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

Kölbl N, Rampp S, Kaltenhäuser M, Tziridis K, Maier A, Kinfe T, Chavarriaga R, Krauß P, Schilling A (2026)

Publication Type: Journal article

Publication year: 2026

Journal

Scientific Reports Nature Publishing Group: Open Access Journals - Option B

Book Volume: 16

Journal Issue: 1

DOI: 10.1038/s41598-026-41532-0

Abstract

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.

Authors with CRIS profile

Nikola Kölbl Department of Otorhinolaryngology – Head and Neck Surgery (HNO-Klinik) Stefan Rampp Department of Neurosurgery Konstantin Tziridis Department of Otorhinolaryngology – Head and Neck Surgery (HNO-Klinik) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Patrick Krauß Department of Otorhinolaryngology – Head and Neck Surgery (HNO-Klinik) Achim Schilling Department of Otorhinolaryngology – Head and Neck Surgery (HNO-Klinik)

Involved external institutions

Universitätsklinikum Mannheim / University Medical Centre Mannheim (Universitätsmedizin Mannheim) DE Germany (DE) Zürcher Hochschule für Angewandte Wissenschaften (ZHAW) CH Switzerland (CH)

How to cite

APA:

Kölbl, N., Rampp, S., Kaltenhäuser, M., Tziridis, K., Maier, A., Kinfe, T.,... Schilling, A. (2026). Prediction, syntax and semantic grounding in the brain and large language models. Scientific Reports, 16(1). https://doi.org/10.1038/s41598-026-41532-0

MLA:

Kölbl, Nikola, et al. "Prediction, syntax and semantic grounding in the brain and large language models." Scientific Reports 16.1 (2026).

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