.M. (1961). Learning and memorization of classifications. Psychological Monographs, 75 (13), Whole No. 517.
Shin, H.J. , & Nosofsky, R.M. (1992). Similarity-scaling studies of dot-pattern classification and recognition. Journal of Experimental Psychology: General, 121, 278–304.
Stanton, R.D. , Nosofsky, R.M. , & Zaki, S. R. (2002). Comparisons between exemplar similarity and mixed prototype models using a linearly separable category structure. Memory & Cognition, 30, 934–944.
Stewart, N. , & Brown, G.D.A. (2005). Similarity and dissimilarity as evidence in perceptual categorization. Journal of Mathematical Psychology, 49, 403–409.
Vanpaemel, W. (2009). BayesGCM: software for Bayesian inference with the Generalized Context Model. Behavior Research Methods, 41, 1111–1120.
Vanpaemel, W. , & Storms, G. (2008). In search of abstraction: the varying abstraction model of categorization. Psychonomic Bulletin & Review, 15, 732–749.
Viken, R.J. , Treat, T.A. , Nosofsky, R.M. , McFall, R.M. , & Palmeri, T.J. (2002). Modeling individual differences in perceptual and attentional processes related to bulimic symptoms. Journal of Abnormal Psychology, 111, 598–609.
Zaki, S.R. , & Nosofsky, R.M. (2004). False prototype enhancement effects in dot pattern categorization. Memory & Cognition, 32, 390–398.
Zaki, S.R. (2007). A high-distortion enhancement effect in the prototype learning paradigm: dramatic effects of category learning during test. Memory & Cognition, 35, 2088–2096.
Zaki, S.R. , Nosofsky, R.M. , Stanton, R.D. , & Cohen, A.L. (2003). Prototype and exemplar accounts of category learning and attentional allocation: a reassessment. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 1160–1173.

Reference Title: REFERENCES

Reference Type: reference-list

Ashby, F.G. , & Maddox, W.T. (2005). Human category learning. Annual Review of Psychology, 56, 149–178.
Blair, M. , & Homa, D. (2001). Expanding the search for a linear separability constraint on category learning. Memory & Cognition, 29, 1153–1164.
Blair, M. , (2004). As easy to memorize as they are to classify: the 5-4 categories and the category advantage. Memory & Cognition, 31, 1293–1301.
Chin-Parker, S. , & Ross, B.H. (2004). Diagnosticity and prototypicality in category learning: a comparison of inference learning and classification learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 216–226.
Homa, D. , Cross, J. , Cornell, D. , & Shwartz, S. (1973). Prototype abstraction and classification of new instances as a function of number of instances defining the prototype. Journal of Experimental Psychology, 101, 116–122.
Homa, D. , & Cultice, J.C. (1984). Role of feedback, category size, and stimulus distortion on the acquisition and utilization of ill-defined categories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 83–94.
Knowlton, B.J. , & Squire, L.R. (1993). The learning of categories: parallel brain systems for item memory and category knowledge. Science, 262, 1747–1749.
Love, B.C. , & Gureckis, T.M. (2007). Models in search of a brain. Cognitive, Affective, & Behavioral Neuroscience, 7, 90–108.
Love, B.C. , Medin, D.L. , & Gureckis, T.M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111, 309–332.
Medin, D.L. , & Schaffer, M.M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.
Medin, D.L. , & Schwanenflugel, P.J. (1981). Linear separability in classification learning. Journal of Experimental Psychology: Human Learning and Memory, 7, 355–368.
Minda, J.P. , & Ross, B.H. (2004). Learning categories by making predictions: an investigation of indirect category learning. Memory & Cognition, 32, 1355–1368.
Minda, J.P. , & Smith, J.D. (2001). Prototypes in category learning: the effects of category size, category structure, and stimulus complexity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 775–799.
Minda, J.P. , & Smith, J.D. , (2002). Comparing prototype-based and exemplar-based accounts of category learning and attentional allocation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 275–292.
Myung, I.J. (2000). The importance of complexity in model selection. Journal of Mathematical Psychology, 44, 190–204.
Nosofsky, R.M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39–57.
Nosofsky, R. M. (1987). Attention and learning processes in the identification and categorization of integral stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 87–108.
Nosofsky, R. M. (1988). Exemplar-based accounts of relations between classification, recognition, and typicality. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 700–708.
Nosofsky, R. M. (1992). Exemplars, prototypes, and similarity rules. In A.F. Healy , S.M. Kosslyn , & R.M. Shiffrin (eds.), From Learning Theory to Connectionist Theory: Essays in Honor of William K. Estes (Vol. 1, pp. 149–167). Hillsdale, NJ: Lawrence Erlbaum.
Nosofsky, R.M. , & Zaki, S.R. (2002). Exemplar and prototype models revisited: response strategies, selective attention, and stimulus generalization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 924–940.
Posner, M.I. , & Keele, S.W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353–363.
Pothos, E. , & Chater, N. (2002). A simplicity principle in unsupervised human categorization. Cognitive Science, 26, 303–343.
Reber, P. , Stark, C. , & Squire, L. (1998a). Contrasting cortical activity associated with category memory and recognition memory. Learning & Memory, 5, 420–428.
Reber, P. , Stark, C. , & Squire, L. , (1998b). Cortical areas supporting category learning identified using functional MRI. Proceedings of the National Academy of Sciences, 95, 747– 750.
Rehder, B. , & Murphy, G.L. (2003). A knowledge-resonance (KRES) model of category learning. Psychonomic Bulletin & Review, 10, 759–784.
Rosch, E. , & Mervis, C.B. (1975). Family resemblances: studies in the internal structure of categories. Cognitive Psychology, 7, 573–605.
Rosch, E. , Mervis, C.B. , Gray, W. , Johnson, D. , & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382–439.
Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317–1323.
Smith, E.E. , & Medin, D.L. (1981). Categories and Concepts. Cambridge, MA: Harvard University Press.
Smith, J.D. (2005). Wanted: a new psychology of exemplars. Canadian Journal of Experimental Psychology, 2003 Festschrift for Lee R. Brooks, 59, 47–53.
Smith, J.D. , & Minda, J.P. (1998). Prototypes in the mist: the early epochs of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 1411–1436.
Smith, J. D. , (2000). Thirty categorization results in search of a model. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 3–27.
Smith, J. D. , (2001). Journey to the center of the category: the dissociation in amnesia between categorization and recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 4, 501–516.
Smith, J.D. , Murray, J. , Morgan, J. , & Minda, J.P. (1997). Straight talk about linear separability. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 659–680.
Smith, J.D. , Redford, J.S. , & Haas, S.M. (2008). Prototype abstraction by monkeys (Macaca mulatta). Journal of Experimental Psychology: General, 137, 390–401.
Wittgenstein, L. (1958/2001). Philosophical Investigations. New York: Blackwell.
Yamauchi, T. , & Markman, A. B. (1998). Category learning by inference and classification. Journal of Memory & Language, 39, 124–148.
Zeithamova, D. , Maddox, W.T. , & Schnyer, D.M. (2008). Dissociable prototype learning systems: evidence from brain imaging and behavior. Journal of Neuroscience, 28, 13194–13201.

