Decoding the Neural Dynamics of Headed Syntactic Structure Building

The brain builds hierarchical phrases during language comprehension; however, the details and dynamics of the phrase-building process remain underspecified. This study directly probes whether the neural code of verb phrases involves reactivating the syntactic property of a key subcomponent (the "head" verb). To this end, we train a part-of-speech sliding-window verb/adverb decoder on EEG signals recorded while 30 participants read sentences in a controlled experiment. The decoder reaches above-chance performance that is spatiotemporally consistent and generalizes to unseen data across sentence positions. Applying the decoder to held-out data yields predicted activation levels for the verbal "head" of a verb phrase at a distant nonhead word (adverb); the critical adverb appeared either at the end of a verb phrase or at a sequentially and lexically matched position with no verb phrase boundary. There is stronger verb activation beginning at ∼600 milliseconds at the critical adverb when it appears at a verb phrase boundary; this effect is not modulated by the strength of conceptual association nor does it reflect word predictability. Time-locked analyses additionally reveal a negativity waveform component and increased beta-delta inter-trial phase coherence, both previously linked to linguistic composition, in a similar time window. With a novel application of neural decoding, our findings delineate the dynamics by which the brain encodes phrasal representations by, in part, reactivating the representation of key subcomponents. We thus establish a link between cognitive accounts of phrasal representations and electrophysiological dynamics.

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Publication Details

Journal
Journal of Neuroscience
Published
2025-03-06
DOI
https://doi.org/10.1523/jneurosci.2126-24.2025
Citations
3
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
7.44
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article

Decoding the Neural Dynamics of Headed Syntactic Structure Building

Jonathan Brennan, Junyuan Zhao 赵隽元, Ruimin Gao 高睿敏
3 citations
Journal of Neuroscience
Neural Networks and Applications
7.44
article

Decoding the Neural Dynamics of Headed Syntactic Structure Building

Jonathan Brennan, Junyuan Zhao 赵隽元, Ruimin Gao 高睿敏
article en
3 citations

Abstract

The brain builds hierarchical phrases during language comprehension; however, the details and dynamics of the phrase-building process remain underspecified. This study directly probes whether the neural code of verb phrases involves reactivating the syntactic property of a key subcomponent (the "head" verb). To this end, we train a part-of-speech sliding-window verb/adverb decoder on EEG signals recorded while 30 participants read sentences in a controlled experiment. The decoder reaches above-chance performance that is spatiotemporally consistent and generalizes to unseen data across sentence positions. Applying the decoder to held-out data yields predicted activation levels for the verbal "head" of a verb phrase at a distant nonhead word (adverb); the critical adverb appeared either at the end of a verb phrase or at a sequentially and lexically matched position with no verb phrase boundary. There is stronger verb activation beginning at ∼600 milliseconds at the critical adverb when it appears at a verb phrase boundary; this effect is not modulated by the strength of conceptual association nor does it reflect word predictability. Time-locked analyses additionally reveal a negativity waveform component and increased beta-delta inter-trial phase coherence, both previously linked to linguistic composition, in a similar time window. With a novel application of neural decoding, our findings delineate the dynamics by which the brain encodes phrasal representations by, in part, reactivating the representation of key subcomponents. We thus establish a link between cognitive accounts of phrasal representations and electrophysiological dynamics.

Journal of NeuroscienceVol. 45(17)
Georgia Institute of Technology (US), University of Michigan (US)
Openalex Percentile: Top 4%
Neural Networks and Applications
7.44
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