Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/55320
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dc.contributor.authorKemp, C.-
dc.contributor.authorPerfors, A.-
dc.contributor.authorTenenbaum, J.-
dc.date.issued2007-
dc.identifier.citationDevelopmental Science, 2007; 10(3):307-321-
dc.identifier.issn1363-755X-
dc.identifier.issn1467-7687-
dc.identifier.urihttp://hdl.handle.net/2440/55320-
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.language.isoen-
dc.publisherWiley-Blackwell Publishing-
dc.source.urihttp://dx.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-
dc.identifier.doi10.1111/j.1467-7687.2007.00585.x-
pubs.publication-statusPublished-
Appears in Collections:Aurora harvest 5
Psychology publications

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