Experience-induced plasticity in the attention system of healthy adults practising musical or non-musical activities
Bibliographic record
Abstract
It is well known that executive functions, such as attention and inhibition, decline with aging. It has been suggested that the practice of a musical activity, such as singing or playing an instrument can reduce this decline through experience-induced brain plasticity. However, little is known about the plasticity mechanisms associated with different musical activities and those associated with non-musical activities. In this cross-sectional study, we examined the relationship between attention and cortical aging in the attention system of amateur singers, instrument players and active non-musicians with a focus on plasticity mechanisms. One hundred and nine healthy adults, aged between 20 and 88, were recruited and separated into 3 carefully matched groups: 34 singers, 38 instrumentalists, and 37 active non-musicians. Auditory selective attention and visual inhibition were evaluated, and anatomical MRI images were acquired. Our results confirm that aging is associated with poorer cognitive performance and thinner cortical grey matter, and further suggest that practising a musical activity is associated with greater compensatory scaffolding compared to practising a non-musical activity. However, more experience was not always associated with reduced age-related cortical thinning, meaning that, in some regions, more experience was associated with thicker cortex and in others, with thinner cortex. Importantly, the results for singers and instrumentalists suggest distinct underlying plasticity mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".