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1 - Introduction

Published online by Cambridge University Press:  05 June 2012

Emmanuel M. Pothos
Affiliation:
Swansea University
Andy J. Wills
Affiliation:
University of Exeter
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Summary

Categorization is one of the most fascinating aspects of human cognition. It refers to the process of organizing sensory experience into groups. This is an ability we share to some extent with other animals (e.g. Herrnstein & Loveland, 1964), and is key to our understanding of the world. Humans seem particularly adept at the systematic and productive combination of elementary concepts to develop complex thought. All in all, it is hard to envisage much of cognition without concepts.

The study of categorization has a long history (e.g. Hull, 1920). It is usually considered a particular research theme of cognitive psychology, cognitive science, and cognitive neuroscience. Categorization relates intimately to many other cognitive processes, such as learning, language acquisition and production, decision making, and inductive reasoning. What all these processes have in common is that they are inductive. That is, the cognitive system is asked to process some experience and subsequently extrapolate to novel experience.

A formal model of categorization is taken to correspond to any description of categorization processes in a principled, lawful way. Formal models of categorization are theories that allow quantitative predictions regarding the categorization behaviour of participants. Some formal models also make predictions about the underlying neuroscience.

Selecting the models to be discussed in this volume was difficult. Our goal was to create an accessible volume with a reasonably small number of models. As a result, there are many excellent models which we were not able to include.

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Publisher: Cambridge University Press
Print publication year: 2011

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References

Anderson, J.R. (1991). The adaptive nature of human categorization. Psychological Review, 98, 409–429.CrossRefGoogle Scholar
Ashby, G.F., & Alfonso-Reese, A. L. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39, 216–233.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Busemeyer, J.R., Wang, Z., & Townsend, J.T. (2006). Quantum dynamics of human decision making. Journal of Mathematical Psychology, 50, 220–241.CrossRefGoogle Scholar
Chater, N. (1996). Reconciling simplicity and likelihood principles in perceptual organization. Psychological Review, 103, 566–591.CrossRefGoogle ScholarPubMed
Corter, J.E., & Gluck, M.A. (1992). Explaining basic categories: feature predictability and information. Psychological Bulletin, 2, 291–303.CrossRefGoogle Scholar
Fisher, D. (1996). Iterative optimization and simplification of hierarchical clusterings. Journal of Artificial Intelligence, 4, 147–179.Google Scholar
Fodor, J.A. (1983). The Modularity of Mind. Cambridge, MA: MIT Press.Google Scholar
Fraboni, M., & Cooper, D. (1989). Six clustering algorithms applied to the WAIS-R: the problem of dissimilar cluster analysis. Journal of Clinical Psychology, 45, 932–935.3.0.CO;2-T>CrossRefGoogle Scholar
Griffiths, T.L., Steyvers, M., & Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114, 211–244.CrossRefGoogle ScholarPubMed
Hampton, J.A. (2000). Concepts and prototypes. Mind and Language, 15, 299–307.CrossRefGoogle Scholar
Heit, E. (1997). Knowledge and concept learning. In Lamberts, K. & Shanks, D. (eds.), Knowledge, Concepts, and Categories (pp. 7–41). London: Psychology Press.Google Scholar
Herrnstein, R.J., & Loveland, D.H. (1964). Complex visual concept in the pigeon. Science, 146, 549–551.CrossRefGoogle ScholarPubMed
Hull, C.L. (1920). Quantitative aspects of the evolution of concepts: an experimental study. Psychological Monographs, 28 (1), Whole No. 123.CrossRefGoogle Scholar
Kurtz, K.J. (2007). The divergent autoencoder (DIVA) model of category learning. Psychonomic Bulletin & Review, 14, 560–576.CrossRefGoogle Scholar
Lamberts, K. (2000). Information-accumulation theory of speeded categorization. Psychological Review, 107, 227–260.CrossRefGoogle ScholarPubMed
Love, B.C., Medin, D.L., & Gureckis, T.M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111, 309–332.CrossRefGoogle ScholarPubMed
McClelland, J.L., & Rumelhart, D.E. (eds.) (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge, MA: MIT Press.Google Scholar
Medin, D.L., & Schaffer, M.M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.CrossRefGoogle Scholar
Medin, D.L., & Schwanenflugel, P.J. (1981). Linear separability in classification learning. Journal of Experimental Psychology: Human Learning and Memory, 75, 355–368.Google Scholar
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.Google Scholar
Murphy, G.L., & Medin, D.L. (1985). The role of theories in conceptual coherence. Psychological Review, 92, 289–316.CrossRefGoogle ScholarPubMed
Navarro, D.J. (2007). Similarity, distance, and categorization: a discussion of Smith's (2006) warning about ‘colliding parameters’. Psychonomic Bulletin & Review, 14, 823–833.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Nosofsky, R.M. (1988). Similarity, frequency, and category representation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 54–65.Google Scholar
Nosofsky, R. M. (1990). Relations between exemplar-similarity and likelihood models of classification. Journal of Mathematical Psychology, 34, 393–418.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Plaut, D.C., & Shallice, T. (1993). Deep dyslexia: a case study of connectionist neuropsychology. Cognitive Neuropsychology, 10, 377–500.CrossRefGoogle Scholar
Plunkett, K., & Bandelow, S. (2006). Stochastic approaches to understanding dissociations in inflectional morphology. Brain and Language, 98, 194–209.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.Google ScholarPubMed
Pothos, E.M., & Chater, N. (2002). A simplicity principle in unsupervised human categorization. Cognitive Science, 26, 303–343.CrossRefGoogle Scholar
Rehder, B. (2003). Categorization as causal reasoning. Cognitive Science, 27, 709–748.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Schyns, P.G. (1991). A modular neural network model of concept acquisition. Cognitive Science, 15, 461–508.CrossRefGoogle Scholar
Shepard, R.N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317–1323.CrossRefGoogle Scholar
Smith, J.D. (2007). When parameters collide: a warning about categorization models. Psychonomic Bulletin & Review, 13, 743–751.CrossRefGoogle Scholar
Tenenbaum, J., & Griffiths, T.L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24, 629–641.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Vanpaemel, W., & Storms, G. (2008). In search of abstraction: the varying abstraction model of categorization. Psychonomic Bulletin & Review, 15, 732–749.CrossRefGoogle ScholarPubMed
Rijsbergen, K. (2004). The Geometry of Information Retrieval. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Zeithamova, D., & Maddox, W.T. (2006). Dual-task interference in perceptual category learning. Memory & Cognition, 34, 387–398.CrossRefGoogle ScholarPubMed
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.Google Scholar

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