Lists with and without syntax: A new approach to measuring the neural processing of syntax
Bibliographic record
Abstract
ABSTRACT In the neurobiology of language, a fundamental challenge is deconfounding syntax from semantics. Changes in syntactic structure usually correlate with changes in meaning. We approached this challenge from a new angle. We deployed word lists, which are usually the unstructured control in studies of syntax, as both the test and the control stimulus. Three-noun lists ( lamps, dolls, guitars ) were embedded in sentences ( The eccentric man hoarded lamps, dolls, guitars …) and in longer lists ( forks, pen, toilet, rodeo, graves, drums, mulch, lamps, dolls, guitars …). This allowed us to perfectly control both lexical characteristics and local combinatorics: the same words occurred in both conditions and in neither case did the list items locally compose into phrases (e.g. ‘ lamps ’ and ‘ dolls ’ do not form a phrase). But in one case, the list partakes in a syntactic tree, while in the other, it does not. Being embedded inside a syntactic tree increased source-localized MEG activity at ~250-300ms from word onset in the left inferior frontal cortex, at ~300-350ms in the left anterior temporal lobe and, most reliably, at ~330-400ms in left posterior temporal cortex. In contrast, effects of semantic association strength, which we also varied, localized in left temporo-parietal cortex, with high associations increasing activity at around 400ms. This dissociation offers a novel characterization of the structure vs. meaning contrast in the brain: The fronto-temporal network that is familiar from studies of sentence processing can be driven by the sheer presence of global sentence structure, while associative semantics has a more posterior neural signature. SIGNIFICANCE STATEMENT Human languages all have a syntax, which both enables the infinitude of linguistic creativity and determines what is grammatical in a language. The neurobiology of syntactic processing has, however, been challenging to characterize despite decades of study. One reason is pure manipulations of syntax are difficult to design. The approach here offers a perfect control of two variables that are notoriously hard to keep constant when syntax is manipulated: word meaning and phrasal combinatorics. The same noun lists occurred inside longer lists and sentences, while semantic associations also varied. Our MEG results show that classic fronto-temporal language regions can be driven by sentence structure even when local semantic contributions are absent. In contrast, the left temporo-parietal junction tracks associative relationships.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".