Decreased Gray-Matter Volume in Insular Cortex as a Correlate of Singers’ Enhanced Sensorimotor Control of Vocal Production
Bibliographic record
Abstract
Accumulating evidence has shown enhanced sensorimotor control of vocal production as a consequence of extensive singing experience. The neural basis of this ability, however, is poorly understood. Given that the insula mediates motor aspects of vocal production, the present study investigated structural plasticity in insula induced by singing experience and its link to auditory feedback control of vocal production. Voxel-based morphometry (VBM) was used to examine the differences in gray matter (GM) volume in the insula of 21 singers and 21 non-singers. An auditory feedback perturbation paradigm was used to examine the differences in auditory-motor control of vocal production between singers and non-singers. Both groups vocalized sustained vowels while hearing their voice unexpectedly pitch-shifted -50 or -200 cents (200 ms duration). VBM analyses showed that singers exhibited significantly lower GM volumes in the bilateral insula than non-singers. When exposed to pitch perturbations in voice auditory feedback, singers involuntarily compensated for pitch perturbations in voice auditory feedback to a significantly lesser degree than non-singers. Moreover, across the two sizes of pitch perturbations, the magnitudes of vocal compensations were positively correlated with the total regional GM volumes in the bilateral insula. These results indicate that extensive singing training leads to decreased GM volumes in insula and suggest that morphometric plasticity in insula contributes to the enhanced sensorimotor control of vocal production observed in singers.
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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 | 0.000 |
| 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".