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