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A probabilistic computational model of cross-situational word learning
Afsaneh Fazly1, Afra Alishahi, Suzanne Stevenson
1Department of Computer Science, University of Toronto Department of Computational Linguistics, Saarland University.
Cognitive Science
|May 14, 2011
Summary
This study introduces a computational model demonstrating that children can learn word meanings using general cognitive abilities and probabilistic learning from language input, without needing specialized word-learning mechanisms.
Area of Science:
- Cognitive Science
- Developmental Psychology
- Computational Linguistics
Background:
- Word learning is crucial for language acquisition in children.
- Theories debate whether specialized mechanisms or general cognitive abilities drive early word learning.
- Understanding the efficiency and patterns of children's vocabulary acquisition remains a key research question.
Purpose of the Study:
- To present a novel computational model of early word learning.
- To investigate the mechanisms underlying efficient vocabulary acquisition in young children.
- To determine if general cognitive abilities are sufficient for learning word meanings.
Main Methods:
- Developed a computational model simulating early word learning.
- The model uses probabilistic associations between words and semantic elements.
- Employed an incremental learning mechanism relying solely on general cognitive abilities.
Main Results:
- The model successfully learned word meanings from child-directed speech without special biases.
- Demonstrated that general cognitive abilities are adequate for significant vocabulary acquisition.
- Showed that developmental changes in learning mechanisms are not required.
Conclusions:
- Early word learning can be explained by general cognitive mechanisms and probabilistic learning.
- The model provides insights into existing child experimental data and predicts future behavior.
- Suggests that specialized word-learning biases may not be necessary for initial vocabulary acquisition.
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