Reference Title: REFERENCES

Reference Type: reference-list

Allen, S.W. , & Brooks, L.R. (1991). Specializing the operation of an explicit rule. Journal of Experimental Psychology: General, 120, 3–19.
Arbuthnott, G.W. , Ingham, C.A. , & Wickens, J.R. (2000). Dopamine and synaptic plasticity in the neostriatum. Journal of Anatomy, 196, 587–596.
Ashby, F.G. , Alfonso-Reese, L.A. , Turken, A.U. , & Waldron, E.M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105, 442–481.
Ashby, F.G. , & Ell, S.W. (2002). Single versus multiple systems of category learning: reply to Nosofsky and Kruschke (2002). Psychonomic Bulletin & Review, 9, 175–180.
Ashby, F.G. , Ell, S.W. , Valentin, V. , & Casale, M.B. (2005). FROST: a distributed neurocomputational model of working memory maintenance. Journal of Cognitive Neuroscience, 17, 1728–1743.
Ashby, F.G. , Ell, S.W. , & Waldron, E.M. (2003). Procedural learning in perceptual categorization. Memory & Cognition, 31, 1114–1125.
Ashby, F.G. , & Ennis, J.M. (2006). The role of the basal ganglia in category learning. The Psychology of Learning and Motivation, 47, 1–36.
Ashby, F.G. , Ennis, J.M. , & Spiering, B.J. (2007). A neurobiological theory of automaticity in perceptual categorization. Psychological Review, 114, 632–656.
Ashby, F.G. , & Gott, R.E. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 33–53.
Ashby, F.G. , & Maddox, W.T. (2005). Human category learning. Annual Review of Psychology, 56, 149–178.
Ashby, F.G. , Noble, S. , Filoteo, J.V. , Waldron, E.M. , & Ell, S.W. (2003). Category learning deficits in Parkinson's disease. Neuropsychology, 17, 115–124.
Ashby, F.G. , & O'Brien, J.B. (2005). Category learning and multiple memory systems. Trends in Cognitive Sciences, 9, 83–89.
Ashby, F.G. , & Valentin, V.V. (2005). Multiple systems of perceptual category learning: theory and cognitive tests. In H. Cohen & C. Lefebvre (eds.), Categorization in Cognitive Science. New York: Elsevier.
Ashby, F.G. , & Waldron, E.M. (1999). On the nature of implicit categorization. Psychonomic Bulletin & Review, 6, 363–378.
Ashby, F.G. , & Waldschmidt, J.G. (2008). Fitting computational models to fMRI data. Behavior Research Methods, 40, 713–721.
Bayer, H.M. , & Glimcher, P.W. (2005). Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129–141.
Brooks, L. (1978). Nonanalytic Concept Formation and Memory for Instances. Hillsdale, NJ: Erlbaum.
Calabresi, P. , Pisani, A. , Mercuri, N.B. , & Bernardi, G. (1992). Long-term potentiation in the striatum is unmasked by removing the voltage-dependent magnesium block of NMDA receptor channels. European Journal of Neuroscience, 4, 929–935.
Calabresi, P. , Pisani, A. , Mercuri, N. B. , (1996). The corticostriatal projection: from synaptic plasticity to dysfunctions of the basal ganglia. Trends in Neurosciences, 19, 19–24.
DeGutis, J. , & D'Esposito, M. (2007). Distinct mechanisms in visual category learning. Cognitive, Affective, & Behavioral Neuroscience, 7 (3), 251–259.
Erickson, M.A. , & Kruschke, J.K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 127, 107–140.
Filoteo, J. V. , & Maddox, W.T. (2007). Category learning in Parkinson's disease. In M.K. Sun (ed.), Research Progress in Alzheimer's Disease and Dementia (pp. 339–365). Nova Sciences Publishers.
Filoteo, J. V. , Maddox, W.T. , Simmons, A.N. , Ing, A.D. , Cagigas, X.E. , Matthews, S. , et al. (2005). Cortical and subcortical brain regions involved in rule-based category learning. NeuroReport, 16 (2), 111–115.
Gotham, A. M. , Brown, R.G. , & Marsden, C.D. (1988). ‘Frontal’ cognitive function in patients with Parkinson's disease ‘ON’ and ‘OFF’ Levodopa. Brain, 111, 299–321.
Hazeltine, E. , & Ivry, R. (2002). Motor skill. In V. Ramachandran (ed.), Encyclopedia of the Human Brain (pp. 183–200). San Diego, CA: Academic Press.
Kincaid, A.E. , Zheng, T. , & Wilson, C.J. (1998). Connectivity and convergence of single corticostriatal axons. Journal of Neuroscience, 18, 4722–4731.
Kruschke, J.K. (1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological Review, 99, 22–44.
Lees, A.J. , & Smith, F. (1983). Cognitive deficits in the early stages of Parkinson's disease. Brain, 106, 257–270.
Lisman, J. , Schulman, H. , & Cline, H. (2002). The molecular basis of CaMKII function in synaptic and behavioural memory. Nature Reviews Neuroscience, 3, 175–190.
Love, B.C. , Medin, D.L. , & Gureckis, T.M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111 (2), 309–332.
Maddox, W.T. , & Ashby, F.G. (1993). Comparing decision bound and exemplar models of categorization. Perception & Psychophysics, 53, 49–70.
Maddox, W. T. , (2004). Dissociating explicit and procedural-learning based systems of perceptual category learning. Behavioural Processes, 66 (3), 309–332.
Maddox, W.T. , Ashby, F.G. , & Bohil, C.J. (2003). Delayed feedback effects on rule-based and information-integration category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 650–662.
Maddox, W.T. , Bohil, C.J. , & Ing, A.D. (2004). Evidence for a procedural-learning-based system in perceptual category learning. Psychonomic Bulletin & Review, 11 (5), 945–952.
Maddox, W.T. , & Filoteo, J.V. (2005). The neuropsychology of perceptual category learning. In H. Cohen & C. Lefebvre (eds.), Handbook of Categorization in Cognitive Science (pp. 573–599). Amsterdam: Elsevier.
Maddox, W. T. , (2007). Modeling visual attention and category learning in amnesiacs, striatal-damaged patients and normal aging. In R.W.J. Neufeld (ed.), Advances in Clinical Cognitive Science: Formal Modeling and Assessment of Processes and Symptoms (pp. 113–146). Washington DC: American Psychological Association.
Maddox, W.T. , Glass, B.D. , O'Brien, J.B. , Filoteo, J.V. , & Ashby, F.G. (2010). Category label and response location shifts in category learning. Psychological Research, 74, 219–236.
Maddox, W.T. , & Ing, A.D. (2005). Delayed feedback disrupts the procedural-learning system but not the hypothesis-testing system in perceptual category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 100–107.
Markman, A.B. , & Ross, B.H. (2003). Category use and category learning. Psychological Bulletin, 129 (4), 592–613.
McKinley, S.C. , & Nosofsky, R.M. (1995). Investigations of exemplar and decision bound models in large, ill-defined category structures. Journal of Experimental Psychology: Human Perception and Performance, 21, 128–148.
Medin, D.L. , & Schaffer, M.M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.
Nomura, E.M. , Maddox, W.T. , Filoteo, J.V. , Ing, A.D. , Gitelman, D.R. , Parrish, T.B. , et al. (2007). Neural correlates of rule-based and information-integration visual category learning. Cerebral Cortex, 17 (1), 37–43.
Nomura, E.M. , & Reber, P.J. (2008). A review of medial temporal lobe and caudate contributions to visual category learning. Neuroscience and Biobehavioral Reviews, 32 (2), 279–291.
Nosofsky, R.M. , & Kruschke, J.K. (2002). Single-system models and interference in category learning: commentary on Waldron and Ashby (2001). Psychonomic Bulletin & Review, 9, 169–174.
Nosofsky, R.M. , Palmeri, T.J. , & McKinley, S.C. (1994). A rule-plus-exception model of classification learning. Psychological Review, 101, 53–79.
Posner, M.I. , & Keele, S.W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353–363.
Price, A. , Filoteo, J.V. , & Maddox, W.T. (2009). Rule-based category learning in patients with Parkinson's disease. Neuropsychologia, 47 (5), 1213– 1226.
Reber, P.J. , Gitelman, D.R. , Parrish, T.B. , & Mesulam, M.M. (2003). Dissociating explicit and implicit category knowledge with fMRI. Journal of Cognitive Neuroscience, 15 (4), 574–583.
Regehr, G. , & Brooks, L.R. (1993). Perceptual manifestations of an analytic structure: the priority of holistic individuation. Journal of Experimental Psychology: General, 122 (1), 92–114.
Rescorla, R.A. , & Wagner, A. R. (1972). A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. In A.H. Black & W.F. Prokasy (eds.), Classical Conditioning II: Current Research and Theory (pp. 64–99). New York: Appleton-Century-Crofts.
Reynolds, J.N.J. , & Wickens, J.R. (2002). Dopamine-dependent plasticity of corticostriatal synapses. Neural Networks, 15, 507–521.
Riesenhuber, M. , & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2, 1019–1025.
Schultz, W. , Dayan, P. , & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275, 1593–1599.
Seger, C.A. (2008). How do the basal ganglia contribute to categorization? Their roles in generalization, response selection, and learning via feedback. Neuroscience and Biobehavioral Review, 32 (2), 265–278.
Seger, C.A. , & Cincotta, C.M. (2005). The roles of the caudate nucleus in human classification learning. Journal of Neuroscience, 25 (11), 2941– 2951.
Seger, C. A. , (2006). Dynamics of frontal, striatal, and hippocampal systems during rule learning. Cerebral Cortex, 16 (11), 1546–1555.
Smiley, J.F. , Levey, A.I. , Ciliax, B.J. , & Goldman-Rakic, P.S. (1994). D1 dopamine receptor immunoreactivity in human and monkey cerebral cortex: predominant and extrasynaptic localization in dendritic spines. Proceedings of the National Academy of Sciences, 91, 5720–5724.
Smith, J.D. , Beran, M. J. , Crossley, M. , Boomer, J. , & Ashby, F.G. (2010). Implicit and explicit category learning by macaques (Macaca mulatta) and humans (Homo sapiens). Journal of Experimental Psychology: Animal Behavior Processes, 36, 54–65.
Smith, J.D. , & Minda, J.P. (1998). Prototypes in the mist: the early epochs of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 1411–1436.
Tobler, P.N. , Dickinson, A. , & Schultz, W. (2003). Coding of predicted reward omission by dopamine neurons in a conditioned inhibition paradigm. Journal of Neuroscience, 23, 10402–10410.
Waldron, E.M. , & Ashby, F. G. (2001). The effects of concurrent task interference on category learning: evidence for multiple category learning systems. Psychonomic Bulletin & Review, 8, 168–176.
Willingham, D.B. (1998). A neuropsychological theory of motor skill learning. Psychological Review, 105, 558–584.
Willingham, D.B. , Nissen, M.J. , & Bullemer, P. (1989). On the development of procedural knowledge. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15 (6), 1047–1060.
Zeithamova, D. , & Maddox, W.T. (2006). Dual task interference in perceptual category learning. Memory & Cognition, 34 (2), 387–398.

Reference Title: REFERENCES

Reference Type: reference-list

Anderson, J.R. (1991). The adaptive nature of human categorization. Psychological Review, 98 (3), 409–426.
Ashby, F.G. , & Alfonso-Reese, L.A. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39 (2), 216–233.
Barsalou, L. (1993). Flexibility, structure, and linguistic vagary in concepts: manifestations of a compositional system of perceptual symbols. In A.F. Collins , S.E. Gathercole , M.A. Conway , & P.E. Morris (eds.), Theories of Memory. Hillsdale, NJ: Lawrence Erlbaum Associates.
Brown, R. (1958). How shall a thing be called? Psychological Review, 65, 14–21.
Carey, S. (1985). Conceptual Change in Childhood. Cambridge, 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 Behavior, 8, 240–247.
Gelman, R. , & Williams, E.M. (1998). Enabling constraints for cognitive development and learning: a domain-specific epigenetic theory. In W. Damon (ed.), Handbook of Child Psychology, Vol. II: Cognition, Perception and Development (pp. 575–630). New York: John Wiley and Sons.
Gelman, S. , & Coley, J.D. (1990). The importance of knowing a dodo is a bird: categories and inferences in 2-year-old children. Developmental Psychology, 26, 796–804.
Gelman, S.A. , & Markman, E.M. (1986). Categories and induction in young children. Cognition, 23, 183–209.
Gelman, S.A. , & Wellman, H.M. (1991). Insides and essences: early understandings of the nonobvious. Cognition, 38, 213–244.
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.L. , Sanborn, A.N. , Canini, D.J. , & Navarro, D.J. (2008). Categorization as nonparametric Bayesian density estimation. In N. Chater & M. Oaksford (eds.), The Probabilistic Mind: Prospects for a Bayesian Cognitive Science. Oxford: Oxford University Press.
Hinton, G.E. (1981). Implementing semantic networks in parallel hardware. In G.E. Hinton & J.A. Anderson (eds.), Parallel Models of Associative Memory (pp. 161–187). Hillsdale, NJ: Erlbaum.
Hinton, G. E. (1986). Learning distributed representations of concepts. In Proceedings of the Cognitive Science Society (pp. 1–12). Hillsdale, NJ: LEA.
Hinton, G.E. , & McClelland, J.L. (1988). Learning representations by recirculation. In D.Z. Anderson (ed.), Neural Information Processing Systems (pp. 358–366). New York: American Institute of Physics.
Jolicoeur, P. , Gluck, M. , & Kosslyn, S.M. (1984). Pictures and names: making the connection. Cognitive Psychology, 19, 31–53.
Jones, S.S. , Smith, L.B. , & Landau, B. (1991). Object properties and knowledge in early lexical learning. Child Development, 62 (3), 499–516.
Keil, F. (1979). Semantic and Conceptual Development: an Ontological Perspective. Cambridge, MA: Harvard University Press.
Kemp, C. , & Tenenbaum, J.B. (2008). The discovery of structural form. Proceedings of the National Academy of Sciences, 105 (31), 10687–10692.
Kruschke, J.K. (1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological Review, 99 (1), 22–44.
Love, B.C. , Medin, D.L. , & Gureckis, T.M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111 (2), 3009–3332.
Macario, J.F. (1991). Young children's use of color in classification: foods and canonically colored objects. Cognitive Development, 6, 17–46.
Mandler, J.M. (2000). Perceptual and conceptual processes in infancy. Journal of Cognition and Development, 1, 3–36.
Mandler, J.M. , & Bauer, P.J. (1988). The cradle of categorization: is the basic level basic? Cognitive Development, 3, 247–264.
Mandler, J.M. , & McDonough, L. (1996). Drinking and driving don't mix: inductive generalization in infancy. Cognition, 59, 307–355.
Massey, C.M. , & Gelman, R. (1988). Preschooler's ability to decide whether a photographed unfamiliar object can move by itself. Developmental Psychology, 24 (3), 307–317.
McClelland, J.L. , Botvinick, M.B. , Noelle, D. , Plaut, D.C. , Rogers, T.T. , Seidenberg, M. , & Smith, L. (2010). Letting structure emerge: connectionist and dynamical systems approaches to cognition. Trends in Cognitive Sciences, 14 (8), 348–356.
McClelland, J.L. , McNaughton, B.L. , & O'Reilly, R.C. (1995). Why there are complementary learning-systems in the hippocampus and neocortex – insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102 (3), 419–457.
McClelland, J.L. , & Rumelhart, D.E. (1986). A distributed model of human learning and memory. In J.L. McClelland , D.E. Rumelhart & the PDP Research Group (eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 2, pp. 170–215). Cambridge, MA: MIT Press.
McClelland, J.L. , & Rumelhart, D.E. (1988). Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises. Cambridge, MA: MIT Press.
Medin, D.L. , & Schaffer, M.M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.
Mervis, C.B. (1987). Child basic object categories and early lexical development. In U. Neisser (ed.), Concepts and Conceptual Development: Ecological and Intellectual Factors in Categorization. Cambridge: Cambridge University Press.
Mervis, C.A. , & Crisafi, M.A. (1982). Order of acquisition of subordinate-, basic-, and superordinate-level categories. Child Development, 53 (1), 258–266.
Mervis, C.B. , & Rosch, E. (1981). Categorization of natural objects. Annual Review of Psychology, 32, 89–115.
Movellan, J.R. , & McClelland, J.L. (1993). Learning continuous probability distributions with symmetric diffusion networks. Cognitive Science, 17, 463–496.
Murphy, G.L. , & Brownell, H.H. (1985). Category differentiation in object recognition: typicality constraints on the basic category advantage. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11 (1), 70–84.
Murphy, G.L. , & Lassaline, M.E. (1997). Hierarchical structure in concepts and the basic level of categorization. In K. Lamberts & D. Shanks (eds.), Knowledge, Concepts and Categories (pp. 93–131). Hove: Psychology Press.
Murphy, G.L. , & Medin, D.L. (1985). The role of theories in conceptual coherence. Psychological Review, 92, 289–316.
Murphy, G.L. , & Smith, E.E. (1982). Basic level superiority in picture categorization. Journal of Verbal Learning and Verbal Behavior, 21, 1–20.
Nosofsky, R. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 104–110.
Nosofsky, R.M. (1986). Attention, similarity and the identification-categorization relationship. Journal of Experimental Psychology: Learning, Memory, and Cognition, 115 (1), 39–57.
O'Reilly, R.C. (1996). Biologically plausible error-driven learning using local activation differences: the generalized recirculation algorithm. Neural Computation, 8, 895–938.
Palmeri, T.J. (1999). Learning categories at different hierarchical levels: a comparison of category learning models. Psychonomic Bulletin & Review, 6, 495–503.
Patterson, K. , & Hodges, J. (2000). Semantic dementia: one window on the structure and organisation of semantic memory. In J. Cermak (ed.), Handbook of Neuropsychology, Vol. 2: Memory and its Disorders (pp. 313–333). Amsterdam: Elsevier Science.
Patterson, K. , Nestor, P.J. , & Rogers, T. (2007). Where do you know what you know? The representation of semantic knowledge in the human brain. Nature Reviews Neuroscience, 8, 976–987.
Pauen, S. (2002a). Evidence for knowledge-based category discrimination in infancy. Child Development, 73 (4), 1016–1033.
Pauen, S. (2002b). The global-to-basic shift in infants' categorical thinking: first evidence from a longitudinal study. International Journal of Behavioural Development, 26 (6), 492–499.
Pothos, E.M. , & Chater, N. (2002). A simplicity principle in unsupervised human categorization. Cognitive Science, 26, 303–343.
Rogers, T.T. , Lambon Ralph, M.A. , Garrard, P. , Bozeat, S. , McClelland, J.L. , Hodges, J.R. , et al. (2004). The structure and deterioration of semantic memory: a computational and neuropsychological investigation. Psychological Review, 111 (1), 205–235.
Rogers, T.T. , & McClelland, J.L. (2004). Semantic Cognition: A Parallel Distributed Processing Approach. Cambridge, MA: MIT Press.
Rogers, T.T. , & McClelland, J.L. (2008). A simple model from a powerful framework that spans levels of analysis. Behavioral and Brain Sciences, 31, 729–749.
Rogers, T.T. , & Patterson, K. (2007). Object categorization: reversals and explanations of the basic-level advantage. Journal of Experimental Psychology: General, 136 (3), 451–469.
Rosch, E. (1978). Principles of categorization. In E. Rosch & B. Lloyd (eds.), Cognition and Categorization. Hillsdale, NJ: Lawrence Erlbaum Associates.
Rosch, E. , Mervis, C.B. , Gray, W. , Johnson, D. , & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382–439.
Rumelhart, D.E. (1990). Brain style computation: learning and generalization. In S.F. Zornetzer , J.C. Davis , & C. Lau (eds.), An Introduction to Neural and Electronic Networks (pp. 405–420). San Diego, CA: Academic Press.
Rumelhart, D.E. , Durbin, R. , Golden, R. , & Chauvin, Y. (1995). Backpropagation: the basic theory. In Y. Chauvin & D.E. Rumelhart (eds.), Back-Propagation: Theory, Architectures, and Applications (pp. 1–34). Hillsdale, NJ: Erlbaum.
Rumelhart, D.E. , Hinton, G.E. , & Williams, R.J. (1986a). Learning representations by back-propagating errors. Nature, 323 (9), 533–536.
Rumelhart, D.E. , McClelland, J.L. , & the PDP Research Group (1986b). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. I: Foundations & Vol. II: Psychological and Biological Models. Cambridge, MA: MIT Press.
Rumelhart, D.E. , & Todd, P.M. (1993). Learning and connectionist representations. In D.E. Meyer & S. Kornblum (eds.), Attention and Performance XIV: Synergies in Experimental Psychology, Artificial Intelligence, and Cognitive Neuroscience (pp. 3–30). Cambridge, MA: MIT Press.
Tanaka, J. , & Taylor, M. (1991). Object categories and expertise: is the basic level in the eye of the beholder? Cognitive Psychology, 23, 457–482.
Tenenbaum, J.B. , & Griffiths, T.L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24, 629–640.
Tenenbaum, J.B. , Griffiths, T.L. , & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences, 10 (7), 309–318.
Verheyen, S. , Ameel, E. , Rogers, T.T. , & Storms, G. (2008). Learning a hierarchical organization of categories. Paper presented at the Proceedings of the Cognitive Science Society, Amsterdam, the Netherlands.

