Successful aging of musicians: Preservation of sensorimotor regions aids audiovisual speech-in-noise perception
Bibliographic record
Abstract
Musicianship can mitigate age-related declines in audiovisual speech-in-noise perception. We tested whether this benefit originates from functional preservation or functional compensation by comparing fMRI responses of older musicians, older nonmusicians, and young nonmusicians identifying noise-masked audiovisual syllables. Older musicians outperformed older nonmusicians and showed comparable performance to young nonmusicians. Notably, older musicians retained similar neural specificity of speech representations in sensorimotor areas to young nonmusicians, while older nonmusicians showed degraded neural representations. In the same region, older musicians showed higher neural alignment to young nonmusicians than older nonmusicians, which was associated with their training intensity. In older nonmusicians, the degree of neural alignment predicted better performance. In addition, older musicians showed greater activation in frontal-parietal, speech motor, and visual motion regions and greater deactivation in the angular gyrus than older nonmusicians, which predicted higher neural alignment in sensorimotor areas. Together, these findings suggest that musicianship-related benefit in audiovisual speech-in-noise processing is rooted in preserving youth-like representations in sensorimotor regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".