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 help explain how the rest can be acquired. The hierarchical approach also addresses a common question about Bayesian models of cognition: where do the priors come from? To illustrate our claims, we consider two specific 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. Compared to machine-learning algorithms, humans are remarkable for doing so much with so little. A single

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