Aerobic exercise and aerobic fitness level do not modify motor learning
Bibliographic record
Abstract
Abstract Motor learning may be enhanced when a single session of aerobic exercise is performed immediately before or after motor skill practice. Most research to date has focused on aerobically trained (AT) individuals, but it is unknown if aerobically untrained (AU) individuals would equally benefit. We aimed to: (a) replicate previous studies and determine the effect of rest (REST) versus exercise (EXE) on motor skill retention, and (b) explore the effect of aerobic fitness level (AU, AT), assessed by peak oxygen uptake (VO2peak), on motor skill retention after exercise. Forty-four participants (20–29 years) practiced a visuomotor tracking task (acquisition), immediately followed by 25-min of high-intensity cycling or rest. Twenty-four hours after acquisition, participants completed a motor skill retention test. REST and EXE groups significantly improved motor skill performance during acquisition [F(3.17, 133.22) = 269.13, P = 0.001], but had no group differences in motor skill retention across time. AU-exercise (VO2peak = 31.6 ± 4.2 ml kg−1 min−1) and AT-exercise (VO2peak = 51.5 ± 7.6 ml kg−1 min−1) groups significantly improved motor skill performance during acquisition [F(3.07, 61.44) = 155.95, P = 0.001], but had no group differences in motor skill retention across time. Therefore, exercise or aerobic fitness level did not modify motor skill retention.
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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) | 0.002 | 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".