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Modeling Structure-Building in the Brain With CCG Parsing and Large Language Models.

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Summary
This summary is machine-generated.

Combinatory categorial grammars (CCGs) better model brain activity during language comprehension than context-free grammars (CFGs). This research shows CCG structure-building uniquely impacts neural signals in the temporal lobe.

Keywords:
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Area of Science:

  • Neuroscience
  • Computational Linguistics
  • Cognitive Science

Background:

  • Natural language processing and machine learning tools are increasingly used to model language comprehension.
  • Context-free grammars (CFGs) have been predominantly used for syntactic structure modeling, but lack the expressiveness for human languages.
  • Combinatory categorial grammars (CCGs) offer greater expressiveness and directly compositional models for incremental interpretation.

Purpose of the Study:

  • To evaluate if a more expressive CCG provides a better model of human neural signals than a CFG during naturalistic language comprehension.
  • To compare different CCG variants in their ability to model neural data.
  • To differentiate the neural correlates of grammatical structure-building from word predictability.

Main Methods:

  • Functional magnetic resonance imaging (fMRI) was used to collect neural signals from participants listening to an audiobook.
  • The study compared the fit of CCG-derived measures against CFG-derived measures and a baseline of next-word predictability from a transformer model.
  • CCG variants differing in adjunct handling were tested.

Main Results:

  • CCG-derived measures showed a superior fit to neural signals compared to CFG-derived measures, particularly in the left posterior temporal lobe.
  • Neural effects related to CCG structure-building were spatially distinct from bilateral superior temporal effects associated with predictability.
  • The findings suggest that grammatical structure-building has separable neural effects from predictability during naturalistic listening.

Conclusions:

  • More expressive grammars like CCGs offer a better model for the neural basis of language comprehension than less expressive CFGs.
  • Grammatical structure-building and word predictability engage distinct neural mechanisms.
  • The study highlights the importance of grammar's expressive power in understanding the neural processing of language.