Reference Title: REFERENCES

Reference Type: reference-list

Anderson, J.R. (1991). The adaptive nature of human categorization. Psychological Review, 98 (3), 409–429.
Ashby, F.G. , Alfonso-Reese, L.A. , Turken, A.U. , & Waldron, E.M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105 (3), 442–481.
Atkinson, R.C. , & Estes, W.K. (1963). Stimulus sampling theory. In R.D. Luce , R.R. Bush , & E. Galanter (eds.), Handbook of Mathematical Psychology. New York: Wiley.
Chater, N. , Tenenbaum, J. B. , & Yuille, A. (eds.) (2006). Special issue: probabilistic models of cognition. Trends in Cognitive Sciences, 10 (7), 287–344.
Courville, A.C. , Daw, N.D. , & Touretzky, D.S. (2006). Bayesian theories of conditioning in a changing world. Trends in Cognitive Sciences, 10 (7), 294–300.
Dayan, P. , & Kakade, S. (2001). Explaining away in weight space. In T. Leen , T. Dietterich , & V. Tresp (eds.), Advances in Neural Information Processing Systems (Vol. 13, pp. 451–457). Cambridge, MA: MIT Press.
Denton, S.E. , & Kruschke, J.K. (2006). Attention and salience in associative blocking. Learning & Behavior, 34 (3), 285–304.
Denton, S.E. , Kruschke, J.K. , & Erickson, M.A. (2008). Rule-based extrapolation: a continuing challenge for exemplar models. Psychonomic Bulletin & Review, 15 (4), 780–786.
Dickinson, A. , & Burke, J. (1996). Within-compound associations mediate the retrospective revaluation of causality judgements. Quarterly Journal of Experimental Psychology: Comparative & Physiological Psychology, 49B, 60–80.
Erickson, M.A. (2008). Executive attention and task switching in category learning: evidence for stimulus-dependent representation. Memory & Cognition, 36 (4), 749–761.
Erickson, M.A. , & Kruschke, J.K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127 (2), 107–140.
Erickson, M. A. , (2002). Rule-based extrapolation in perceptual categorization. Psychonomic Bulletin & Review, 9 (1), 160–168.
Estes, W. K. (1962). Learning theory. Annual Review of Psychology, 13 (1), 107–144.
George, D.N. , & Pearce, J.M. (1999). Acquired distinctiveness is controlled by stimulus relevance not correlation with reward. Journal of Experimental Psychology: Animal Behavior Processes, 25 (3), 363–373.
Goodman, N.D. , Tenenbaum, J.B. , Feldman, J. , & Griffiths, T.L. (2008). A rational analysis of rule-based concept learning. Cognitive Science, 32 (1), 108–154.
Hall, G. , Mackintosh, N.J. , Goodall, G. , & Dal Martello, M. (1977). Loss of control by a less valid or by a less salient stimulus compounded with a better predictor of reinforcement. Learning and Motivation, 8, 145–158.
Harris, J.A. (2006). Elemental representations of stimuli in associative learning. Psychological Review, 113 (3), 584–605.
Jacobs, R.A. , Jordan, M.I. , & Barto, A. (1991). Task decomposition through competition in a modular connectionist architecture: the what and where vision tasks. Cognitive Science, 15, 219–250.
Jacobs, R.A. , Jordan, M.I. , Nowlan, S.J. , & Hinton, G.E. (1991). Adaptive mixtures of local experts. Neural Computation, 3, 79–87.
Kalish, M.L. (2001). An inverse base rate effect with continuously valued stimuli. Memory & Cognition, 29 (4), 587–597.
Kalish, M.L. , & Kruschke, J.K. (2000). The role of attention shifts in the categorization of continuous dimensioned stimuli. Psychological Research, 64, 105–116.
Kalish, M.L. , Lewandowsky, S. , & Kruschke, J.K. (2004). Population of linear experts: knowledge partitioning and function learning. Psychological Review, 111 (4), 1072–1099.
Kruschke, J.K. (1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological Review, 99, 22–44.
Kruschke, J. K. (1993). Human category learning: implications for backpropagation models. Connection Science, 5, 3–36.
Kruschke, J. K. (1996a). Base rates in category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 3–26.
Kruschke, J. K. (1996b). Dimensional relevance shifts in category learning. Connection Science, 8, 201–223.
Kruschke, J. K. (2001a). The inverse base rate effect is not explained by eliminative inference. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 1385–1400.
Kruschke, J. K. (2001b). Toward a unified model of attention in associative learning. Journal of Mathematical Psychology, 45, 812–863.
Kruschke, J. K. (2003a). Attentionally modulated exemplars and exemplar mediated attention. Invited talk at the Seventh International Conference on Cognitive and Neural Systems, Boston University, May 28–31.
Kruschke, J. K. (2003b). Attentionally modulated exemplars and exemplar mediated attention. Keynote Address to the Associative Learning Conference, Gregynog (University of Cardiff) Wales, April 15–17.
Kruschke, J. K. (2003c). Attentional theory is a viable explanation of the inverse base rate effect: a reply to Winman, Wennerholm, and Juslin (2003). Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 1396– 1400.
Kruschke, J. K. (2005). Learning involves attention. In G. Houghton (ed.), Connectionist Models in Cognitive Psychology (pp. 113–140). Hove: Psychology Press.
Kruschke, J. K. (2006a). Locally Bayesian learning. In R. Sun (ed.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 453–458). Mahwah, NJ: Erlbaum.
Kruschke, J. K. (2006b). Locally Bayesian learning with applications to retrospective revaluation and highlighting. Psychological Review, 113 (4), 677–699.
Kruschke, J. K. (2008). Bayesian approaches to associative learning: from passive to active learning. Learning & Behavior, 36 (3), 210–226.
Kruschke, J. K. (2010). Highlighting: a canonical experiment. In B. Ross (ed.), The Psychology of Learning and Motivation, 51, 153–185.
Kruschke, J.K. , & Blair, N. J. (2000). Blocking and backward blocking involve learned inattention. Psychonomic Bulletin & Review, 7, 636–645.
Kruschke, J.K. , & Denton, S.E. (2010). Backward blocking of relevance-indicating cues: evidence for locally Bayesian learning. In C.J. Mitchell & M.E. Le Pelley (eds.), Attention and Associative Learning. New York: Oxford University Press.
Kruschke, J.K. , & Erickson, M.A. (1994). Learning of rules that have high-frequency exceptions: New empirical data and a hybrid connectionist model. In The Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society (pp. 514–519). Hillsdale, NJ: Erlbaum.
Kruschke, J.K. , & Hullinger, R.A. (2010). The evolution of learned attention. In N. Schmajuk (ed.), Computational Models of Conditioning. Cambridge: Cambridge University Press.
Kruschke, J.K. , & Johansen, M.K. (1999). A model of probabilistic category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25 (5), 1083–1119.
Kruschke, J.K. , Kappenman, E.S. , & Hetrick, W.P. (2005). Eye gaze and individual differences consistent with learned attention in associative blocking and highlighting. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 830–845.
Larrauri, J.A. , & Schmajuk, N.A. (2008). Attentional, associative, and configural mechanisms in extinction. Psychological Review, 115 (3), 640–675.
Le Pelley, M.E. , & McLaren, I.P.L. (2003). Learned associability and associative change in human causal learning. The Quarterly Journal of Experimental Psychology Section B, 56 (1), 68–79.
Le Pelley, M.E. , Oakeshott, S.M. , Wills, A.J. , & McLaren, I.P.L. (2005). The outcome specificity of learned predictiveness effects: parallels between human causal learning and animal conditioning. Journal of Experimental Psychology: Animal Behavior Processes, 31 (2), 226–236.
Lewandowsky, S. , Roberts, L. , & Yang, L.X. (2006). Knowledge partitioning in categorization: boundary conditions. Memory & Cognition, 34 (8), 1676–1688.
Little, D.R. , & Lewandowsky, S. (2009). Beyond non-utilization: irrelevant cues can gate learning in probabilistic categorization. Journal of Experimental Psychology: Human Perception and Performance, 35 (2), 530–550.
Love, B.C. , Medin, D.L. , & Gureckis, T.M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111 (2), 309–332.
Mackintosh, N.J. (1975). A theory of attention: variations in the associability of stimuli with reinforcement. Psychological Review, 82, 276–298.
McLaren, I.P.L. , & Mackintosh, N.J. (2000). An elemental model of associative learning: I. Latent inhibition and perceptual learning. Animal Learning and Behavior, 28 (3), 211–246.
McLaren, I.P.L. , & Mackintosh, N.J. (2002). An elemental model of associative learning: II. Generalization and discrimination. Animal Learning and Behavior, 30, 177–200.
Medin, D.L. , & Edelson, S.M. (1988). Problem structure and the use of base-rate information from experience. Journal of Experimental Psychology: General, 117, 68–85.
Medin, D.L. , & Schaffer, M.M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.
Nosofsky, R.M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology, 115, 39–57.
Nosofsky, R.M. , Gluck, M.A. , Palmeri, T.J. , McKinley, S.C. , & Glauthier, P. (1994). Comparing models of rule-based classification learning: a replication of Shepard, Hovland, and Jenkins (1961). Memory & Cognition, 22, 352–369.
Nosofsky, R.M. , & Johansen, M.K. (2000). Exemplar-based accounts of ‘multiple-system’ phenomena in perceptual categorization. Psychonomic Bulletin & Review, 7 (3), 375–402.
Oswald, C.J.P. , Yee, B.K. , Rawlins, J.N.P. , Bannerman, D.B. , Good, M. , & Honey, R.C. (2001). Involvement of the entorhinal cortex in a process of attentional modulation: evidence from a novel variant of an IDS/EDS procedure. Behavioral Neuroscience, 115 (4), 841–849.
Pearce, J.M. (1994). Similarity and discrimination: a selective review and a connectionist model. Psychological Review, 101, 587–607.
Pothos, E.M. , & Chater, N. (2002). A simplicity principle in unsupervised human categorization. Cognitive Science, 26, 303–343.
Rehder, B. , & Murphy, G.L. (2003). A knowledge-resonance (KRES) model of category learning. Psychonomic Bulletin & Review, 10 (4), 759–784.
Rescorla, R.A. , & Wagner, A. R. (1972). A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and non-reinforcement. In A.H. Black & W.F. Prokasy (eds.), Classical Conditioning II: Current Research and Theory (pp. 64–99). New York: Appleton-Century-Crofts.
Rodrigues, P.M. , & Murre, J.M.J. (2007). Rules-plus-exception tasks: a problem for exemplar models? Psychonomic Bulletin & Review, 14, 640–646.
Rumelhart, D.E. , Hinton, G.E. , & Williams, R.J. (1986). Learning internal representations by back-propagating errors. In D.E. Rumelhart & J.L. McClelland (eds.), Parallel Distributed Processing (Vol. 1). Cambridge, MA: MIT Press.
Sanborn, A.N. , Griffiths, T.L. , & Navarro, D. (2006). A more rational model of categorization. In R. Sun & N. Miyake (eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum.
Shanks, D.R. (1985). Forward and backward blocking in human contingency judgement. Quarterly Journal of Experimental Psychology: Comparative & Physiological Psychology, 37B, 1–21.
Shepard, R.N. , Hovland, C.L. , & Jenkins, H.M. (1961). Learning and memorization of classifications. Psychological Monographs, 75 (13), Whole No. 517.
Slamecka, N.J. (1968). A methodological analysis of shift paradigms in human discrimination learning. Psychological Bulletin, 69, 423–438.
Tenenbaum, J.B. , & Griffiths, T.L. (2003). Theory-based causal inference. In S. Becker , S. Thrun , & K. Obermayer (eds.), Advances in Neural Information Processing Systems (Vol. 15, pp. 35–42). Cambridge, MA: MIT Press.
Treat, T.A. , Kruschke, J.K. , Viken, R.J. , & McFall, R. M. (2010). Application of associative learning paradigms to clinically relevant individual differences in cognitive processing. In T.R. Schachtman & S. Reilly (eds.), Conditioning and Animal Learning: Human and non-Human Animal Applications. Oxford: Oxford University Press.
Treat, T.A. , McFall, R.M. , Viken, R. J. , & Kruschke, J.K. (2001). Using cognitive science methods to assess the role of social information processing in sexually coercive behavior. Psychological Assessment, 13 (4), 549–565.
Treat, T.A. , McFall, R.M. , Viken, R.J. , Kruschke, J.K. , Nosofsky, R.M. , & Wang, S.S. (2007). Clinical cognitive science: applying quantitative models of cognitive processing to examine cognitive aspects of psychopathology. In R.W.J. Neufeld (ed.), Advances in Clinical Cognitive Science: Formal Modeling of Processes and Symptoms (pp. 179–205). Washington, DC: American Psychological Association.
Vigo, R. (2006). A note on the complexity of Boolean concepts. Journal of Mathematical Psychology, 50, 501–510.
Vigo, R. (2009). Categorical invariance and structural complexity in human concept learning. Journal of Mathematical Psychology, 53, 203–221.
Wills, A.J. , Lavric, A. , Croft, G.S. , & Hodgson, T.L. (2007). Predictive learning, prediction errors, and attention: evidence from event-related potentials and eye tracking. Journal of Cognitive Neuroscience, 19 (5), 843–854.
Yang, L.-X. , & Lewandowsky, S. (2003). Context-gated knowledge partitioning in categorization. Journal of Experimental Psychology: Learning, Memory, & Cognition, 29 (4), 663–679.
Yang, L.-X. , & Lewandowsky, S. (2004). Knowledge partitioning in categorization: constraints on exemplar models. Journal of Experimental Psychology: Learning, Memory, & Cognition, 30 (5), 1045–1064.

