Isolating syntax in natural language: MEG evidence for an early contribution of left posterior temporal cortex
Bibliographic record
Abstract
ABSTRACT Syntax is the engine that allows us to create an infinitude of linguistic expressions, and the construction of syntactic structures, such as noun phrases and verb phrases, is considered a fundamental component of language processing. Nevertheless, insights concerning the neurobiological basis of syntax have remained elusive, in part because it is difficult to isolate syntax from semantic composition. Consequently, many studies of syntax have relied on meaningless artificial stimuli, such as jabberwocky expressions or artificial grammars. However, while pure manipulations of syntax are challenging to design, natural language grammars do have a sparse set of constructions presenting this possibility. Here we examined one such case, English post-nominal adjectives (mountain TALL enough for a strenuous hike), which were contrasted with semantically parallel but structurally simpler noun-adjective sequences in an MEG experiment. We observed a sharp activity increase in the left posterior temporal lobe (PTL) when syntactic composition was more straightforward, approximately 200 ms after adjective onset. The semantic fit between the noun and adjective was also varied, but this affected anterior temporal cortex, consistent with prior work. These findings offer a unique demonstration of the relevance of posterior temporal cortex for syntactic processing in natural language. We also present connectivity evidence that the syntax-related PTL responses were relayed to ipsilateral inferior frontal and anterior temporal regions. The combined results draw an initial picture of the rapid spatio-temporal dynamics of the syntactic and semantic composition network in sentence processing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".