Aerobic exercise enhances retention of a gradually imposed visuomotor rotation
Bibliographic record
Abstract
A single bout of intense aerobic exercise performed after the acquisition of a new sequence of movements has been shown to enhance retention. However, no such exercise-related advantage was reported when participants learned to adapt their reaching movements to compensate for a 60° deviation of the visual feedback (rVMA task). Since the sequence tasks used previously relied mainly on implicit learning processes, as opposed to the rVMA task which had an explicit component given the size of the deviation, it is possible that exercise-related gains in retention may be restricted to implicit learning. To test this hypothesis, we used a modified version of the rVMA task in which a smaller deviation (25°) was introduced gradually (1° per 10 trials), making it difficult to be perceived explicitly and thus favoring an implicit adaptation. Thirty participants took part in the experiment (7 males, 23 females; mean age: 22 ± 2.36) and, immediately after the rVMA acquisition session (500 trials in total), either performed 3 bouts of 3 minutes of cycling at 80% of their maximal aerobic power (Exercise Group) or watched a relaxation video (Control Group). When retention of the rVMA task was assessed 24 hours later in a no-vision test, participants of the Exercise group demonstrated better retention compared to those of the Control group (p = 0.037, d = 0.8). This result demonstrates that visuomotor adaptation can benefit from exercise and suggests that exercise-related effects on motor skill learning may be restricted to implicit learning.
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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 | 0.000 |
| 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) | 0.002 | 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".