Concept Learning and Representation: Models

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Concept learning describes the process by which experience allows us to partition objects in the world into classes for the purpose of generalization, discrimination, and inference. Models of concept learning have adopted one of three contrasting views concerning category representation. In prototype theories, the concept learning process is assumed to yield an abstract representation corresponding to the central tendency of the category exemplars on each of the dimensions of variation. In exemplar models, the concept is simply the set of mental representations of all of the category exemplars that have been previously observed, with each instance assumed to be stored as a separate trace. In decision rule models, the learner is assumed to construct a boundary or rule in psychological space which partitions it into different category regions. These different models, and some of the evidence supporting each of them, are considered in turn. Next, the role of selective attention in categorization, and the way in which the different models deal with selective attention, is discussed. Evidence that categorization may be controlled by multiple mechanisms is evaluated, and finally the fine-scale dynamics (i.e., time course) and some aspects of the neuropsychology of categorization are reviewed.