Auditory and Visual Interhemispheric Communication in Musicians and Non-Musicians
Bibliographic record
Abstract
The corpus callosum (CC) is a brain structure composed of axon fibres linking the right and left hemispheres. Musical training is associated with larger midsagittal cross-sectional area of the CC, suggesting that interhemispheric communication may be faster in musicians. Here we compared interhemispheric transmission times (ITTs) for musicians and non-musicians. ITT was measured by comparing simple reaction times to stimuli presented to the same hemisphere that controlled a button-press response (uncrossed reaction time), or to the contralateral hemisphere (crossed reaction time). Both visual and auditory stimuli were tested. We predicted that the crossed-uncrossed difference (CUD) for musicians would be smaller than for non-musicians as a result of faster interhemispheric transfer times. We did not expect a difference in CUDs between the visual and auditory modalities for either musicians or non-musicians, as previous work indicates that interhemispheric transfer may happen through the genu of the CC, which contains motor fibres rather than sensory fibres. There were no significant differences in CUDs between musicians and non-musicians. However, auditory CUDs were significantly smaller than visual CUDs. Although this auditory-visual difference was larger in musicians than non-musicians, the interaction between modality and musical training was not significant. Therefore, although musical training does not significantly affect ITT, the crossing of auditory information between hemispheres appears to be faster than visual information, perhaps because subcortical pathways play a greater role for auditory interhemispheric transfer.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".