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Universal Grammar, statistics or both?

Charles D Yang1

  • 1Department of Linguistics and Psychology, Yale University, 370 Temple Street 302, New Haven, CT 06511, USA. charles.yang@yale.edu

Trends in Cognitive Sciences
|September 29, 2004
PubMed
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Statistical learning alone struggles with word segmentation in realistic language settings. Language acquisition requires integrating phonological knowledge and probabilistic learning for syntactic development.

Area of Science:

  • Developmental Psychology
  • Computational Linguistics
  • Language Acquisition

Background:

  • Recent infant statistical learning studies have intensified the nature vs. nurture debate in language acquisition.
  • Existing models face challenges in explaining how infants acquire complex grammatical structures.

Purpose of the Study:

  • To computationally and developmentally evaluate the efficacy of statistical learning in language acquisition.
  • To propose an integrated model for understanding children's grammar development.

Main Methods:

  • Computational analysis of statistical learning using transitional probabilities in realistic language settings (child-directed English).
  • Developmental perspective examining the Principles and Parameters framework and learning mechanisms.

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Main Results:

  • Statistical learning with transitional probabilities is insufficient for reliable word segmentation in naturalistic speech.
  • Phonological structure knowledge is necessary to constrain statistical learning for successful word segmentation.
  • The Principles and Parameters framework requires a shift from domain-specific triggering to domain-general probabilistic learning.

Conclusions:

  • Effective language acquisition necessitates combining statistical learning with innate linguistic knowledge and phonological constraints.
  • Probabilistic, potentially domain-general, learning mechanisms operating within syntactic parameter spaces offer a more comprehensive explanation for grammar development than simple triggering.