The effect of an acute bout of exercise on implicit sensorimotor adaptation
Bibliographic record
Abstract
Recent studies have found that aerobic exercise improves sensorimotor adaptation. For instance, adaptation to an abruptly-presented 45° visuomotor rotation is improved if preceded by an acute bout of exercise. Still, it is known that adaptation is driven both by an explicit (cognitive) process as well as an implicit process. Given that exercise is known to benefit cognitive function, it remains an open question whether exercise selectively improves implicit adaptation. Here we tested this hypothesis, using a paradigm known to isolate implicit adaptation. In a within-participant design, participants (n = 26) had to reach toward a target, while being pseudo randomly exposed to CW or CCW 30° visuomotor rotations. Implicit adaptation was assessed by the involuntary bias in hand direction that follows a rotated trial, called post rotation bias (PRB). On separate days, participants performed 180 trials before (PRE) and after (POST) a period of exercise, or a period of rest. The exercise bout consisted of 20 min of moderate intensity cycling, which has previously been shown to benefit performance in cognitive and adaptation tasks. Results revealed robust PRBs in both conditions and phases, but critically there was an interaction: the magnitude of PRBs was significantly attenuated following rest, but not following exercise. Further analyses revealed that movements were produced significantly faster following exercise, confirming that exercise impacted motor vigor. By showing that an acute bout of exercise prevents the attenuation of implicit adaptation that is observed following rest, these results indirectly suggest that exercise has a positive effect on implicit adaptation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".