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<p>Language comprehension involves continuous anticipation of upcoming linguistic input, requiring the rapid integration of syntactic structure and semantic information. To capture the spatio-temporal dynamics of such anticipatory processes during naturalistic language comprehension, we combined electroencephalography (EEG) and magnetoencephalography (MEG), leveraging their complementary sensitivities and high temporal resolution. Using this combined EEG-MEG approach, we investigated word-class-specific neural responses during continuous speech perception and related these findings to word class-level predictability and representational structure in a large language model. Twenty-nine healthy participants listened to a German audio book while their neural responses were recorded. Event-related fields and event-related potentials for different word classes showed highly reproducible, characteristic spatio-temporal signatures, including significant pre-onset activity for nouns, suggesting enhanced anticipatory processing of this word class. Source-space analyses revealed activity patterns extending beyond temporal regions into areas compatible with sensorimotor cortices, suggesting a deeper semantic grounding of nouns in e.g. sensory experiences than verbs. By analyzing word class-specific predictability and representational structure in the transformer-based language model Llama, we provide a computational reference frame that complements the neural findings at the level of word classes. These findings highlight the power of simultaneous MEG-EEG recordings in unraveling the predictive, syntactic, and semantic mechanisms that underlie language comprehension.</p>

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Prediction, syntax and semantic grounding in the brain and large language models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月10日

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  • Nikola Kölbl,
  • Stefan Rampp,
  • Martin Kaltenhäuser,
  • Konstantin Tziridis,
  • Andreas Maier,
  • Thomas Kinfe,
  • Ricardo Chavarriaga,
  • Patrick Krauss,
  • Achim Schilling

摘要

Language comprehension involves continuous anticipation of upcoming linguistic input, requiring the rapid integration of syntactic structure and semantic information. To capture the spatio-temporal dynamics of such anticipatory processes during naturalistic language comprehension, we combined electroencephalography (EEG) and magnetoencephalography (MEG), leveraging their complementary sensitivities and high temporal resolution. Using this combined EEG-MEG approach, we investigated word-class-specific neural responses during continuous speech perception and related these findings to word class-level predictability and representational structure in a large language model. Twenty-nine healthy participants listened to a German audio book while their neural responses were recorded. Event-related fields and event-related potentials for different word classes showed highly reproducible, characteristic spatio-temporal signatures, including significant pre-onset activity for nouns, suggesting enhanced anticipatory processing of this word class. Source-space analyses revealed activity patterns extending beyond temporal regions into areas compatible with sensorimotor cortices, suggesting a deeper semantic grounding of nouns in e.g. sensory experiences than verbs. By analyzing word class-specific predictability and representational structure in the transformer-based language model Llama, we provide a computational reference frame that complements the neural findings at the level of word classes. These findings highlight the power of simultaneous MEG-EEG recordings in unraveling the predictive, syntactic, and semantic mechanisms that underlie language comprehension.