Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/68459
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Type: Journal article
Title: Hypothesis generation, sparse categories, and the positive test strategy
Author: Navarro, D.
Perfors, A.
Citation: Psychological Review, 2011; 118(1):120-134
Publisher: American Psychological Association
Issue Date: 2011
ISSN: 0033-295X
1939-1471
Statement of
Responsibility: 
Daniel J. Navarro and Amy F. Perfors
Abstract: We consider the situation in which a learner must induce the rule that explains an observed set of data but the hypothesis space of possible rules is not explicitly enumerated or identified. The first part of the article demonstrates that as long as hypotheses are sparse (i.e., index less than half of the possible entities in the domain) then a positive test strategy is near optimal. The second part of this article then demonstrates that a preference for sparse hypotheses (a sparsity bias) emerges as a natural consequence of the family resemblance principle; that is, it arises from the requirement that good rules index entities that are more similar to one another than they are to entities that do not satisfy the rule.
Keywords: active learning; hypothesis generation; hypothesis testing; positive test strategy
Rights: © 2010 American Psychological Association
RMID: 0020103542
DOI: 10.1037/a0021110
Appears in Collections:Psychology publications

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