Musical Expertise Influences the Processing of Short and Long Auditory Time Intervals: An Electroencephalography Study
Bibliographic record
Abstract
Musical expertise has been proven to be beneficial for time perception abilities, with musicians outperforming nonmusicians in several explicit timing tasks. However, it is unclear how musical expertise impacts implicit time perception. Twenty nonmusicians and 15 expert musicians participated in an EEG recording during a passive auditory oddball paradigm with 0.8- and 1.6-sec standard time intervals and deviant intervals that were either played earlier or delayed relative to the standard interval. We first confirmed that, as was the case for nonmusicians, musicians use different neurofunctional processes to support the perception of short (below 1.2 sec) and long (above 1.2 sec) time intervals: Whereas deviance detection for long intervals elicited a N1 component, P2 was associated with deviance detection for short time intervals. Interestingly, musicians did not elicit a contingent negative variation (CNV) for longer intervals but show additional components of deviance detection such as (i) an attention-related N1 component, even for deviants occurring during short intervals; (ii) a N2 component for above and below 1.2-sec deviance detection, and (iii) a P2 component for above 1.2-sec deviance detection. We propose that the N2 component is a marker of explicit deviance detection and acts as an inhibitory/conflict monitoring of the deviance. This hypothesis was supported by a positive correlation between CNV and N2 amplitudes: The CNV reflects the temporal accumulator and can predict explicit detection of the deviance. In expert musicians, a N2 component is observable without CNV, suggesting that deviance detection is optimized and does not require the temporal accumulator. Overall, this study suggests that musical expertise is associated with optimized implicit time perception.
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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) | 0.002 | 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".