Musical training sharpens and bonds ears and tongue to hear speech better
Bibliographic record
Abstract
The idea that musical training improves speech perception in challenging listening environments is appealing and of clinical importance, yet the mechanisms of any such musician advantage are not well specified. Here, using functional magnetic resonance imaging (fMRI), we found that musicians outperformed nonmusicians in identifying syllables at varying signal-to-noise ratios (SNRs), which was associated with stronger activation of the left inferior frontal and right auditory regions in musicians compared with nonmusicians. Moreover, musicians showed greater specificity of phoneme representations in bilateral auditory and speech motor regions (e.g., premotor cortex) at higher SNRs and in the left speech motor regions at lower SNRs, as determined by multivoxel pattern analysis. Musical training also enhanced the intrahemispheric and interhemispheric functional connectivity between auditory and speech motor regions. Our findings suggest that improved speech in noise perception in musicians relies on stronger recruitment of, finer phonological representations in, and stronger functional connectivity between auditory and frontal speech motor cortices in both hemispheres, regions involved in bottom-up spectrotemporal analyses and top-down articulatory prediction and sensorimotor integration, respectively.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".