Reference Title: REFERENCES

Reference Type: reference-list

Blough, D.S. (1975). Steady state data and a quantitative model of operant generalization and discrimination. Journal of Experimental Psychology: Animal Behavior Processes, 1, 3–21.
Brandon, S.E. , Vogel, E.H. , & Wagner, A.R. (2000). A componential view of configural cues in generalization and discrimination in Pavlovian conditioning. Behavioral Brain Research, 110, 67–72.
Ghirlanda, S. , & Enquist, M. (2003). A century of generalization. Animal Behaviour, 66, 15–36.
Harris, J. A. (2006). Elemental representations of stimuli in associative learning. Psychological Review, 113, 584–605.
Harris, J. A. & Livesey, E. J. (2008). Comparing patterning and biconditional discriminations in humans. Journal of Experimental Psychology: Animal Behavior Processes, 34, 144–154.
Honig, W.K. , & Urcuioli, P.J. (1981). The legacy of Guttman and Kalish (1956): 25 years of research on stimulus generalization. Journal of the Experimental Analysis of Behavior, 36, 405–445.
Jones, F. , & McLaren, I.P.L. (1999). Rules and associations. In Proceedings of the Twenty-First Annual Conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum.
Jones, F. W. , & McLaren, I.P.L. (2009). Human sequence learning under incidental and intentional conditions. Journal of Experimental Psychology: Animal Behavior Processes, 35, 538–553.
Lamberts, K. (1996). Exemplar models and prototype effects in similarity-based categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 1503–1507.
Le Pelley, M.E. , & McLaren, I.P.L. (2003). Learned associability and associative change in human causal learning. Quarterly Journal of Experimental Psychology, 56B, 56–67.
Le Pelley, M.E. , Suret, M. B. , & Beesley, T. (2009). Learned predictiveness effects in humans: a function of learning, performance, or both? Journal of Experimental Psychology: Animal Behavior Processes, 35 (3), 312–327.
Livesey, E.J. (2006). Discrimination learning and stimulus representation. Unpublished PhD thesis, University of Cambridge, Cambridge.
Livesey, E.J. , Harris, I.M. , & Harris, J.A. (2009). Attentional changes during implicit learning: signal validity protects a target stimulus from the attentional blink. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 408–422.
Livesey, E.J. , & McLaren, I.P.L. (2007). Elemental associability changes in human discrimination learning. Journal of Experimental Psychology: Animal Behavior Processes, 33, 148–159.
Livesey, E.J. , & McLaren, I.P.L. (2009). Discrimination and generalization along a simple dimension: peak shift and rule-governed responding. Journal of Experimental Psychology: Animal Behavior Processes, 35, 554–565.
Livesey, E.J. , Pearson, L.S. , & McLaren, I.P.L. (2005). Spatial variability and peak shift: a challenge for elemental associative learning? In Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society (pp. 1302–1307). Mahwah, NJ: Erlbaum.
Lochman, T. , & Wills, A.J. (2003). Predictive history in an allergy prediction task. In Proceedings of EuroCogSci 03: The European Conference of the Cognitive Science Society (pp. 217–222).
McClelland, J.L. , & Rumelhart, D.E. (1985). Distributed memory and the representation of general and specific information. Journal of Experimental Psychology: General, 114, 159–188.
McLaren, I.P.L. , Bennett, C.H. , Guttman-Nahir, T. , Kim, K. , & Mackintosh, N. J. (1995). Prototype effects and peak-shift in categorisation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21, 662–673.
McLaren, I.P.L. , Kaye, H. , & Mackintosh, N.J. (1989). An associative theory of the representation of stimuli: applications to perceptual learning and latent inhibition. In R. G. M. Morris (ed.), Parallel Distributed Processing: Implications for Psychology and Neurobiology (pp. 102–130). Oxford: Oxford University Press, Clarendon Press.
McLaren, I.P.L. , & Mackintosh, N.J. (2000). An elemental model of associative learning: I. Latent inhibition and perceptual learning. Animal Learning and Behavior, 28, 211–246.
McLaren, I.P.L. , & Mackintosh, N.J. (2002). Associative learning and elemental representation: II. Generalization and discrimination. Animal Learning and Behavior, 30, 177–200.
Nosofsky, R.M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39–57.
Nosofsky, R.M. (1991). Typicality in logically defined categories: exemplar-similarity versus rule instantiation. Memory & Cognition, 19, 131–150.
Oakeshott, S.M. (2002). Peak shift: an elemental vs a configural analysis. Unpublished PhD thesis, University of Cambridge, Cambridge.
Palmeri, T.J. , & Nosofsky, R.M. (2001). Central tendencies, extreme points, and prototype enhancement effects in ill-defined perceptual categorization. Quarterly Journal of Experimental Psychology, 54A, 197–235.
Pearce, J.M. (1987). A model of stimulus generalisation for Pavlovian conditioning. Psychological Review, 94, 61–73.
Pearce, J. M. (1994). Similarity and discrimination: a selective review and a connectionist model. Psychological Review, 101, 587–607.
Shanks, D.R. , & Darby, R.J. (1998). Feature- and rule-based generalization in human associative learning. Journal of Experimental Psychology: Animal Behavior Processes, 24, 405–415.
Spetch, M.L. , Cheng, K. , & Clifford, C.W.G. (2004). Peak shift but not range effects in recognition of faces. Learning and Motivation, 35 (3), 221– 241.
Spiegel, R. & McLaren, I.P.L. (2003). Abstract and associatively-based representations in human sequence learning. Philosophical Transactions of the Royal Society of London, Series B, 358, 1277–1283.
Suret, M.B. , & McLaren, I.P.L. (2005). Elemental representation and associability: an integrated model. In A.J. Wills (ed.), New Directions in Human Associative Learning. Mahwah, NJ: Lawrence Erlbaum.
Thompson, R.F. (1965). The neural basis of stimulus generalization. In D.I. Mostofsky (ed.), Stimulus Generalization (pp. 154–178). Stanford, CA: Stanford University Press.
Wagner, A.R. , & Brandon, S.E. (2001). A componential theory of Pavlovian conditioning. In R.R. Mowrer & S.B. Klein (eds.), Handbook of Contemporary Learning Theories (pp. 23–64). Mahwah, NJ: Erlbaum.
Widrow, G. , & Hoff, M.E. (1960). Adaptive switching circuits. Institute of Radio Engineers, Western Electronic Show and Convention Record, 4, 96–104.
Wills, A. J. , Reimers, S. , Stewart, N. , Suret, M. , & McLaren, I. P. L. (2000). Tests of the ratio rule in categorization. Quarterly Journal of Experimental Psychology, 53A (4), 983–1011.
Wills, S. , & Mackintosh, N. J. (1998). Peak shift on an artificial dimension. Quarterly Journal of Experimental Psychology, 51B (1), 1–32.

