The importance of the motor system in the development of music‐based forms of auditory rehabilitation
Bibliographic record
Abstract
Hearing abilities decline with age, and one of the most commonly reported hearing issues in older adults is a difficulty understanding speech when there is loud background noise. Understanding speech in noise relies on numerous cognitive processes, including working memory, and is supported by numerous brain regions, including the motor and motor planning systems. Indeed, many working memory processes are supported by motor and premotor cortical regions. Interestingly, lifelong musicians and nonmusicians given music training over the course of weeks or months show an improved ability to understand speech when there is loud background noise. These benefits are associated with enhanced working memory abilities, and enhanced activity in motor and premotor cortical regions. Accordingly, it is likely that music training improves the coupling between the auditory and motor systems and promotes plasticity in these regions and regions that feed into auditory/motor areas. This leads to an enhanced ability to dynamically process incoming acoustic information, and is likely the reason that musicians and those who receive laboratory-based music training are better able to understand speech when there is background noise. Critically, these findings suggest that music-based forms of auditory rehabilitation are possible and should focus on tasks that promote auditory-motor interactions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".