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Related Experiment Videos

Sensitivity to sampling in Bayesian word learning.

Fei Xu1, Joshua B Tenenbaum

  • 1Department of Psychology, University of British Columbia, Vancouver, Canada. fei@psych.ubc.ca

Developmental Science
|April 21, 2007
PubMed
Summary
This summary is machine-generated.

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Children

Area of Science:

  • Cognitive Science
  • Developmental Psychology
  • Computational Linguistics

Background:

  • Word learning is crucial for cognitive development.
  • Existing models struggle to explain context-dependent word acquisition.
  • Bayesian inference offers a potential framework for understanding word learning.

Purpose of the Study:

  • To test if word learning aligns with Bayesian inference principles.
  • To investigate how sampling context influences word generalization.
  • To compare Bayesian models with associative models of word learning.

Main Methods:

  • Participants (children and adults) learned novel object labels in two distinct sampling contexts.
  • Contexts varied based on whether exemplars were teacher-selected or self-selected.

Related Experiment Videos

  • Generalization patterns were analyzed to infer word meaning acquisition.

    • Main Results:

    • Both children and adults exhibited more conservative generalization when labels were provided by a knowledgeable teacher.
    • This conservative generalization (restricting labels to highly similar objects) was absent when learners self-selected examples.
    • Findings suggest learners adjust word meaning inferences based on the perceived reliability of the source.

    Conclusions:

    • The results support the hypothesis that word learning approximates Bayesian inference.
    • Learners adapt their generalization strategies based on the sampling process, consistent with Bayesian principles.
    • This provides evidence against simpler associative models and highlights the role of rational inference in language acquisition.