Disentangling semantic prediction and association in processing filler-gap dependencies: an MEG study in English
Bibliographic record
Abstract
Understanding language is facilitated by prediction of upcoming words. Sentences with filler-gap dependencies can provide sophisticated cues about an upcoming verb. A sentence beginning with which cat did you … ? Is more likely to end with lift than meow. M/EEG recordings show a diverging response ∼200–400 ms (“N400”) after the onset of unpredictable words vs. predictable words, and similarly for pairs of words with high vs. low semantic association. Previous studies report N400 responses to implausible filler-gap dependencies, however it is unclear whether these findings index verb predictability or semantic association between the reactivated filler and verb. We report on an MEG study examining argument-verb relations in sentences with and without filler-gap dependencies, controlling for lexical association between arguments and verbs. Implausible subject-verb relations showed the characteristic response at 200–500 ms in left frontal cortex, and implausible filler-gap at 600–800 ms in right frontal cortex, suggesting different mechanisms for filler-gap dependencies.
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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) | 0.001 | 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".