Cognitive Science 39 (2):268-306 (2015)

Abstract
It is unclear how children learn labels for multiple overlapping categories such as “Labrador,” “dog,” and “animal.” Xu and Tenenbaum suggested that learners infer correct meanings with the help of Bayesian inference. They instantiated these claims in a Bayesian model, which they tested with preschoolers and adults. Here, we report data testing a developmental prediction of the Bayesian model—that more knowledge should lead to narrower category inferences when presented with multiple subordinate exemplars. Two experiments did not support this prediction. Children with more category knowledge showed broader generalization when presented with multiple subordinate exemplars, compared to less knowledgeable children and adults. This implies a U-shaped developmental trend. The Bayesian model was not able to account for these data, even with inputs that reflected the similarity judgments of children. We discuss implications for the Bayesian model, including a combined Bayesian/morphological knowledge account that could explain the demonstrated U-shaped trend
Keywords Word learning  Bayesian modeling  Vocabulary development  Similarity judgment  Categorization
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DOI 10.1111/cogs.12135
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References found in this work BETA

Word Learning as Bayesian Inference.Fei Xu & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):245-272.

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