Enriched childhood experiences moderate age-related motor and cognitive decline
Bibliographic record
Abstract
Aging is associated with deterioration of skilled manual movement. Specifically, aging corresponds with increased reaction time, greater movement duration, segmentation of movement, increased movement variability, and reduced ability to adapt to external forces and inhibit previously learned sequences. Moreover, it is thought that decreased lateralization of neural function in older adults may point to increased neural recruitment as a compensatory response to deterioration of key frontal and intra-hemispheric networks, particularly of callosal structures. However, factors that mediate age-related motor decline are not well understood. Here we show that music training in childhood is associated with reduced age-related decline of bimanual and unimanual motor skills in a MIDI keyboard motor learning task. Compared to older adults without music training, older adults with more than a year of music training demonstrated proficient bimanual and unimanual movement, evidenced by enhanced speed and decreased movement errors. Further, this group demonstrated significantly better implicit learning in the weather prediction task, a non-motor task. The performance of older adults with music training in those tasks was comparable to young adults. Older adults, however, displayed greater verbal ability compared to young adults irrespective of a past history of music training. Our results indicate that music training early in life may reduce age-associated decline of neural motor and cognitive networks.
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 | 0.000 |
| 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".