Effects of Context on Electrophysiological Response to Musical Accents
Bibliographic record
Abstract
Listeners' aesthetic and emotional responses to music typically occur in the context of long musical passages that contain structures defined in terms of the events that precede them. We describe an electrophysiological study of listeners' brain responses to musical accents that coincided in longer musical sequences. Musically trained listeners performed a timbre-change detection task in which a single-tone timbre change was positioned within 4-bar melodies composed of 350-ms tones to coincide or not with melodic contour accents and temporal accents (induced with temporal gaps). Event-related potential responses to (task-relevant) attended timbre changes elicited an early negativity (MMN/N2b) around 200 ms and a late positive component around 350 ms (P300), reflecting updating of the timbre change in working memory. The amplitudes of both components changed systematically across the sequence, consistent with expectancy-based context effects. Furthermore, melodic contour changes modulated the MMN/N2b response (but not the P300) to timbre changes in later sequence positions. In contrast, task-irrelevant temporal gaps elicited an MMN that was not modulated by position within the context; absence of a P300 indicated that temporal-gap accents were not updated in working memory. Listeners' neural responses to musical structure changed systematically as sequential predictability and listeners' expectations changed across the melodic context.
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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 | 0.000 | 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".