Predicting Motor Skill Learning in Older Adults Using Visuospatial Performance
Bibliographic record
Abstract
Between-group comparisons of older and younger adults suggest that motor learning decreases with advancing age. However, such comparisons do not necessarily account for group differences in cognitive function, despite the co-occurrence of aging and cognitive decline. As such, cognitive differences may explain the observed age effects on motor learning. Recent work has shown that the extent to which a motor task is learned is related to visuospatial function in adults over age 65. The current study tested whether this relationship is replicable across a wider age range and with a brief, widely available cognitive test. Thirty-three adults (aged 39-89 years old) completed the Montreal Cognitive Assessment (MoCA) prior to practicing a functional upper extremity motor task; performance on the motor task was assessed 24 hours later to quantify learning. Backward elimination stepwise linear regression identified which cognitive domains significantly predicted retention. Consistent with previous findings, only the Visuospatial/Executive subtest score predicted change in performance 24 hours later, even when accounting for participant age. Thus, the age-related declines in motor learning that have been reported previously may be explained in part by deficits in visuospatial function that can occur with advancing age.
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 | 0.001 | 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 | 0.000 |
| 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".