Neurophysiological Correlates of Musical and Prosodic Phrasing: Shared Processing Mechanisms and Effects of Musical Expertise
Bibliographic record
Abstract
The processing of prosodic phrase boundaries in language is immediately reflected by a specific event-related potential component called the Closure Positive Shift (CPS). A component somewhat reminiscent of the CPS in language has also been reported for musical phrases (i.e., the so-called 'music CPS'). However, in previous studies the quantification of the music-CPS as well as its morphology and timing differed substantially from the characteristics of the language-CPS. Therefore, the degree of correspondence between cognitive mechanisms of phrasing in music and in language has remained questionable. Here, we probed the shared nature of mechanisms underlying musical and prosodic phrasing by (1) investigating whether the music-CPS is present at phrase boundary positions where the language-CPS has been originally reported (i.e., at the onset of the pause between phrases), and (2) comparing the CPS in music and in language in non-musicians and professional musicians. For the first time, we report a positive shift at the onset of musical phrase boundaries that strongly resembles the language-CPS and argue that the post-boundary 'music-CPS' of previous studies may be an entirely distinct ERP component. Moreover, the language-CPS in musicians was found to be less prominent than in non-musicians, suggesting more efficient processing of prosodic phrases in language as a result of higher musical expertise.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".