Reference Title: REFERENCES

Reference Type: reference-list

Aldous, D. (1985). Exchangeability and related topics. In École d'Été de Probabilités de Saint-Flour, XIII – 1983 (pp. 1–198). Berlin: Springer.
Anderson, J. R. (1990). The Adaptive Character of Thought. Hillsdale, NJ: Erlbaum.
Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98 (3), 409–429.
Antoniak, C. (1974). Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. The Annals of Statistics, 2, 1152–1174.
Ashby, F. G. , & Alfonso-Reese, L. A. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39, 216–233.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. New York: Springer.
Doucet, A. , de Freitas, N. , & Gordon, N. (2001). Sequential Monte Carlo Methods in Practice. New York: Springer.
Escobar, M. D. , & West, M. (1995). Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association, 90, 577–588.
Ferguson, T. (1973). A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1, 209–230.
Gilks, W. , Richardson, S. , & Spiegelhalter, D. J. (eds.) (1996). Markov Chain Monte Carlo in Practice. Boca Raton, FL: Chapman and Hall CRC.
Goodman, N. D. , Tenenbaum, J. B. , Feldman, J. , & Griffiths, T. L. (2008). A rational analysis of rule-based concept learning. Cognitive Science, 32, 108–154.
Griffiths, T. L. , Canini, K. R. , Sanborn, A. N. , & Navarro, D. J. (2007). Unifying rational models of categorization via the hierarchical Dirichlet process. In Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Erlbaum.
Griffiths, T. L. , Kemp, C. , & Tenenbaum, J. B. (2008a). Bayesian models of cognition. In R. Sun (ed.), Cambridge Handbook of Computational Cognitive Modeling. Cambridge: Cambridge University Press.
Griffiths, T. L. , Sanborn, A. N. , Canini, K. R. , & Navarro, D. J. (2008b). Categorization as nonparametric Bayesian density estimation. In N. Chater & M. Oaksford (eds.), The Probabilistic Mind. Oxford: Oxford University Press.
Grünwald, P. D. (2007). The Minimum Description Length Principle. Cambridge, MA: MIT Press.
Heit, E. , & Bott, L. (2000). Knowledge selection in category learning. In D. L. Medin (ed.), The Psychology of Learning and Motivation (Vol. 39, pp. 163–199). San Diego, CA: Academic Press.
Jaynes, E. T. (2003). Probability Theory: The Logic of Science. Cambridge: Cambridge University Press.
Kemp, C. , Perfors, A. , & Tenenbaum, J. B. (2007). Learning over hypotheses with hierarchical Bayesian models. Developmental Science, 10, 307–321.
Kemp, C. , & Tenenbaum, J. B. (2008). The discovery of structural form. Proceedings of the National Academy of Sciences, 105, 10687–10692.
Kemp, C. , Tenenbaum, J. B. , Griffiths, T. L. , Yamada, T. , & Ueda, N. (2006). Learning systems of concepts with an infinite relational model. In Proceedings of the 21st National Conference on Artificial Intelligence (pp. 381–388). Boston, MA: AAAI Press.
Kemp, C. , Tenenbaum, J. B. , Niyogi, S. , & Griffiths, T. L. (2010). A probabilistic model of theory formation. Cognition, 114, 165–196.
Kruschke, J. K. (1990). A connectionist model of category learning. Unpublished doctoral dissertation, University of California, Berkeley, CA.
Lee, M. D. , & Navarro, D. J. (2005). Minimum description length and psychological clustering models. In P. D. Grünwald , I. J. Myung , & M. A. Pitt (eds.), Advances in Minimum Description Length: Theory and Applications (pp. 355–384). Cambridge, MA: MIT Press.
Love, B. C. , Medin, D. L. , & Gureckis, T. M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111, 309–332.
Maas, A. L. , & Kemp, C. (2009). One-shot learning with Bayesian networks. Proceedings of the 31st Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Marr, D. (1982). Vision. San Francisco, CA: W. H. Freeman.
McLachlan, G. J. , & Basford, K. E. (1988). Mixture Models. New York: Marcel Dekker.
Medin, D. L. , & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.
Murphy, G. L. , & Allopenna, P. D. (1994). The locus of knowledge effects in concept learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 904–919.
Navarro, D. J. (2006). From natural kinds to complex categories. In Proceedings of the 28th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum.
Navarro, D. J. , & Griffiths, T. L. (2008). Latent features in similarity judgments: a nonparametric Bayesian approach. Neural Computation, 20, 2597–2628.
Navarro, D. J. , Griffiths, T. L. , Steyvers, M. , & Lee, M. D. (2006). Modeling individual differences using Dirichlet processes. Journal of Mathematical Psychology, 50, 101–122.
Neal, R. M. (1998). Markov chain sampling methods for Dirichlet process mixture models (Tech. Rep. No. 9815). Department of Statistics, University of Toronto.
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: Oxford University Press.
Pazzani, M. J. (1991). Influence of prior knowledge on concept acquisition: experimental and computational results. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17, 416–432.
Perfors, A. , & Tenenbaum, J. B. (2009). Learning to learn categories. In Proceedings of the 31st Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Pitman, J. (2002). Combinatorial stochastic processes. Notes for Saint Flour Summer School.
Reed, S. K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3, 393–407.
Rehder, B. , & Murphy, G. L. (2003). A knowledge-resonance (KRES) model of category learning. Psychonomic Bulletin & Review, 10, 759–784.
Rosseel, Y. (2002). Mixture models of categorization. Journal of Mathematical Psychology, 46, 178–210.
Sanborn, A. N. , Griffiths, T. L. , & Navarro, D. J. (2006). A more rational model of categorization. In Proceedings of the 28th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum.
Shafto, P. , Kemp, C. , Mansinghka, V. , Gordon, M. , & Tenenbaum, J. B. (2006). Learning cross-cutting systems of categories. In Proceedings of the 28th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum.
Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317–1323.
Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. London: Chapman and Hall.
Smith, J. D. , & Minda, J. P. (1998). Prototypes in the mist: the early epochs of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 1411–1436.
Teh, Y. , Jordan, M. , Beal, M. , & Blei, D. (2004). Hierarchical Dirichlet processes. In Advances in Neural Information Processing Systems 17. Cambridge, MA: MIT Press.
Tenenbaum, J. B. , & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24, 629–641.
Vanpaemel, W. , & Storms, G. (2008). In search of abstraction: the varying abstraction model of categorization. Psychonomic Bulletin & Review, 15, 732–749.
Wattenmaker, W. D. , Dewey, G. I. , Murphy, T. D. , & Medin, D. L. (1986). Linear separability and concept learning: context, relational properties, and concept naturalness. Cognitive Psychology, 18, 158–194.

Reference Title: REFERENCES

Reference Type: reference-list

Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98, 409–429.
Ashby, G. F. , Alfonso-Reese, L. A. , Turken, A. U. , & Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105, 442–481.
Attneave, F. (1959). Applications of Information Theory to Psychology. New York: Holt, Rinehart & Winston.
Barlow, B. H. (1974). Inductive inference, coding, perception, and language. Perception, 3, 123–134.
Brent, M. R. , & Cartwright, T. A. (1996). Distributional regularity and phonotactic constraints are useful for segmentation. Cognition, 61, 93–125.
Busemeyer, J. R. , Matthew, M. , & Wang, Z. A. (2006). Quantum game theory explanation of disjunction effects. In R. Sun & N. Miyake (eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 131–135). Mahwah, NJ: Erlbaum.
Chater, N. (1996). Reconciling simplicity and likelihood principles in perceptual organization. Psychological Review, 103, 566–591.
Chater, N. (1999). The search for simplicity: a fundamental cognitive principle? Quarterly Journal of Experimental Psychology, 52A, 273–302.
Colreavy, E. , & Lewandowsky, S. (2008). Strategy development and learning differences in supervised and unsupervised categorization. Memory & Cognition, 36, 762–775.
Compton, B. J. , & Logan, G. D. (1993). Evaluating a computational model of perceptual grouping. Perception & Psychophysics, 53, 403–421.
Compton, B. J. & Logan, G. D. (1999). Judgments of perceptual groups: reliability and sensitivity to stimulus transformation. Perception & Psychophysics, 61, 1320–1335.
Comtet, L. (1974). Advanced Combinatorics, the Art of Finite and Infinite Expansions. Dordrecht: Reidel.
Feldman, J. (2000). Minimization of Boolean complexity in human concept learning. Nature, 407, 630–633.
Fraboni, M. , & Cooper, D. (1989). Six clustering algorithms applied to the WAIS-R: the problem of dissimilar cluster analysis. Journal of Clinical Psychology, 45, 932–935.
Garner, W. R. (1974). The Processing of Information and Structure. Potomac, MD: LEA.
Gosselin, F. & Schyns, P. G. (2001). Why do we SLIP to the basic-level? Computational constraints and their implementation. Psychological Review, 108, 735–758.
Gureckis, T. M. , & Goldstone, R. L. (2008). The effect of the internal structure of categories on perception. In Proceedings of the 30th Annual Meeting of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum.
Hahn, U. , Chater, N. , & Richardson, L. B. C. (2003). Similarity as transformation. Cognition, 87, 1–32.
Hines, P. , Pothos, E. M. , & Chater, N. (2007). A non-parametric approach to simplicity clustering. Applied Artificial Intelligence, 21, 729–752.
Hochberg, J. E. , & McAlister, E. (1953). A quantitative approach to figural goodness. Journal of Experimental Psychology, 46, 361–364.
Kurtz, K. J. (2007). The divergent autoencoder (DIVA) model of category learning. Psychonomic Bulletin & Review, 14, 560–576.
Leeuwenberg, E. (1969). Quantitative specification of information in sequential patterns. Psychological Review, 76, 216–220.
Li, M. , & Vitányi, P. (1997). An Introduction to Kolmogorov Complexity and its Applications (2nd edition). Berlin: Springer-Verlag.
Love, B. C. , Medin, D. L. , & Gureckis, T. M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111, 309–332.
Mach, E. (1959/1906). The Analysis of Sensations and the Relation of the Physical to the Psychical. New York: Dover Publications.
Milton, F. , Longmore, C. A. , & Wills, A. J. (2008). Processes of overall similarity sorting in free classification. Journal of Experimental Psychology: Human Perception and Performance, 34, 676–692.
Milton, F. , & Wills, A. J. (2004). The influence of stimulus properties on category construction. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 407–415.
Milton, F. , Wills, A. J. , & Hodgson, T. L. (2009). The neural basis of overall similarity sorting. NeuroImage, 46, 319–326.
Murphy, G. L. (1991). Parts in object concepts: experiments with artificial categories. Memory & Cognition, 19, 423–438.
Murphy, G. L. , & Allopenna, P. D. (1994). The locus of knowledge effects in concept learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 904–919.
Nielsen, M. A. , & Chuang, L. L. (2000). Quantum Computation and Quantum Information. Cambridge: Cambridge University Press.
Oaksford, M. , & Chater, N. (1994). A rational analysis of the selection task as optimal data selection. Psychological Review, 101, 608–631.
Op de Beeck, H. , Torfs, K. , & Wagemans, J. (2008). Perceived shape similarity among unfamiliar objects and the organization of the human object vision pathway. Journal of Neuroscience, 28, 10111–10123.
Pomerantz, J. R. , & Kubovy, M. (1986). Theoretical approaches to perceptual organization: simplicity and likelihood principles. In K. R. Boff , L. Kaufman , & J. P. Thomas (Eds.), Handbook of Perception and Human Performance, Vol. II: Cognitive Processes and Performance (pp. 1–45). New York: Wiley.
Pothos, E. M. (2007). Occam and Bayes in predicting category intuitiveness. Artificial Intelligence Review, 21 (8), 729–752.
Pothos, E. M. , & Bailey, T. M. (2009). Predicting category intuitiveness with the rational model, the simplicity model, and the Generalized Context Model. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 1062–1080.
Pothos, E. M. , & Chater, N. (2002). A simplicity principle in unsupervised human categorization. Cognitive Science, 26, 303–343.
Pothos, E. M. , & Chater, N. (2005). Unsupervised categorization and category learning. Quarterly Journal of Experimental Psychology, 58A, 733–752.
Pothos, E. M. , & Close, J. (2008). One or two dimensions in spontaneous classification: a simplicity approach. Cognition, 107, 581–602.
Pothos, E. M. , Perlman, A. , Edwards, D. J. , Gureckis, T. M. , Hines, P. M. , & Chater, N. (2008). Modeling category intuitiveness. In Proceedings of the 30th Annual Conference of the Cognitive Science Society. Mahwah, NJ: LEA.
Pothos, E. M. , & Wolff, J. G. (2006). The simplicity and power model for inductive inference. Artificial Intelligence Review, 26, 211–225.
Rissanen, J. (1978). Modeling by shortest data description. Automatica, 14, 465–471.
Rissanen, J. (1986). Stochastic complexity and modeling. Annals of Statistics, 14, 1080–1100.
Rosch, E. , & Mervis, C. B. (1975). Family resemblances: studies in the internal structure of categories. Cognitive Psychology, 7, 573–605.
Sanborn, A. N. , Griffiths, T. L. , & Navarro, D. (2006). A more rational model of categorization. In R. Sun & N. Miyake (eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum.
Stewart, N. , & Brown, G. D. A. (2005). Similarity and dissimilarity as evidence in perceptual categorization. Journal of Mathematical Psychology, 49, 403–409.
Tversky, A. (1977). Features of similarity. Psychological Review, 84, 327–352.
Wallace, C. S. , & Freeman, P. R. (1987). Estimation and inference by compact coding. Journal of the Royal Statistical Society, Series B, 49, 240–251.
Wolff, J. G. (1977). The discovery of segmentation in natural language. British Journal of Psychology, 67, 377–390.

