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  1. Categories and Concepts.Edward E. Smith & L. Douglas - 1981 - Harvard University Press.
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  • The adaptive nature of human categorization.John R. Anderson - 1991 - Psychological Review 98 (3):409-429.
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  • (1 other version)Theory-based Bayesian models of inductive learning and reasoning.Joshua B. Tenenbaum, Thomas L. Griffiths & Charles Kemp - 2006 - Trends in Cognitive Sciences 10 (7):309-318.
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  • ALCOVE: An exemplar-based connectionist model of category learning.John K. Kruschke - 1992 - Psychological Review 99 (1):22-44.
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  • "Schema abstraction" in a multiple-trace memory model.Douglas L. Hintzman - 1986 - Psychological Review 93 (4):411-428.
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  • Can semi-supervised learning explain incorrect beliefs about categories?Charles W. Kalish, Timothy T. Rogers, Jonathan Lang & Xiaojin Zhu - 2011 - Cognition 120 (1):106-118.
    Three experiments with 88 college-aged participants explored how unlabeled experiences—learning episodes in which people encounter objects without information about their category membership—influence beliefs about category structure. Participants performed a simple one-dimensional categorization task in a brief supervised learning phase, then made a large number of unsupervised categorization decisions about new items. In all three experiments, the unsupervised experience altered participants’ implicit and explicit mental category boundaries, their explicit beliefs about the most representative members of each category, and even their memory (...)
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  • Image-based object recognition in man, monkey and machine.Michael J. Tarr & Heinrich H. Bülthoff - 1998 - Cognition 67 (1-2):1-20.
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  • On the genesis of abstract ideas.M. I. Posner & S. W. Keele - 1968 - Journal of Experimental Psychology 77 (2p1):353-363.
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  • A simplicity principle in unsupervised human categorization.Emmanuel M. Pothos & Nick Chater - 2002 - Cognitive Science 26 (3):303-343.
    We address the problem of predicting how people will spontaneously divide into groups a set of novel items. This is a process akin to perceptual organization. We therefore employ the simplicity principle from perceptual organization to propose a simplicity model of unconstrained spontaneous grouping. The simplicity model predicts that people would prefer the categories for a set of novel items that provide the simplest encoding of these items. Classification predictions are derived from the model without information either about the number (...)
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  • Categories and Concepts: Theoretical Views and Inductive Data Analysis.Iven van Mechelen, James Hampton, Ryszard S. Michalski & Peter Theuns (eds.) - 1993 - Academic Press.
    A book aimed at advanced undergraduates and graduates in cognitive science and artificial intelligence, linguistics, applied mathematics and data analysis.
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  • What attributes guide the deployment of visual attention and how do they do it?J. M. Wolfe & T. S. Horowitz - 2004 - Nature Reviews Neuroscience 5 (6):495-501.
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  • An exemplar-based random walk model of speeded classification.Robert M. Nosofsky & Thomas J. Palmeri - 1997 - Psychological Review 104 (2):266-300.
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  • Semi-supervised learning is observed in a speeded but not an unspeeded 2D categorization task.Timothy T. Rogers, Charles Kalish, Bryan R. Gibson, Joseph Harrison & Xiaojin Zhu - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
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  • Context theory of classification learning.Douglas L. Medin & Marguerite M. Schaffer - 1978 - Psychological Review 85 (3):207-238.
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  • Alcove-an exemplar-based connectionist model of category learning.Jk Kruschke & Rm Nosofsky - 1991 - Bulletin of the Psychonomic Society 29 (6):475-475.
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  • SUSTAIN: A Network Model of Category Learning.Bradley C. Love, Douglas L. Medin & Todd M. Gureckis - 2004 - Psychological Review 111 (2):309-332.
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