Hypothesis generation, sparse categories, and the positive test strategy

dc.contributor.authorNavarro, D.
dc.contributor.authorPerfors, A.
dc.date.issued2011
dc.description.abstractWe 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.
dc.description.statementofresponsibilityDaniel J. Navarro and Amy F. Perfors
dc.identifier.citationPsychological Review, 2011; 118(1):120-134
dc.identifier.doi10.1037/a0021110
dc.identifier.issn0033-295X
dc.identifier.issn1939-1471
dc.identifier.orcidNavarro, D. [0000-0001-7648-6578]
dc.identifier.urihttp://hdl.handle.net/2440/68459
dc.language.isoen
dc.publisherAmerican Psychological Association
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0773794
dc.relation.isreplacedby2440/90824
dc.relation.isreplacedbyhttp://hdl.handle.net/2440/90824
dc.rights© 2010 American Psychological Association
dc.source.urihttps://doi.org/10.1037/a0021110
dc.subjectactive learning
dc.subjecthypothesis generation
dc.subjecthypothesis testing
dc.subjectpositive test strategy
dc.titleHypothesis generation, sparse categories, and the positive test strategy
dc.typeJournal article
pubs.publication-statusPublished

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