A simplicity principle in unsupervised human categorization
UNSPECIFIED (2002) A simplicity principle in unsupervised human categorization. COGNITIVE SCIENCE, 26 (3). pp. 303-343. ISSN 0364-0213Full text not available from this repository.
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 of categories sought or information about the distributional properties of the objects to be classified. These features of the simplicity model distinguish it from other models in unsupervised categorization (where, for example, the number of categories sought is determined via a free parameter), and we discuss how these computational differences are related to differences in modeling objectives. The predictions of the simplicity model are validated in four experiments. We also discuss the significance of simplicity in cognitive modeling more generally. (C) 2002 Cognitive Science Society, Inc. All rights reserved.
|Item Type:||Journal Item|
|Subjects:||B Philosophy. Psychology. Religion > BF Psychology|
|Journal or Publication Title:||COGNITIVE SCIENCE|
|Publisher:||ELSEVIER SCIENCE INC|
|Number of Pages:||41|
|Page Range:||pp. 303-343|
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