Oscillatory Responses to Semantic and Syntactic Violations
Bibliographic record
Abstract
EEG studies employing time-frequency analysis have revealed changes in theta and alpha power in a variety of language and memory tasks. Semantic and syntactic violations embedded in sentences evoke well-known ERPs, but little is known about the oscillatory responses to these violations. We investigated oscillatory responses to both kinds of violations, while monolingual and bilingual participants performed an acceptability judgment task. Both violations elicited power decreases (event-related desynchronization, ERD) in the 8-30 Hz frequency range, but with different scalp topographies. In addition, semantic anomalies elicited power increases (event-related synchronization, ERS) in the 1-5 Hz frequency band. The 1-5 Hz ERS was strongly phase-locked to stimulus onset and highly correlated with time domain averages, whereas the 8-30 Hz ERD response varied independently of these. In addition, the results showed that language expertise modulated 8-30 Hz ERD for syntactic violations as a function of the executive demands of the task. When the executive function demands were increased using a grammaticality judgment task, bilinguals but not monolinguals demonstrated reduced 8-30 Hz ERD for syntactic violations. These findings suggest a putative role of the 8-30 Hz ERD response as a marker of linguistic processing that likely represents a separate neural process from those underlying ERPs.
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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".