Disentangling semantic prediction and association in processing filler-gap dependencies: an MEG study in English
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it