Reference Title: REFERENCES

Reference Type: reference-list

Abidi, S. , Hoe, K. , & Goh, A. (2001). Analyzing data clusters: a rough sets approach to extract cluster-defining symbolic rules. In Advances in Intelligent Data Analysis (pp. 248–257). Berlin: Springer-Verlag.
Ahn, W. K ., & Medin, D. L. (1992). A two-stage model of category construction. Cognitive Science, 16, 81–121.
Alfonso-Reese, L. (1996). Dynamics of category learning. Unpublished doctoral dissertation, University of Santa Barbara, Santa Barbara, CA.
Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98 (3), 409–429.
Andrieu, C. , De Freitas, N. , Doucet, A. , & Jordan, M. (2003). An introduction to mcmc for machine learning. Machine Learning, 50, 5–43.
Ashby, F. , Alfonso-Reese, L. , Turken, A. , & Waldron, E. (1998). A neuropsychological theory of multiple system in category learning. Psychological Review, 105 (5), 442–481.
Ashby, F. , Queller, S. , & Berretty, P. (1999). On the dominance of unidimensional rules in unsupervised categorization. Perception & Psychophysics, 61, 1178–1199.
Blair, M. , & Homa, D. L. (2005). Integrating novel dimensions to eliminate category exceptions: when more is less. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 258–271.
Bower, G. , & Trabasso, T. (1964). Presolution reversal and dimensional shifts in concept identification. Journal of Experimental Psychology, 67, 398–399.
Brown, S. , & Steyvers, M. (2009). Detecting and predicting changes. Cognitive Psychology, 58, 49–67.
Bruner, J. , Goodnow, J. , & Austin, G. (1956). A Study of Thinking. New York: Wiley.
Carpenter, G. A. , & Grossberg, S. (1988). The art of adaptive pattern recognition by a self-organizing neural network. Computer, 21 (3), 77–88.
Chater, N. (1999). The search for simplicity: a fundmental cognitive principle? The Quarterly Journal of Experimental Psychology, 52A (2), 273–302.
Daw, N. , & Courville, A. (2007). The pigeon as particle filter. In J. Platt, D. Koller, T. Singer, & S. Roweis (eds.), Advances in Neural Information Processing Systems (Vol. 20, pp. 369–376). Cambridge, MA: MIT Press.
Daw, N. , & Touretzky, D. (2002). Long-term reward prediction in td models of the dopamine system. Neural Computation, 14, 603–616.
Doucet, A. , Godsill, S. , & Andrieu, C. (2000). On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing, 10 (3), 197–208.
Elio, R. , & Anderson, J. (1984). The effects of information order and learning mode on schema abstraction. Memory & Cognition, 12 (1), 20–30.
Erickson, M. , & Kruschke, J. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127 (2), 107–140.
Fu, W. , & Anderson, J. (2006). From recurrent choice to skill learning: a reinforcement-learning model. Journal of Experimental Psychology: General, 135 (2), 184–206.
Gluck, M. , & Myers, C. (1993). Hippocampal mediation of stimulus representation: a computational theory. Hippocampus, 3 (4), 491–516.
Goldstone, R. (1994). Influence of categorization on perceptual discrimination. Journal of Experimental Psychology: General, 123 (2), 178–200.
Goldstone, R. , & Steyvers, M. (2001). The sensitization and differentiation of dimensions during category learning. Journal of Experimental Psychology: General, 1, 116–139.
Goodman, N. , Tenenbaum, J. , Feldman, J. , & Griffiths, T. L. (2009). A rational analysis of rule-based concept learning. Cognitive Science, 32 (1), 108–154.
Grossberg, S. (1976). Adaptive pattern classification and universal recoding. II: feedback, expectation, olfaction, and illusions. Biological Cybernetics, 23, 187–202.
Grossberg, S. (1987). Competitive learning: from interactive activation to adaptive resonance. Cognitive Science, 11, 23–63.
Gureckis, T. M. , & Goldstone, R. L. (2008). The effect of the internal structure of categories on perception. In B. C. Love , K. McRae , & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (p. 843). Austin, TX: Cognitive Science Society.
Gureckis, T. , & Love, B. (2002). Who says models can only do what you tell them? Unsupervised category learning data, fits, and predictions. In Proceedings of the 24th Annual Conference of the Cognitive Science Society (pp. 399–404). Hillsdale, NJ: Lawrence Erlbaum Associates.
Gureckis, T. , & Love, B. (2003). Towards a unified account of supervised and unsupervised learning. Journal of Experimental and Theoretical Artificial Intelligence, 15, 1–24.
Gureckis, T. , & Love, B. (2004). Common mechanisms in infant and adult category learning. Infancy, 5 (2), 173–198.
Gureckis, T. , & Love, B. C. (2009). Short term gains, long term pains: how cues about state aid learning in dynamic environments. Cognition, 113, 293–313.
Harnad, S. (ed.) (1987). Categorical Perception: The Groundwork of Cognition. New York: Cambridge University Press.
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59–69.
Kruschke, J. (1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological Review, 99 (1), 22–44.
Logan, J. , Lively, S. , & Pisoni, D. (1991). Training Japanese listeners to identify English /r/ and /l/: a first report. Journal of the Acoustical Society of America, 89, 874–886.
Love, B. (2005). Environment and goals jointly direct category acquisition. Current Directions in Psychological Science, 14 (4), 195–199.
Love, B. , & Gureckis, T. (2005). Modeling learning under the influence of culture. In W. Ahn , R. Goldstone , B. Love , A. Markman , & P. Wolff (eds.), Categorization Inside and Outside the Laboratory: Essays in Honor of Douglas L. Medin (pp. 229–248). Washington, DC: APA Books.
Love, B. , & Gureckis, T. (2007). Models in search of the brain. Cognitive, Affective, & Behavioral Neuroscience, 7 (2), 90–108.
Love, B. , & Gureckis, T. (2006). The emergence of multiple learning systems. In Proceedings of the 28th Annual Meeting of the Cognitive Science Society. Mahwah, NJ: Erlbaum.
Love, B. , Medin, D. , & Gureckis, T. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111 (2), 309–332.
Luce, R. D. (1959). Individual Choice Behavior: A Theoretical Analysis. Westport, CT: Greenwood Press.
Markman, A. , & Ross, B. (2003). Category use and category learning. Psychological Bulletin, 4, 592–613.
Mathy, F. , & Feldman, J. (2009). A rule-based presentation order facilitates category learning. Psychonomic Bulletin & Review, 16, 1050–1057.
McDonnell, J. , & Gureckis, T. (2009). How perceptual categories influence trial and error learning in humans. In Multidisciplinary Symposium on Reinforcement Learning. Montreal, Canada.
Medin, D. L. , & Bettger, J. (1994). Presentation order and recognition of categorically related examples. Psychonomic Bulletin & Review, 1, 250–254.
Medin, D. , & Schaffer, M. (1978). Context theory of classification learning. Psychological Review, 85 (3), 207–238.
Michalski, R. , & Stepp, R. (1983). Learning from observation: conceptual clustering. In R. Michalski , J. Carbonell , & T. Mitchell (eds.), Machine Learning: an Artificial Intelligence Approach (Vol. I, pp. 331–363). Los Altos, CA: Morgan-Kaufmann.
Montague, P. , Dayan, P. , Person, C. , & Sejnowski, T. (1995). Bee foraging in uncertain environments using predictive hebbian learning. Nature, 377 (6551), 725–728.
Murphy, G. , & Ross, B. (1994). Predictions from uncertain categorizations. Cognitive Psychology, 27, 148–193.
Nosofsky, R. M. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10 (1), 104–114.
Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115 (1), 39–57.
Nosofsky, R. M. , Palmeri, T. J. , & McKinley, S. C. (1994). Rule-plus-exception model of classification learning. Psychological Review, 101 (1), 53–79.
Palmeri, T. J. , & Nosofsky, R. M. (1995). Recognition memory for exceptions to the category rule. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21 (3), 548–568.
Pothos, E. , & Bailey, T. (2009). Predicting category intuitiveness with the rational model, the simplicity model, and the generalized context model. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35 (4), 1062–1080.
Pothos, E. , & Chater, N. (2002). A simplicity principle in unsupervised human categorization. Cognitive Science, 26, 303–343.
Pothos, E. , & Close, J. (2008). One or two dimensions in spontaneous classification: a simplicity approach. Cognition, 107, 581–602.
Pothos, E. , Perlman, A. , Edwards, D. , Gureckis, T. , Hines, P. , & Chater, N. (2008). Modeling category intuitiveness. In B. Love , K. McRae , & V. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Redish, A. , Jensen, S. , Johnson, A. , & Kurth-Nelson, Z. (2007). Reconciling reinforcement learning models with behavioral extinction and renewal: implications for addition, relapse, and problem gambling. Psychological Review, 114 (3), 784–805.
Rehder, B. , & Hoffman, A. (2005). Eyetracking and selective attention in category learning. Cognitive Psychology, 51, 1–41.
Rosch, E. , & Mervis, C. (1975). Family resemblances: studies in the internal structure of categories. Cognitive Psychology, 7, 573–605.
Sakamoto, Y. , Jones, M. , & Love, B. (2008). Putting the psychology back into psychological models: mechanistic vs. rational approaches. Memory & Cognition, 36, 1057–1065.
Sakamoto, Y. , & Love, B. C. (2004). Schematic influences on category learning and recognition memory. Journal of Experimental Psychology: General, 133 (4), 534–553.
Sanborn, A. , Griffiths, T. , & Navarro, D. (2006). A more rational model of categorization. In R. Sun & N. Miyake (eds.), Proceedings of the 28th Annual Meeting of the Cognitive Science Society. Mahwah, NJ: Erlbaum.
Schultz, W. , Dayan, P. , & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275, 1593–1598.
Shepard, R. , Hovland, C. , & Jenkins, H. (1961). Learning and memorization of classifications. Psychological Monographs, 75 (13), Whole No. 517.
Smith, J. , & Minda, J. (2000). Thirty categorization results in search of a model. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26 (1), 3–27.
Steyvers, M. (1999). Morphing techniques for generating and manipulating face images. Behavior Research Methods, Instruments, & Computers, 31, 359–369.
Sutton, R. , & Barto, A. (1998). Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.
Vanpaemel, W. , Storms, G. , & Ons, B. (2005). A varying abstraction model for categorization. In Proceedings of the 27th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum.
Verde, M. , Murphy, G. , & Ross, B. (2005). Influence of multiple categories on the prediction of unknown properties. Memory & Cognition, 33 (3), 479–487.
Widrow, B. , & Hoff, M. (1960). Adaptive switching circuits. Institute of Radio Engineers, Western Electronic Show and Convention Record, 4, 96–104.
Wills, A. J. , Noury, M. , Moberly, N. J. , & Newport, M. (2006). Formation of category representations. Memory & Cognition, 34, 17–27.
Yamauchi, T. , Love, B. , & Markman, A. (2002). Learning nonlinearly separable categories by inference and classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 3, 585–593.
Younger, B. (1985). The segregation of items into categories by ten-month-old infants. Child Development, 56 (6), 1574–1583.
Younger, B. , & Cohen, L. (1986). Developmental change in infants' perception of correlations among attributes. Child Development, 57 (3), 803–815.

