Acute Exercise and Motor Memory Consolidation: The Role of Exercise Timing
Bibliographic record
Abstract
High intensity aerobic exercise amplifies offline gains in procedural memory acquired during motor practice. This effect seems to be evident when exercise is placed immediately after acquisition, during the first stages of memory consolidation, but the importance of temporal proximity of the exercise bout used to stimulate improvements in procedural memory is unknown. The effects of three different temporal placements of high intensity exercise were investigated following visuomotor skill acquisition on the retention of motor memory in 48 young (24.0 ± 2.5 yrs), healthy male subjects randomly assigned to one of four groups either performing a high intensity (90% Maximal Power Output) exercise bout at 20 min (EX90), 1 h (EX90+1), 2 h (EX90+2) after acquisition or rested (CON). Retention tests were performed at 1 d (R1) and 7 d (R7). At R1 changes in performance scores after acquisition were greater for EX90 than CON (p < 0.001) and EX90+2 (p = 0.001). At R7 changes in performance scores for EX90, EX90+1, and EX90+2 were higher than CON (p < 0.001, p = 0.008, and p = 0.008, resp.). Changes for EX90 at R7 were greater than EX90+2 (p = 0.049). Exercise-induced improvements in procedural memory diminish as the temporal proximity of exercise from acquisition is increased. Timing of exercise following motor practice is important for motor memory consolidation.
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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) | 0.001 | 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".