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Published online by Cambridge University Press:  05 June 2012

Emmanuel M. Pothos
Swansea University
Andy J. Wills
University of Exeter
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The COVIS model of category learning assumes separate rule-based and procedural-learning categorization systems that compete for access to response production. The rule-based system selects and tests simple verbalizable hypotheses about category membership. The procedural-learning system gradually associates categorization responses with regions of perceptual space via reinforcement learning.

Description and motivation of COVIS

Despite the obvious importance of categorization to survival, and the varied nature of category-learning problems facing every animal, research on category learning has been narrowly focused (e.g., Markman & Ross, 2003). For example, the majority of category-learning studies have focused on situations in which two categories are relevant, the motor response is fixed, the nature and timing of feedback is constant (or ignored), and the only task facing the participant is the relevant categorization problem.

One reason for this narrow focus is that until recently, the goal of most categorization research has been to test predictions from purely cognitive models that assume a single category-learning system. In typical applications, the predictions of two competing single-system models were pitted against each other and simple goodness-of-fit was used to select a winner (Maddox & Ashby, 1993; McKinley & Nosofsky, 1995; Smith & Minda, 1998). During the past decade, however, two developments have begun to alter this landscape.

First, there are now many results suggesting that human categorization is mediated by multiple category-learning systems (Ashby & O'Brien, 2005; Ashby et al., 1998; Erickson & Kruschke, 1998; Love, Medin, & Gureckis, 2004; Reber et al., 2003).

