When More Evidence Makes Word Learning Less Suspicious

Gavin W. Jenkins, Jodi R. Smith, John P. Spencer, Larissa K. Samuelson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

One challenging problem that children overcome in learning new words is recognizing the hierarchical category of a label. For instance, one object could be called a Dalmatian, a dog, or an animal. Xu and Tenenbaum (2007) proposed a Bayesian model to explain how 3.5 to 5-year-olds solve this ambiguity. They emphasized children's appreciation for “suspicious coincidences:” a label applied to three identical toys is interpreted more narrowly than a label applied to one toy. Xu and Tenenbaum did not investigate children’s prior category knowledge, however. We replicated their “suspicious coincidence” effect and measured this knowledge. Unexpectedly, children with more category knowledge appreciated “suspicious coincidences” less. In a second experiment, repeatedly emphasizing novel labels caused all children to stop recognizing the “suspicious coincidence.” These data are inconsistent with the Bayesian account and suggest the phenomenon is influenced by subtler aspects of prior knowledge and by task-specific details.

Original languageEnglish
Title of host publicationExpanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011
EditorsLaura Carlson, Christoph Hoelscher, Thomas F. Shipley
PublisherThe Cognitive Science Society
Pages2556-2561
Number of pages6
ISBN (Electronic)9780976831877
Publication statusPublished - 2011
Event33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011 - Boston, United States
Duration: 20 Jul 201123 Jul 2011

Publication series

NameExpanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011

Conference

Conference33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011
Country/TerritoryUnited States
CityBoston
Period20/07/1123/07/11

Keywords

  • Bayesian Model
  • Categorization
  • Word Learning

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