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Developmental Science2
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Tenenbaum, Joshua B.2
Kemp, Charles1
Perfors, Amy1
Xu, Fei1
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Xu, Fei; Tenenbaum, Joshua B. – Developmental Science, 2007
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…
Descriptors: Semantics, Bayesian Statistics, Sampling, Inferences
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Kemp, Charles; Perfors, Amy; Tenenbaum, Joshua B. – Developmental Science, 2007
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 can help to explain how the rest are acquired. To illustrate this claim, we develop models that acquire two kinds of…
Descriptors: Bayesian Statistics, Logical Thinking, Models, Statistical Analysis