nips nips2002 nips2002-40 nips2002-40-reference knowledge-graph by maker-knowledge-mining
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Author: Neville E. Sanjana, Joshua B. Tenenbaum
Abstract: We argue that human inductive generalization is best explained in a Bayesian framework, rather than by traditional models based on similarity computations. We go beyond previous work on Bayesian concept learning by introducing an unsupervised method for constructing flexible hypothesis spaces, and we propose a version of the Bayesian Occam’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.
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[11] J. Tenenbaum and F. Xu. Word learning as Bayesian inference. In Proceedings of the 22nd Annual Conference of the Cognitive Science Society, 2000. Bayes 1 General: mammals n=3 ρ = 0.94 0.5 0 0 1 0 0 0.5 1 0 0 0.5 1 0.5 1 0 0 0.5 1 ρ = 0.87 0.5 0 0.2 0.4 0.6 0.8 0 0 1 ρ = 0.93 0.5 0 0 1 ρ = 0.91 1 ρ = 0.97 ρ = _ 0.33 0.5 0.5 0.5 0 ρ = 0.87 1 ρ = 0.97 1 Specific: horse n=1,2,3 0.5 0.5 0 Sum−Similarity 1 0.5 1 Specific: horse n=2 Max−Similarity 1 0.5 1 ρ = 0.39 0.5 0 0.2 0.4 0.6 0.8 0 0 1 2 3 Figure 3: Model predictions ( -axis) plotted against human confirmation scores ( -axis). Each ¡ column shows the results for a particular model. Each row is a different inductive generalization experiment, where indicates the number of examples (premises) in the stimuli. ¢