Music Training and Education Slow the Deterioration of Music Perception Produced by Presbycusis in the Elderly
Bibliographic record
Abstract
The perception of music depends on the normal function of the peripheral and central auditory system. Aged subjects without hearing loss have altered music perception, including pitch and temporal features. Presbycusis or age-related hearing loss is a frequent condition in elderly people, produced by neurodegenerative processes that affect the cochlear receptor cells and brain circuits involved in auditory perception. Clinically, presbycusis patients have bilateral high-frequency hearing loss and deteriorated speech intelligibility. Music impairments in presbycusis subjects can be attributed to the normal aging processes and to presbycusis neuropathological changes. However, whether presbycusis further impairs music perception remains controversial. Here, we developed a computerized version of the Montreal battery of evaluation of amusia (MBEA) and assessed music perception in 175 Chilean adults aged between 18 and 90 years without hearing complaints and in symptomatic presbycusis patients. We give normative data for MBEA performance in a Latin-American population, showing age and educational effects. In addition, we found that symptomatic presbycusis was the most relevant factor determining global MBEA accuracy in aged subjects. Moreover, we show that melodic impairments in presbycusis individuals were diminished by music training, while the performance in temporal tasks were affected by the educational level and music training. We conclude that music training and education are important factors as they can slow the deterioration of music perception produced by age-related hearing loss.
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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 | 0.000 |
| 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".