title: Learning overhypotheses with hierarchical Bayesian models.
abstract: Inductive 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.
authors: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. ckemp@mit.edu
authors: Charles
authors: C
authors: Kemp
authors: Amy
authors: A
authors: Perfors
authors: Joshua B
authors: JB
authors: Tenenbaum
mesh_terms: D001499:Bayes Theorem; D003071:Cognition / Q000502:physiology*; D003210:Concept Formation / Q000502:physiology*; D005793:Generalization, Psychological / Q000502:physiology*; D006801:Humans; D007804:Language Development; D008960:Models, Psychological; D014706:Verbal Learning / Q000502:physiology*