Are musical activities associated with enhanced speech perception in noise in adults? A systematic review and meta-analysis
Bibliographic record
Abstract
The ability to process speech in noise (SPiN) declines with age, with a detrimental impact on life quality. Music-making activities such as singing and playing a musical instrument have raised interest as potential prevention strategies for SPiN perception decline because of their positive impact on several brain system, especially the auditory system, which is critical for SPiN. However, the literature on the effect of musicianship on SPiN performance has yielded mixed results. By critically assessing the existing literature with a systematic review and a meta-analysis, we aim to provide a comprehensive portrait of the relationship between music-making activities and SPiN in different experimental conditions. 38/49 articles, most focusing on young adults, were included in the quantitative analysis. The results show a positive relationship between music-making activities and SPiN, with the strongest effects found in the most challenging listening conditions, and little to no effect in less challenging situations. This pattern of results supports the notion of a relative advantage for musicians on SPiN performance and clarify the scope of this effect. However, further studies, especially with older adults, using adequate randomization methods, are needed to extend the present conclusions and assess the potential for musical activities to be used to mitigate SPiN decline in seniors.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".