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Learning times for large lexicons through cross-situational learning
Richard A Blythe1, Kenny Smith, Andrew D M Smith
1SUPA, School of Physics and Astronomy, University of Edinburgh Cognition and Communication Research Centre, Department of Psychology, Northumbria University Language Evolution and Computation Research Unit, Linguistics and English Language, University of Edinburgh.
Cognitive Science
|May 14, 2011
Summary
Cross-situational learning enables acquiring large vocabularies even with word meaning uncertainty. Mathematical modeling shows this learning strategy is plausible for human lexicon acquisition within reasonable timeframes.
Area of Science:
- Cognitive Science
- Developmental Psychology
- Computational Linguistics
Background:
- Cross-situational learning is a proposed mechanism for word meaning acquisition.
- Concerns exist about its efficiency for large lexicons given referential uncertainty.
Purpose of the Study:
- To mathematically assess the plausibility of cross-situational learning for large-scale vocabulary acquisition.
- To estimate lexicon learning times under varying uncertainty levels.
Main Methods:
- Developed a mathematical model of cross-situational word learning.
- Analyzed learning dynamics across different learning strategies.
- Estimated time required to build human-scale lexicons.
Main Results:
- Cross-situational learning effectively supports large vocabulary acquisition despite high referential uncertainty.
- Calculated plausible learning times for various cross-situational learning strategies.
- Demonstrated that rapid individual word learning is not a prerequisite for large lexicon capacity.
Conclusions:
- Cross-situational word learning is a viable mechanism for acquiring extensive vocabularies.
- Concerns about unrealistic learning times for cross-situational learning are not supported by the model.
- The capacity for large lexicon acquisition may not depend on the speed of individual word learning.
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