Musical Training Orchestrates Coordinated Neuroplasticity in Auditory Brainstem and Cortex to Counteract Age-Related Declines in Categorical Vowel Perception
Bibliographic record
Abstract
Musicianship in early life is associated with pervasive changes in brain function and enhanced speech-language skills. Whether these neuroplastic benefits extend to older individuals more susceptible to cognitive decline, and for whom plasticity is weaker, has yet to be established. Here, we show that musical training offsets declines in auditory brain processing that accompanying normal aging in humans, preserving robust speech recognition late into life. We recorded both brainstem and cortical neuroelectric responses in older adults with and without modest musical training as they classified speech sounds along an acoustic-phonetic continuum. Results reveal higher temporal precision in speech-evoked responses at multiple levels of the auditory system in older musicians who were also better at differentiating phonetic categories. Older musicians also showed a closer correspondence between neural activity and perceptual performance. This suggests that musicianship strengthens brain-behavior coupling in the aging auditory system. Last, "neurometric" functions derived from unsupervised classification of neural activity established that early cortical responses could accurately predict listeners' psychometric speech identification and, more critically, that neurometric profiles were organized more categorically in older musicians. We propose that musicianship offsets age-related declines in speech listening by refining the hierarchical interplay between subcortical/cortical auditory brain representations, allowing more behaviorally relevant information carried within the neural code, and supplying more faithful templates to the brain mechanisms subserving phonetic computations. Our findings imply that robust neuroplasticity conferred by musical training is not restricted by age and may serve as an effective means to bolster speech listening skills that decline across the lifespan.
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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) | 0.001 | 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".