Music Lessons Improve Auditory Perceptual and Cognitive Performance in Deaf Children
Bibliographic record
Abstract
Despite advanced technologies in auditory rehabilitation of profound deafness, deaf children often exhibit delayed cognitive and linguistic development and auditory training remains a crucial element of their education. In the present cross-sectional study, we assess whether music would be a relevant tool for deaf children rehabilitation. In normal-hearing children, music lessons have been shown to improve cognitive and linguistic-related abilities, such as phonetic discrimination and reading. We compared auditory perception, auditory cognition, and phonetic discrimination between 14 profoundly deaf children who completed weekly music lessons for a period of 1.5-4 years and 14 deaf children who did not receive musical instruction. Children were assessed on perceptual and cognitive auditory tasks using environmental sounds: discrimination, identification, auditory scene analysis, auditory working memory. Transfer to the linguistic domain was tested with a phonetic discrimination task. Musically trained children showed better performance in auditory scene analysis, auditory working memory and phonetic discrimination tasks, and multiple regressions showed that success on these tasks was at least partly driven by music lessons. We propose that musical education contributes to development of general processes such as auditory attention and perception, which, in turn, facilitate auditory-related cognitive and linguistic processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".