Linguistic Bias Modulates Interpretation of Speech via Neural Delta-Band Oscillations
Bibliographic record
Abstract
Language comprehension requires that single words be grouped into syntactic phrases, as words in sentences are too many to memorize individually. In speech, acoustic and syntactic grouping patterns mostly align. However, when ambiguous sentences allow for alternative grouping patterns, comprehenders may form phrases that contradict speech prosody. While delta-band oscillations are known to track prosody, we hypothesized that linguistic grouping bias can modulate the interpretational impact of speech prosody in ambiguous situations, which should surface in delta-band oscillations when grouping patterns chosen by comprehenders differ from those indicated by prosody. In our auditory electroencephalography study, the interpretation of ambiguous sentences depended on whether an identical word was either followed by a prosodic boundary or not, thereby signaling the ending or continuation of the current phrase. Delta-band oscillatory phase at the critical word should reflect whether participants terminate a phrase despite a lack of acoustic boundary cues. Crossing speech prosody with participants' grouping choice, we observed a main effect of grouping choice-independent of prosody. An internal linguistic bias for grouping words into phrases can thus modulate the interpretational impact of speech prosody via delta-band oscillatory phase.
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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 | 0.003 |
| 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".