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Learning overhypotheses with hierarchical Bayesian models

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dc.contributor.authorKemp, C.
dc.contributor.authorPerfors, A.
dc.contributor.authorTenenbaum, J.
dc.date.issued2007
dc.description.abstractInductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models can help to explain how the rest are acquired. To illustrate this claim, we develop models that acquire two kinds of overhypotheses – overhypotheses about feature variability (e.g. the shape bias in word learning) and overhypotheses about the grouping of categories into ontological kinds like objects and substances.
dc.description.statementofresponsibilityCharles Kemp, Amy Perfors and Joshua B. Tenenbaum
dc.identifier.citationDevelopmental Science, 2007; 10(3):307-321
dc.identifier.doi10.1111/j.1467-7687.2007.00585.x
dc.identifier.issn1363-755X
dc.identifier.issn1467-7687
dc.identifier.urihttp://hdl.handle.net/2440/55320
dc.language.isoen
dc.publisherWiley-Blackwell Publishing
dc.source.urihttps://doi.org/10.1111/j.1467-7687.2007.00585.x
dc.subjectHumans
dc.subjectBayes Theorem
dc.subjectLanguage Development
dc.subjectCognition
dc.subjectVerbal Learning
dc.subjectConcept Formation
dc.subjectModels, Psychological
dc.subjectGeneralization, Psychological
dc.titleLearning overhypotheses with hierarchical Bayesian models
dc.typeJournal article
pubs.publication-statusPublished

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