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Children learn complex language structures without innate knowledge, using inductive biases and domain-general learning capacities. This Bayesian model shows how hierarchical syntax emerges from typical speech input, challenging nativist theories of language acquisition.

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Area of Science:

  • Cognitive Science
  • Linguistics
  • Developmental Psychology

Background:

  • Children acquire language rapidly, inferring complex syntactic rules with limited direct evidence.
  • Nativist theories propose innate linguistic knowledge, such as hierarchical phrase structures, to explain this acquisition.
  • The phenomenon of auxiliary fronting in English questions is a classic example used to support innate syntactic knowledge.

Purpose of the Study:

  • To investigate whether hierarchical phrase structure knowledge is innate or can be learned.
  • To model language acquisition using a Bayesian framework for grammar induction.
  • To challenge the assumption that specific linguistic structures must be innately specified.

Main Methods:

  • Utilized a Bayesian framework for grammar induction.
  • Modeled an ideal learner processing typical child-directed speech.
  • Analyzed the emergence of hierarchical structure recognition from input data.

Main Results:

  • Demonstrated that an ideal learner can infer hierarchical phrase structures without innate specification.
  • Showed that domain-general learning capacities and typical speech input are sufficient.
  • Provided evidence against the necessity of innate knowledge for recognizing complex syntax.

Conclusions:

  • Hierarchical syntactic structure can be learned through general inductive biases and environmental input.
  • Challenges nativist perspectives on language acquisition, suggesting a greater role for learning.
  • Implications for understanding the nature of human language acquisition and the language faculty.