Reference Title: REFERENCES

Reference Type: reference-list

Anderson, J. R. , & Matessa, M. (1991). An incremental Bayesian algorithm for categorization. In D. H. Fisher , M. J. Pazzani , & P. Langley (eds.), Concept Formation: Knowledge and Experience in Unsupervised Learning. San Mateo, CA: Morgan Kaufmann.
Cheeseman, P. , Kelly, J. , Self, M. , Stutz, J. , Taylor, W. , & Freeman, D. (1988). autoclass: A Bayesian classification system. In Proceedings of the Fifth International Conference on Machine Learning (pp. 54–64). Ann Arbor, MI: Morgan Kaufmann.
Clapper, J. P. , & Bower, G. H. (2002). Adaptive categorization in unsupervised learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28 (5), 908–923.
Dretske, F. (1999). Knowledge and the Flow of Information. Palo Alto, CA: CSLI Press.
Feigenbaum, E. (1961). The simulation of verbal learning behavior. In Proceedings of the Western Joint Computer Conference (pp. 121–132). Reprinted in J. W. Shavlik & T. G. Dietterich (eds.) (1990). Readings in Machine Learning. San Mateo, CA: Morgan Kaufmann.
Fisher, D. H. (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2, 139–172.
Fisher, D. H. , & Langley, P. (1990). The structure and formation of natural categories. In G. H. Bower (ed.), The Psychology of Learning and Motivation: Advances in Research and Theory (Vol. 26). Cambridge, MA: Academic Press.
Fisher, D. H. , & Pazzani, M. J. (1991). Computational models of concept learning. In D. H. Fisher , M. J. Pazzani , & P. Langley (eds.), Concept Formation: Knowledge and Experience in Unsupervised Learning. San Mateo, CA: Morgan Kaufmann.
Gennari, J. H. (1990). An experimental study of concept formation. Doctoral dissertation, Department of Information & Computer Science, University of California, Irvine, CA.
Gennari, J. H. , Langley, P. , & Fisher, D. H. (1989). Models of incremental concept formation. Artificial Intelligence, 40, 11–61.
Gluck, M. , & Corter, J. (1985). Information, uncertainty and the utility of categories. In Proceedings of the Seventh Annual Conference of the Cognitive Science Society (pp. 283–287). Irvine, CA: Lawrence Erlbaum.
Gruber, T. R. (1995). Toward principles for the design of ontologies used for knowledge sharing. International Journal of Human-Computer Studies, 43, 907–928. Available on-line.
Iba, W. (1991). Acquisition and improvement of human motor skills: learning through observation and practice. Doctoral dissertation, Department of Information & Computer Science, University of California, Irvine, CA.
Iba, W. (1993). Concept formation in temporally structured domains. In NASA Workshop on the Automation of Time Series, Signatures, and Trend Analysis. Moffett Field: NASA Ames Research Center.
Iba, W. , & Langley, P. (2001). Unsupervised learning of probabilistic concept hierarchies. In G. Paliouras , V. Karkaletsis , & C. D. Spyropoulos (eds.), Machine Learning and its Applications. Berlin: Springer.
Kolodner, J. L. (1983). Reconstructive memory: a computer model. Cognitive Science, 7, 281–328.
Langley, P. (1995). Order effects in incremental learning. In P. Reimann & H. Spada (eds.), Learning in Humans and Machines: Towards an Interdisciplinary Learning Science. Oxford: Elsevier.
Langley, P. , & Allen, J. A. (1993). A unified framework for planning and learning. In S. Minton (ed.), Machine Learning Methods for Planning and Scheduling. San Mateo, CA: Morgan Kaufmann.
Lebowitz, M. (1982). Correcting erroneous generalizations. Cognition and Brain Theory, 5, 367–381.
Martin, J. D. , & Billman, D. O. (1994). Acquiring and combining overlapping concepts. Machine Learning, 16, 121–155.
McKusick, K. B. , & Langley, P. (1991). Constraints on tree structure in concept formation. In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (pp. 810–816). Sydney: Morgan Kaufmann.
Thompson, K. , & Langley, P. (1991). Concept formation in structured domains. In D. H. Fisher , M. J. Pazzani , & P. Langley (eds.), Concept Formation: Knowledge and Experience in Unsupervised Learning. San Mateo, CA: Morgan Kaufmann.

Reference Title: REFERENCES

Reference Type: reference-list

Anderson, J. R. (1991). Is human cognition adaptive? Behavioral and Brain Sciences, 14, 471–517.
Balcetis, E. , & Dale, R. (2007). Conceptual set as a top-down constraint on visual object identification. Perception, 36, 581–595.
Choi, S. , McDaniel, M. A. , & Busemeyer, J. R. (1993). Incorporating prior biases in network models of conceptual rule learning. Memory & Cognition, 21, 413–423.
Erickson, J. E. , Chin-Parker, S. , & Ross, B. H. (2005). Inference and classification learning of abstract coherent categories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 86–99.
Friston, K. (2003). Learning and inference in the brain. Neural Networks, 16, 1325–1352.
Gluck, M. A. , & Bower, G. H. (1988). From conditioning to category learning: an adaptive network model. Journal of Experimental Psychology: General, 117, 227–247.
Goldstone, R. L. , & Medin, D. L. (1994). Similarity, interactive activation, and mapping: an overview. In K. Holyoak & J. Barnden (eds.), Advances in Connectionist and Neural Computation Theory, Vol. 2: Analogical Connections (pp. 321–362). New York: Ablex.
Hampton, J. A. (1979). Polymorphous concepts in semantic memory. Journal of Verbal Learning and Verbal Behavior, 18, 441–461.
Hampton, J. A. (1995). Testing the prototype theory of concepts. Journal of Memory and Language, 34, 686–708.
Harris, H. D. (in preparation). Uneven weights in category learning revealed by tests of partial items.
Harris, H. D. , & Rehder, B. (2006). Modeling category learning with exemplars and prior knowledge. In R. Sun (ed.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 1440–1445). Mahwah, NJ: Lawrence Erlbaum Associates.
Heit, E. (1994). Models of the effects of prior knowledge on category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 1264–1282.
Heit, E. , & Bott, L. (2000). Knowledge selection in category learning. In D. Medin (ed.), Psychology of Learning and Motivation (Vol. 39, pp. 163–199). San Diego, CA: Academic Press.
Hoffman, A. B. , Harris, H. D. , & Murphy, G. L. (2008). Prior knowledge enhances the category dimensionality effect. Memory & Cognition, 36, 256–270.
Hoffman, A. B. , & Murphy, G. L. (2006). Category dimensionality and feature knowledge: when more features are learned as easily as fewer. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32, 301–315.
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 81, 3088–3092.
Hopfield, J. J. (1984). Neurons with graded responses have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences, 81, 3088–3092.
Kim, S. , & Rehder, B. (in press). How prior knowledge affects selective attention during category learning: an eyetracking study. Memory and Cognition.
Kruschke, J. K. (1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological Review, 99, 22–44.
Love, B. C. , Medin, D. L. , & Gureckis, T. M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111, 309–332.
Medin, D. L. , & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.
Medin, D. L. , & Schwanenflugel, P. J. (1981). Linear separability in classification learning. Journal of Experimental Psychology: Human Learning and Memory, 7, 355–368.
Medin, D. L. , Wattenmaker, W. D. , & Hampson, S. E. (1987). Family resemblance, conceptual cohesiveness, and category construction. Cognitive Psychology, 19, 242–279.
Murphy, G. L. (2002). The Big Book of Concepts. Cambridge, MA: MIT Press.
Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 39–57.
O'Reilly, R. C. (1996). Biologically plausible error-driven learning using local activation differences: the generalized recirculation algorithm. Neural Computation, 8, 895–938.
Pitt, M. A. , Kim, W. , Navarro, D. J. , & Myung, J. I. (2006). Global model analysis by parameter space partitioning. Psychological Review, 113, 57–83.
Pothos, E. M. , & Chater, N. (2002). A simplicity principle in unsupervised human categorization. Cognitive Science, 26, 303–343.
Rehder, B. , & Murphy, G. L. (2003). A knowledge-resonance (KRES) model of category learning. Psychonomic Bulletin & Review, 10, 759–784.
Rehder, B. , & Ross, B. H. (2001). Abstract coherent categories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 1261–1275.
Shepard, R. N. , Hovland, C. I. , & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75.
Sikström, S. (2002). Forgetting curves: implications for connectionist models. Cognitive Psychology, 45, 95–152.
Smith, J. D. , & Minda, J. P. (1998). Prototypes in the mist: the early epochs of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 1411–1436.
Smolensky, P. (1986). Information processing in dynamical systems: foundations of harmony theory. In D. E. Rumelhart , J. L. McClelland , & the PDP Research Group (eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 1, pp. 194–281). Cambridge, MA: MIT Press.
Spalding, T. L. , & Murphy, G. L. (1996). Effects of background knowledge on category construction. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 525–538.
Spratling, M. W. , & Johnson, M. H. (2006). A feedback model of perceptual learning and categorization. Visual Cognition, 13, 129–165.
Tanenhaus, M. K. , Spivey-Knowlton, M. J. , Eberhard, K. M. , & Sedivy, J. C. (1995). Integration of visual and linguistic information in spoken language comprehension. Science, 268, 1632–1634.
Wattenmaker, W. D. , Dewey, G. I. , Murphy, T. D. , & Medin, D. L. (1986). Linear separability and concept learning: context, relational properties, and concept naturalness. Cognitive Psychology, 18, 158–194.
Wisniewski, E. J. , & Medin, D. L. (1994). On the interaction of theory and data in concept learning. Cognitive Science, 18, 221–281.
Yamauchi, T. , & Markman, A. B. (1998). Category learning by inference and classification. Journal of Memory and Language, 39, 124–148.

Reference Title: REFERENCES

Reference Type: reference-list

Anderson, J. R. (1976). Language, Memory, and Thought. Hillsdale, NJ: Erlbaum.
The Economist (July 18, 2009). The other-worldly philosophers. The Economist, 392 (8640), 65–67.
Heit, E. , & Rubinstein, J. (1994). Similarity and property effects in inductive reasoning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 411–422.
Murphy, G. L. (2005). The study of concepts inside and outside the lab: Medin vs. Medin. In W. Ahn , R. L. Goldstone , B. C. Love , A. B. Markman , & P. Wolff (eds.), Categorization Inside and Outside the Lab: Essays in Honor of Douglas Medin (pp. 179–195). Washington, DC: APA.
Osherson, D. N. , Smith, E. E. , Wilkie, O. , López, A. , & Shafir, E. (1990). Category-based induction. Psychological Review, 97, 185–200.
Posner, M. I. , & Keele, S. W. (1970). Retention of abstract ideas. Journal of Experimental Psychology, 83, 304–308.
Proffitt, J. B. , Coley, J. D. , & Medin, D. L. (2000). Expertise and category-based induction. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 811–828.
Rehder, B. (2009). Causal-based property generalization. Cognitive Science, 33, 301–344.
Ross, B. H. (1996). Category representations and the effects of interacting with instances. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 1249–1265.
Ross, B. H. , & Murphy, G. L. (1999). Food for thought: cross-classification and category organization in a complex real-world domain. Cognitive Psychology, 38, 495–553.
Schank, R. C. , & Abelson, R. P. (1977). Scripts, Plans, Goals and Understanding. Hillsdale, NJ: Erlbaum.
Sloman, S. A. (1993). Feature-based induction. Cognitive Psychology, 25, 231–280.

