Knowledge partitioning in categorization: constraints on exemplar models

Knowledge partitioning in categorization: constraints on exemplar models (2004)

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by Lee-xieng Yang , Stephan Lew

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Datum	Value	Source
TITLE	Knowledge partitioning in categorization: constraints on exemplar models	INFERENCE
AUTHOR NAME	Lee-xieng Yang	SVM HeaderParse 0.2
AUTHOR ADDR	University of Western Australia	SVM HeaderParse 0.2
AUTHOR NAME	Stephan Lew	SVM HeaderParse 0.2
AUTHOR ADDR	University of Western Australia	SVM HeaderParse 0.2
ABSTRACT	The authors present 2 experiments that establish the presence of knowledge partitioning in perceptual categorization. Many participants learned to rely on a context cue, which did not predict category membership but identified partial boundaries, to gate independent partial categorization strategies. When participants partitioned their knowledge, a strategy used in 1 context was unaffected by knowledge demonstrably present in other contexts. An exemplar model, attentional learning covering map, was shown to be unable to accommodate knowledge partitioning. Instead, a mixture-of-experts model, attention to rules and instances in a unified model (ATRIUM), could handle the results. The success of ATRIUM resulted from its assumption that people memorize not only exemplars but also the way in which they are to be classified. In this article, we address the representation of complex perceptual categories. Contrary to the conventional and widespread assumption that people’s representations are homogeneous and integrated, we show in two experiments that people often master a complex categorization task by forming independent components, or parcels, of knowledge. We also show that once a knowledge	SVM HeaderParse 0.2
YEAR	2004	INFERENCE
CITATIONS	56 found	ParsCit 1.0