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This study introduces an ACT-R model explaining syntactic priming in language production, accounting for both short-term priming and long-term adaptation using established cognitive mechanisms. The model successfully simulates key phenomena, including the inverse frequency interaction and lexical boost effects.

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Area of Science:

  • Psycholinguistics
  • Cognitive Science
  • Computational Linguistics

Background:

  • Syntactic priming in language production exhibits both short-term and long-term adaptation effects.
  • Existing theories struggle to account for both phenomena simultaneously.
  • Lexicalized syntactic theory provides a framework for understanding priming as lexical access facilitation.

Purpose of the Study:

  • To develop and validate an ACT-R model that explains both short-term syntactic priming and long-term adaptation.
  • To integrate established ACT-R mechanisms (base-level learning, spreading activation) into a computational model of syntactic priming.
  • To account for specific empirical observations, including the inverse frequency interaction and the lexical boost effect.

Main Methods:

  • Development of a wide-coverage, lexicalized syntactic theory within the ACT-R cognitive architecture.
  • Simulation of incremental language production using the proposed ACT-R model.
  • Conducting modeling studies to test the model's ability to replicate known syntactic priming phenomena.
  • Analysis of corpus data to verify model predictions regarding the lexical boost effect.

Main Results:

  • The ACT-R model successfully accounts for the inverse frequency interaction in syntactic priming.
  • The model explains the absence of decay in long-term adaptation and its cumulative nature.
  • The model accurately predicts and explains the lexical boost effect, specifically its application to short-term priming.
  • Corpus data confirmed the model's prediction that the lexical boost impacts all lexical material, not solely lexical heads.

Conclusions:

  • The presented ACT-R model provides a unified explanation for both short-term and long-term syntactic adaptation effects in language production.
  • The model's success highlights the utility of integrating cognitive architecture mechanisms with linguistic theory for understanding language processing.
  • The findings offer a computational framework for future research in psycholinguistics and language production.