Reference Title: REFERENCES

Reference Type: reference-list

Ashby, F. , & O'Brien, J. (2005). Category learning and multiple memory systems. Trends in Cognitive Science, 9 (2), 83–89.
Basso, A. , Capitani, E. , & Laiacona, M. (1988). Progressive language impairment without dementia: a case with isolated category specific semantic deficit. Journal of Neurology, Neurosurgery and Psychiatry, 51, 1201–1207.
Bedny, M. , Caramazza, A. , Grossman, E. , Pascual-Leone, A. , & Saxe, R. (2008). Concepts are more than precepts: the case of action verbs. Journal of Neuroscience, 28, 11347–11353.
Blundo, C. , Ricci, M. , & Miller, L. (2006). Category-specific knowledge deficit for animals in a patient with herpes simplex encephalitis. Cognitive Neuropsychology, 23 (8), 1248–1268.
Capitani, E. , Laiacona, M. , Mahon, B. , & Caramazza, A. (2003). What are the facts of semantic category-specific deficits? A critical review of the clinical evidence. Cognitive Neuropsychology, 20 (3–6), 213–261.
Caramazza, A. , Hillis, A. E. , Rapp, B. C. , & Romani, C. (1990). The multiple semantics hypothesis: multiple confusions? Cognitive Neuropsychology, 7, 161–189.
Caramazza, A. , & Shelton, J. (1998). Domain-specific knowledge systems in the brain the animate-inanimate distinction. Journal of Cognitive Neuroscience, 10 (1), 1–34.
Chao, L. , Haxby, J. V. , & Martin, A. (1999). Attribute-based neural substrates in posterior temporal cortex for perceiving and knowing about objects. Nature Neuroscience, 2, 913–919.
Chao, L. , Martin, A. , & Haxby, J. V. (1999). Are face-responsive regions selective only for faces? NeuroReport, 10 (14), 2945–2950.
Cree, G. , & McRae, K. (2003). Analyzing the factors underlying the structure and computation of the meaning of chipmunk, cherry, chisel, cheese, and cello (and many other such concrete nouns). Journal of Experimental Psychology: General, 132 (2), 163–201.
Damasio, H. , Tranel, D. , Grabowski, T. J. , Adolphs, R. , & Damasio, A. (2004). Neural systems behind word and concept retrieval. Cognition, 92, 179–229.
Devlin, J. , Gonnerman, L. , Andersen, E. , & Seidenberg, M. (1998). Category-specific semantic deficits in focal and widespread brain damage: a computational account. Journal of Cognitive Neuroscience, 10, 77–94.
Downing, P. , Jiang, Y. , Shuman, M. , & Kanwisher, N. (2001). A cortical area selective for visual processing of the human body. Science, 293 (5539), 2470–2473.
Epstein, R. , & Kanwisher, N. (1998). A cortical representation of the local visual environment. Nature, 392 (6676), 598–601.
Farah, M. , & Rabinowitz, C. (2003). Genetic and environmental influences on the organization of semantic memory in the brain: is ‘living things’ an innate category? Cognitive Neuropsychology, 20 (3–6), 401–408.
Garrard, P. , Patterson, K. , Watson, P. C. , & Hodges, J. R. (1998). Category-specific semantic loss in dementia of Alzheimer's type: functional-anatomical correlations from cross-sectional analyses. Brain, 121, 633–646.
Griffiths, T. L. , Canini, K. R. , Sanborn, A. N. , & Navarro, D. J. (2007). Unifying rational models of categorization via the hierarchical Dirichlet process. Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. New York: Lawrence Erlbaum.
Hart, J. , Berndt, R. , & Caramazza, A. (1985). Category-specific naming deficit following cerebral infarction. Nature, 316 (6027), 439–440.
Haxby, J. , Gobbini, M. , Furey, M. , Ishai, A. , Schouten, J. , & Pietrini, P. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293 (5539), 2425–2430.
Hillis, A. , & Caramazza, A. (1991).Category-specific naming and comprehension impairment: a double dissociation. Brain, 114 (5), 2081–2094.
Johansen, M. , & Kruschke, J. (2005). Category representation for classification and feature inference. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31 (6), 1433–1458.
Kanwisher, N. , McDermott, J. , & Chun, M. (1997). The fusiform face area: a module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17 (11), 4302–4311.
Kruschke, J. K. (2008). Models of categorization. In R. Sun (ed.), The Cambridge Handbook of Computational Psychology (pp. 267–301). New York: Cambridge University Press.
Laiacona, M. , Allamano, N. , Lorenzi, L. , & Capitani, E. (2006). A case of impaired naming and knowledge of body parts: are limbs a separate sub-category? Neurocase, 12 (5), 307–316.
Laiacona, M. , Barbarotto, R. , & Capitani, E. (2005).Animals recover but plant life knowledge is still impaired 10 years after herpetic encephalitis: the long-term follow-up of a patient. Cognitive Neuropsychology, 22 (1), 78–94.
Laiacona, M. , & Capitani, E. (2001).A case of prevailing deficit of nonliving categories or a case of prevailing sparing of living categories? Cognitive Neuropsychology, 18, 39–70.
Lambon Ralph, M. A. , Howard, D. , Nightingale, G. , & Ellis, A. W. (1998). Are living and nonliving category-specific deficits causally linked to impaired perceptual or associative knowledge? Evidence from a category-specific double dissociation. Neurocase, 4, 311–338.
Love, B. , Medin, D. , & Gureckis, T. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111 (2), 309–332.
Mahon, B. , & Caramazza, A. (2009). Concepts and categories: a cognitive neuropsychological perspective. Annual Review of Psychology, 60, 27–51.
Martin, A. , & Chao, L. (2001). Semantic memory and the brain: structure and processes. Current Opinion Neurobiology, 11 (2), 194–201.
Martin, A. , & Weisberg, J. (2003). Neural foundations for understanding social and mechanical concepts. Cognitive Neuropsychology, 20, 575–587.
Martin, A. , Wiggs, C. , Ungerleider, L. , & Haxby, J. (1996). Neural correlates of category-specific knowledge. Nature, 379 (6566), 649–652.
McLaren, I. , & Mackintosh, N. (2002). Associative learning and elemental representation: II. Generalization and discrimination. Animal Learning & Behavior, 30 (3), 177–200.
Miceli, G. , Capasso, R. , Daniele, A. , Esposito, T. , Magarelli, M. , & Tomaiuolo, F. (2000). Selective deficit for people's names following left temporal damage: an impairment of domain-specific conceptual knowledge. Cognitive Neuropsychology, 17 (6), 489–516.
Minda, J. , & Smith, J. (2002). Comparing prototype-based and exemplar-based accounts of category learning and attentional allocation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28 (2), 275–292.
Mummery, C. , Patterson, K. , Hodges, J. , & Price, C. (1998). Functional neuroanatomy of the semantic system: divisible by what? Journal of Cognitive Neuroscience, 10 (6), 766–777.
Nosofsky, R. , & Zaki, S. (2002). Exemplar and prototype models revisited: response strategies, selective attention, and stimulus generalization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(5), 924–940.
Papagno, C. , Capasso, R. , & Miceli, G. (2009). Reversed concreteness effect for nouns in a subject with semantic dementia. Neuropsychologia, 47 (4), 1138–1148.
Pietrini, P. , Furey, M. L. , Ricciardi, E. , Gobbini, M. I. , Wu, W. H. , Cohen, L. , Guazzelli, M. , & Haxby, J. V. (2004). Beyond sensory images: object-based representation in the human ventral pathway. Proceedings of the National Academy of Sciences of the USA, 101, 5658–5663.
Puce, A. , Allison, T. , Asgari, M. , Gore, J. , & McCarthy, G. (1996). Differential sensitivity of human visual cortex to faces, letterstrings, and textures: a functional magnetic resonance imaging study. Journal of Neuroscience, 16 (16), 5205–5215.
Rehder, B. , & Kim, S. (2006). How causal knowledge affects classification: a generative theory of categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32 (4), 659–683.
Rehder, B. , & Murphy, G. (2003). A knowledge-resonance (KRES) model of category learning. Psychonomic Bulletin & Review, 10 (4), 759–784.
Rogers, T. , & McClelland, J. (2008). Précis of semantic cognition: a parallel distributed processing approach. Behavioral Brain Sciences, 31 (6), 689–749.
Samson, D. , & Pillon, A. (2003). A case of impaired knowledge for fruit and vegetables. Cognitive Neuropsychology, 20 (3–6), 373.
Sartori, G. , Job, R. , & Coltheart, M. (1993). The organization of object knowledge: evidence from neuropsychology. In D. E. Meyer & S. Kornblum (eds.), Attention and Performance XIV (Silver Jubilee Volume): Synergies in Experimental Psychology, Artificial Intelligence, and Cognitive Neuroscience (pp. 451–465). Cambridge, MA: MIT Press.
Sartori, G. , Lombardi, L. , & Mattiuzzi, L. (2005). Semantic relevance best predicts normal and abnormal name retrieval. Neuropsychologia, 43, 754–770.
Shelton, J. R. , Fouch, E. , & Caramazza, A. (1998). The selective sparing of body part knowledge: a case study. Neurocase, Special issue: Category-specific deficits, 4, 339–351.
Smith, E. , & Grossman, M. (2008). Multiple systems of category learning. Neuroscience and Biobehavioral Reviews, 32 (2), 249–264.
Spiridon, M. , & Kanwisher, N. (2002). How distributed is visual category information in human occipito-temporal cortex? An fMRI study. Neuron, 35 (6), 1157–1165.
Tyler, L. , & Moss, H. (2001). Towards a distributed account of conceptual knowledge. Trends in Cognitive Science, 5 (6), 244–252.
Urgesi, C. , Berlucchi, G. , & Aglioti, S. (2004). Magnetic stimulation of extrastriate body area impairs visual processing of nonfacial body parts. Current Biology, 14 (23), 2130–2134.
Vinson, D. P. , Vigliocco, G , Cappa, S. , & Siri, S. (2003). The breakdown of semantic knowledge: insights from a statistical model of meaning representation. Brian and Language, 86, 347–365.
Warrington, E. , & Shallice, T. (1984). Category specific semantic impairments. Brain, 107 (3), 829–854.
Zaki, S. , Nosofsky, R. , Stanton, R. , & Cohen, A. (2003). Prototype and exemplar accounts of category learning and attentional allocation: a reassessment. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, (6), 1160–1173.
Zannino, G. D. , Perri, R. , Carlesimo, G. A. , Pasqualetti, P. , & Caltagirone, C. (2002). Category-specific impairment in patients with Alzheimer's disease as a function of disease severity: a cross-sectional investigation. Neuropsychologia, 40, 2268–2279.
Zannino, G. D. , Perri, R. , Pasqualetti, P. , Caltagirone, C. , & Carlesimo, G. A. (2006). Analysis of the semantic representations of living and nonliving concepts: a normative study. Cognitive Neuropsychology, 23, 515–540.

Reference Title: REFERENCES

Reference Type: reference-list

Asch, S. (1952). Social Psychology. Englewood Cliffs, NJ: Prentice Hall.
Burnett, R. , Medin, D. , Ross, N. , & Blok, S. (2005). Ideal is typical. Canadian Journal of Experimental Psychology, 59 (1), 5–10.
Feeney, A. , & Heit, E. (eds.) (2007). Inductive Reasoning. New York: Cambridge University Press.
Gagné, C. L. , & Shoben, E. J. (2002). Priming relations in ambiguous noun-noun combinations. Memory & Cognition, 30, 637–646.
Gauthier, I. , & Tarr, M. J. (1997). Becoming a ‘Greeble’ expert: exploring mechanisms for face recognition. Vision Research, 37, 1673–1682.
Goldstone, R. L. (1998). Perceptual learning. Annual Review of Psychology, 49, 585–612.
Henrich, J. , Heine, S. J. , & Norenzayan, A. (2010). The weirdest people in the world? Behavior and Brain Sciences, 33, 61–83.
Markman, A. B. , & Gentner, D. (2000). Structure-mapping in the comparison process. American Journal of Psychology, 113 (4), 501–538.
Medin, D. L. , & Atran, S. (2004). The native mind: biological categorization and reasoning in development and across cultures. Psychological Review, 111, 960–983.
Medin, D. L. , Goldstone, R. L. , & Gentner, D. (1993). Respects for similarity. Psychological Review, 100, 254–278.
Medin, D. L. , Lynch, E. B. , Coley, J. D. , & Atran, S. (1997). Categorization and reasoning among tree experts: do all roads lead to Rome? Cognitive Psychology, 32, 49–96.
Medin, D. L. , & Schaffer M. M. (1978). A context theory of classification learning. Psychological Review, 85, 207–238.
Murphy, G. L. (2004). The Big Book of Concepts. Cambridge, MA: MIT Press.
Murphy, G. L. , & Allopenna, P. D. (1994). The locus of knowledge effects in concept learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 904–919.
Posner, M. I. , & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77 (3), 353–363.
Reed, S. K. (1973). Psychological Processes in Pattern Recognition. New York: Academic Press.
Rehder, B. , & Murphy, G. L. (2003). A knowledge-resonance (KRES) model of category learning. Psychonomic Bulletin & Review, 10, 759–784.
Rosch, E. (1973). Natural categories. Cognitive Psychology, 4, 328–350.
Rosch, E. (1975). Cognitive representations of semantic categories. Journal of Experimental Psychology: General, 104, 192–233.
Rosch, E. , & Mervis, C. B. (1975). Family resemblances: studies in the internal structure of categories. Cognitive Psychology, 7, 573–605.
Rosch, E. , Mervis, C. B. , Gray, W. , Johnson, D. , & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382–439.
Ross, N. , Medin, D. L. , Coley, J. D. , & Atran, S. (2003). Cultural and experiential differences in the development of folkbiological induction. Cognitive Development, 18, 25–47.
Shepard, R. N. , Hovland, C. I. , & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs, 75 (13).
Smith, E. E. , & Medin, D. L. (1981). Categories and Concepts. Cambridge, MA: Harvard University Press.
Smith, E. E. , Shoben, E. J. , & Rips, L. J. , (1974). Structure and process in semantic memory: a featural model for semantic decisions. Psychological Review, 1, 214–241.
Storms, G. , De Boeck, P. , & Rits, W. (2000). Prototype and exemplar-based information on natural language categories. Journal of Memory and Language, 42, 51–73.
Waxman, S. R. (1989). Linking language and conceptual development: linguistic cues and the construction of conceptual hierarchies. Genetic Epistemologist, 17 (2), 13–20.
Waxman, S. R. (2002). Links between object categorization and naming: origins and emergence in human infants. In D. H. Rakison & L. M. Oakes (eds.), Early Category and Concept Development: Making Sense of the Blooming, Buzzing Confusion. New York: Oxford University Press.
Waxman, S. R. , & Lidz , J. (2006). Early word learning. In D. Kuhn & R. Siegler (eds.), Handbook of Child Psychology (6th edition, Vol. 2, pp. 299–335). Hoboken, NJ: Wiley.
Wisniewski, E. J. (1997). When concepts combine. Psychonomic Bulletin & Review, 4, 167–183.
Wisniewski, E. J. , & Medin, D. L. (1994). The fiction and nonfiction of features. In R. S. Michalski & G. D. Tecuci (eds.), Machine Learning: A Multistrategy Approach (Vol. 4, pp. 63–84). San Mateo, CA: Morgan Kaufmann.