期刊
COGNITIVE SCIENCE
卷 39, 期 2, 页码 268-306出版社
WILEY
DOI: 10.1111/cogs.12135
关键词
Word learning; Bayesian modeling; Categorization; Vocabulary development; Similarity judgment
资金
- Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program
- Eunice Kennedy Shriver National Institute of Child Health & Human Development [R01HD045713]
- EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT [R01HD045713] Funding Source: NIH RePORTER
It is unclear how children learn labels for multiple overlapping categories such as Labrador, dog, and animal. Xu and Tenenbaum (2007a) 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 modelthat 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.
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