ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2023
Dimensionality and Ramping: Signatures of Sentence Integration in the Dynamics of Brains and Deep Language Models.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Review
- Initial organization and progressive expansion of the math-responsive brain network during the first school years.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- GRASP: a response-locked paradigm that reveals low-beta desynchronisation is a participant-invariant signature of semantic decoding in EEG.Frontiers in human neuroscience · 2026Article
- Towards decoding individual words from non-invasive brain recordings.Nature communications · 2025Article
- Hierarchical dynamic coding coordinates speech comprehension in the human brain.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Low-frequency cortical activity reflects context-dependent parsing of word sequences.iScience · 2025Article
- Cortical language areas are coupled via a soft hierarchy of model-based linguistic features.bioRxiv : the preprint server for biology · 2025Article
- Hierarchical dynamic coding coordinates speech comprehension in the human brain.bioRxiv : the preprint server for biology · 2025Article
- Can Large Language Models Generate Useful Linguistic Corpora?: A Case Study of the Word Frequency Effect in Young German Readers.Open mind : discoveries in cognitive science · 2025Article
- Challenges and Methods in Annotating Natural Speech for Neurolinguistic Research.Neurobiology of language (Cambridge, Mass.) · 2025Article
- The human auditory cortex concurrently tracks syllabic and phonemic timescales via acoustic spectral flux.Science advances · 2024Article
- Neural populations in the language network differ in the size of their temporal receptive windows.Nature human behaviour · 2024Article
- Review
- Lesion-symptom Mapping of Acceptability Judgments in Chronic Poststroke Aphasia Reveals the Neurobiological Underpinnings of Receptive Syntax.Journal of cognitive neuroscience · 2024Article
- The language network as a natural kind within the broader landscape of the human brain.Nature reviews. Neuroscience · 2024Review
- Driving and suppressing the human language network using large language models.bioRxiv : the preprint server for biology · 2023Article
- Article
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10 authors.
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Abstract
A sentence is more than the sum of its words: its meaning depends on how they combine with one another. The brain mechanisms underlying such semantic composition remain poorly understood. To shed light on the neural vector code underlying semantic composition, we introduce two hypotheses: (1) the intrinsic dimensionality of the space of neural representations should increase as a sentence unfolds, paralleling the growing complexity of its semantic representation; and (2) this progressive integration should be reflected in ramping and sentence-final signals. To test these predictions, we designed a dataset of closely matched normal and jabberwocky sentences (composed of meaningless pseudo words) and displayed them to deep language models and to 11 human participants (5 men and 6 women) monitored with simultaneous MEG and intracranial EEG. In both deep language models and electrophysiological data, we found that representational dimensionality was higher for meaningful sentences than jabberwocky. Furthermore, multivariate decoding of normal versus jabberwocky confirmed three dynamic patterns: (1) a phasic pattern following each word, peaking in temporal and parietal areas; (2) a ramping pattern, characteristic of bilateral inferior and middle frontal gyri; and (3) a sentence-final pattern in left superior frontal gyrus and right orbitofrontal cortex. These results provide a first glimpse into the neural geometry of semantic integration and constrain the search for a neural code of linguistic composition.
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