Bayesian Models of Inductive Generalization

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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Neville Sanjana, Joshua Tenenbaum


We argue that human inductive generalization is best explained in a Bayesian framework, rather than by traditional models based on simi- larity computations. We go beyond previous work on Bayesian concept learning by introducing an unsupervised method for constructing flex- ible hypothesis spaces, and we propose a version of the Bayesian Oc- cam’s razor that trades off priors and likelihoods to prevent under- or over-generalization in these flexible spaces. We analyze two published data sets on inductive reasoning as well as the results of a new behavioral study that we have carried out.