Publisher: Cambridge University Press
Print publication year: 2011

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Allen, S.W., & Brooks, L.R. (1991). Specializing the operation of an explicit rule. Journal of Experimental Psychology: General, 120, 3–19.CrossRefGoogle Scholar
Arbuthnott, G.W., Ingham, C.A., & Wickens, J.R. (2000). Dopamine and synaptic plasticity in the neostriatum. Journal of Anatomy, 196, 587–596.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Ashby, F.G., Ell, S.W., & Waldron, E.M. (2003). Procedural learning in perceptual categorization. Memory & Cognition, 31, 1114–1125.CrossRefGoogle ScholarPubMed
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.Google Scholar
Ashby, F.G., Ennis, J.M., & Spiering, B.J. (2007). A neurobiological theory of automaticity in perceptual categorization. Psychological Review, 114, 632–656.CrossRefGoogle ScholarPubMed
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.Google ScholarPubMed
Ashby, F.G., & Maddox, W.T. (2005). Human category learning. Annual Review of Psychology, 56, 149–178.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Ashby, F.G., & O'Brien, J.B. (2005). Category learning and multiple memory systems. Trends in Cognitive Sciences, 9, 83–89.CrossRefGoogle ScholarPubMed
Ashby, F.G., & Valentin, V.V. (2005). Multiple systems of perceptual category learning: theory and cognitive tests. In Cohen, H. & Lefebvre, C. (eds.), Categorization in Cognitive Science. New York: Elsevier.Google Scholar
Ashby, F.G., & Waldron, E.M. (1999). On the nature of implicit categorization. Psychonomic Bulletin & Review, 6, 363–378.CrossRefGoogle ScholarPubMed
Ashby, F.G., & Waldschmidt, J.G. (2008). Fitting computational models to fMRI data. Behavior Research Methods, 40, 713–721.CrossRefGoogle Scholar
Bayer, H.M., & Glimcher, P.W. (2005). Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129–141.CrossRefGoogle ScholarPubMed
Brooks, L. (1978). Nonanalytic Concept Formation and Memory for Instances. Hillsdale, NJ: Erlbaum.Google Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
DeGutis, J., & D'Esposito, M. (2007). Distinct mechanisms in visual category learning. Cognitive, Affective, & Behavioral Neuroscience, 7 (3), 251–259.CrossRefGoogle ScholarPubMed
Erickson, M.A., & Kruschke, J.K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 127, 107–140.CrossRefGoogle ScholarPubMed
Filoteo, J. V., & Maddox, W.T. (2007). Category learning in Parkinson's disease. In Sun, M.K. (ed.), Research Progress in Alzheimer's Disease and Dementia (pp. 339–365). Nova Sciences Publishers.Google Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Hazeltine, E., & Ivry, R. (2002). Motor skill. In Ramachandran, V. (ed.), Encyclopedia of the Human Brain (pp. 183–200). San Diego, CA: Academic Press.Google Scholar
Kincaid, A.E., Zheng, T., & Wilson, C.J. (1998). Connectivity and convergence of single corticostriatal axons. Journal of Neuroscience, 18, 4722–4731.CrossRefGoogle ScholarPubMed
Kruschke, J.K. (1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological Review, 99, 22–44.CrossRefGoogle ScholarPubMed
Lees, A.J., & Smith, F. (1983). Cognitive deficits in the early stages of Parkinson's disease. Brain, 106, 257–270.CrossRefGoogle ScholarPubMed
Lisman, J., Schulman, H., & Cline, H. (2002). The molecular basis of CaMKII function in synaptic and behavioural memory. Nature Reviews Neuroscience, 3, 175–190.CrossRefGoogle ScholarPubMed
Love, B.C., Medin, D.L., & Gureckis, T.M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111 (2), 309–332.CrossRefGoogle ScholarPubMed
Maddox, W.T., & Ashby, F.G. (1993). Comparing decision bound and exemplar models of categorization. Perception & Psychophysics, 53, 49–70.CrossRefGoogle ScholarPubMed
Maddox, W. T., (2004). Dissociating explicit and procedural-learning based systems of perceptual category learning. Behavioural Processes, 66 (3), 309–332.CrossRefGoogle ScholarPubMed
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.Google ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Maddox, W.T., & Filoteo, J.V. (2005). The neuropsychology of perceptual category learning. In Cohen, H. & Lefebvre, C. (eds.), Handbook of Categorization in Cognitive Science (pp. 573–599). Amsterdam: Elsevier.Google Scholar
Maddox, W. T., (2007). Modeling visual attention and category learning in amnesiacs, striatal-damaged patients and normal aging. In Neufeld, R.W.J. (ed.), Advances in Clinical Cognitive Science: Formal Modeling and Assessment of Processes and Symptoms (pp. 113–146). Washington DC: American Psychological Association.Google Scholar
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
Markman, A.B., & Ross, B.H. (2003). Category use and category learning. Psychological Bulletin, 129 (4), 592–613.CrossRefGoogle ScholarPubMed
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.Google ScholarPubMed
Medin, D.L., & Schaffer, M.M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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
Nosofsky, R.M., Palmeri, T.J., & McKinley, S.C. (1994). A rule-plus-exception model of classification learning. Psychological Review, 101, 53–79.CrossRefGoogle ScholarPubMed
Posner, M.I., & Keele, S.W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353–363.CrossRefGoogle ScholarPubMed
Price, A., Filoteo, J.V., & Maddox, W.T. (2009). Rule-based category learning in patients with Parkinson's disease. Neuropsychologia, 47 (5), 1213– 1226.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Rescorla, R.A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. In Black, A.H. & Prokasy, W.F. (eds.), Classical Conditioning II: Current Research and Theory (pp. 64–99). New York: Appleton-Century-Crofts.Google Scholar
Reynolds, J.N.J., & Wickens, J.R. (2002). Dopamine-dependent plasticity of corticostriatal synapses. Neural Networks, 15, 507–521.CrossRefGoogle ScholarPubMed
Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2, 1019–1025.CrossRefGoogle ScholarPubMed
Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275, 1593–1599.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Seger, C.A., & Cincotta, C.M. (2005). The roles of the caudate nucleus in human classification learning. Journal of Neuroscience, 25 (11), 2941– 2951.CrossRefGoogle ScholarPubMed
Seger, C. A., (2006). Dynamics of frontal, striatal, and hippocampal systems during rule learning. Cerebral Cortex, 16 (11), 1546–1555.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.Google Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Willingham, D.B. (1998). A neuropsychological theory of motor skill learning. Psychological Review, 105, 558–584.CrossRefGoogle ScholarPubMed
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.Google ScholarPubMed
Zeithamova, D., & Maddox, W.T. (2006). Dual task interference in perceptual category learning. Memory & Cognition, 34 (2), 387–398.CrossRefGoogle ScholarPubMed

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    • By
  • Edited by Emmanuel M. Pothos, Swansea University, Andy J. Wills, University of Exeter
  • Book: Formal Approaches in Categorization
  • Online publication: 05 June 2012
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    • By
  • Edited by Emmanuel M. Pothos, Swansea University, Andy J. Wills, University of Exeter
  • Book: Formal Approaches in Categorization
  • Online publication: 05 June 2012
  • Chapter DOI:
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