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Modeling cross-situational word-referent learning: prior questions
1Department of Psychological and Brain Sciences and Program in Cognitive Science, Indiana University, Bloomington, IN 47405, USA. chenyu@indiana.edu
Psychological Review
|January 11, 2012
Summary
Children and adults use statistical learning to understand words. Simulation studies show associative learning can mimic hypothesis testing, highlighting the need to study learning components separately.
Area of Science:
- Cognitive Science
- Developmental Psychology
- Computational Linguistics
Background:
- Humans exhibit remarkable statistical learning abilities, inferring word meanings from ambiguous contexts.
- Existing models explain this via hypothesis testing or associative learning.
Purpose of the Study:
- To compare learning mechanisms of hypothesis-testing and associative models.
- To investigate the interaction between information selection, computation, and decision processes.
Main Methods:
- Simulation studies analyzing variants of hypothesis-testing and associative learning models.
- Examination of model components under different specifications.
Main Results:
- Associative models with more data can replicate hypothesis-testing model performance.
- Interactions between model components allow associative models to mimic hypothesis-testing patterns.
- A tradeoff exists between data input and computational complexity.
Conclusions:
- Human statistical learning likely involves a compositional process.
- Decomposing learning into independently evaluable components is crucial for understanding.
- Both associative and hypothesis-testing mechanisms may contribute to word learning.
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