Neural correlates of audiovisual integration in music reading
Bibliographic record
Abstract
Integration of auditory and visual information is important to both language and music. In the linguistic domain, audiovisual integration alters event-related potentials (ERPs) at early stages of processing (the mismatch negativity (MMN)) as well as later stages (P300(Andres et al., 2011)). However, the role of experience in audiovisual integration is unclear, as reading experience is generally confounded with developmental stage. Here we tested whether audiovisual integration of music appears similar to reading, and how musical experience altered integration. We compared brain responses in musicians and non-musicians on an auditory pitch-interval oddball task that evoked the MMN and P300, while manipulating whether visual pitch-interval information was congruent or incongruent with the auditory information. We predicted that the MMN and P300 would be largest when both auditory and visual stimuli deviated, because audiovisual integration would increase the neural response when the deviants were congruent. The results indicated that scalp topography differed between musicians and non-musicians for both the MMN and P300 response to deviants. Interestingly, musicians' musical training modulated integration of congruent deviants at both early and late stages of processing. We propose that early in the processing stream, visual information may guide interpretation of auditory information, leading to a larger MMN when auditory and visual information mismatch. At later attentional stages, integration of the auditory and visual stimuli leads to a larger P300 amplitude. Thus, experience with musical visual notation shapes the way the brain integrates abstract sound-symbol pairings, suggesting that musicians can indeed inform us about the role of experience in audiovisual integration.
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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".