volume 10, issue 3, P288-297 2007
DOI: 10.1111/j.1467-7687.2007.00590.x
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Fei Xu, Joshua B. Tenenbaum

Abstract: We report a new study testing our proposal that word learning may be best explained as an approximate form of Bayesian inference (Xu & Tenenbaum, in press). Children are capable of learning word meanings across a wide range of communicative contexts. In different contexts, learners may encounter different sampling processes generating the examples of word–object pairings they observe. An ideal Bayesian word learner could take into account these differences in the sampling process and adjust his/her inferences…

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