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Published on: January 29, 2020
Cognitive biases, linguistic universals, and constraint-based grammar learning.
Jennifer Culbertson1, Paul Smolensky, Colin Wilson
1Linguistics Program, Department of English, George Mason University, Fairfax, VA 22030, USA. jculber4@gmu.edu
Topics in Cognitive Science
|May 25, 2013
Summary
Cognitive biases shape language learning, influencing both universal linguistic patterns and individual acquisition. A new Bayesian model explains these biases, from absolute to statistical, integrating typological data and experimental results.
Area of Science:
- Cognitive Science
- Computational Linguistics
- Linguistics
Background:
- Cognitive biases are classical concepts influencing language acquisition.
- Linguistic typology reflects these biases in global language patterns.
- Artificial grammar experiments probe biases in learners.
Purpose of the Study:
- To develop a Bayesian model of cognitive biases in language learning.
- To account for detailed results from artificial grammar experiments on noun-phrase word order.
- To integrate insights from linguistic theory, computational cognitive science, and machine learning.
Main Methods:
- Utilized Optimality Theory as a foundational approach to typology.
- Adapted computational techniques for statistical learning.
- Developed a Bayesian model to represent cognitive biases.
Main Results:
- The Bayesian model successfully explains artificial grammar experiment results on noun-phrase word order.
- Demonstrated that biases can range from absolute (hard) to statistical (soft).
- Showed how language-specific and domain-general biases interact.
Conclusions:
- The proposed model offers a novel synthesis of ideas from multiple scientific domains.
- It accounts for language data across macro-level typological distributions and micro-level individual learning.
- This framework provides a nuanced understanding of the interplay between innate biases and language acquisition.
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