High-Intensity Aerobic Exercise Enhances Motor Memory Retrieval
Bibliographic record
Abstract
INTRODUCTION: In previous work, acute high-intensity aerobic exercise benefited continuous motor sequence task learning. As memory processes underlying motor sequence learning vary between tasks involving continuous and discrete movements, the objective of the current study was to determine whether the beneficial effects of acute aerobic exercise generalize to the learning of a discrete motor sequence task. METHODS: Sixteen young healthy individuals practiced a discrete motor sequence task preceded by either a period of rest or a bout of high-intensity cycling. Participants moved a cursor with a computer mouse to a series of discretely presented targets on a screen. Target presentation followed either a repeated or a random sequence, which allowed the evaluation of implicit sequence-specific motor learning. The change in movement response time over practice (△-ACQ) and from practice to a 24-h "no-exercise" retention test (△-RET) and the rate of improvement over practice (α-ACQ) and during the retention test (α-RET) were calculated. RESULTS: α-RET was greater for the repeated sequence than random sequences after aerobic exercise (P = 0.01), but not rest (P = 0.33). Further, α-RET for the repeated sequence was greater after aerobic exercise than for either sequence (repeated, random) in the rest condition (P ≤ 0.01). There were no differences between sequences and/or conditions for △-ACQ, △-RET, or α-RET (P ≥ 0.57). CONCLUSION: Our findings show a positive effect of acute high-intensity aerobic exercise on implicit discrete motor sequence learning. Performing exercise before practice increased the rate of improvement at a 24-h delayed retention test, suggesting an effect on the rate of motor memory retrieval. Pairing acute aerobic exercise with motor practice may facilitate learning of discrete movement sequences in sport or rehabilitation settings.
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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) | 0.003 | 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".