The probabilistic analysis of language acquisition: theoretical, computational, and experimental analysis

Area of Science:

• Cognitive Science
• Linguistics
• Computational Linguistics

Background:

• Debate on innate language-specific biases versus cognition-general principles in language acquisition.
• Probabilistic language acquisition hypothesis as a key area of investigation.

Purpose of the Study:

• To examine the probabilistic language acquisition hypothesis on theoretical and experimental levels.
• To test learnability predictions for debated linguistic constructions.

Main Methods:

• Theoretical analysis demonstrating learnability of generative language models from samples.
• Introduction of a practical framework to quantify natural language learnability.
• Experimental testing of learnability predictions for specific linguistic constructions.

Main Results:

• A novel theoretical result shows exact generative models of languages are learnable from samples.
• A practical framework quantifies language learnability, enabling specific predictions.
• Experimental data support the probabilistic acquisition of linguistic constructions from general cognitive principles.

Conclusions:

• Findings support the hypothesis that language acquisition can occur via probabilistic, cognition-general mechanisms.
• The study provides a framework and experimental evidence for probabilistic language learning.
• Challenges the necessity of innate, language-specific biases for acquiring complex linguistic structures.