Interleaved periods of exercise do not enhance visual perceptual learning
Bibliographic record
Abstract
Animal models indicate that exercise promotes visual cortex neuroplasticity; however, results from studies that have explored this effect in humans are mixed. A potential explanation for these discrepant results is the relative timing of exercise and the task used to index neuroplasticity. We hypothesized that a close temporal pairing of exercise and training on a vision task would enhance perceptual learning (a measure of neuroplasticity) compared to a non exercise control. Thirty-two participants (mean age = 31 years; range, 20-65; SD = 11.1; 50:50 sex ratio) were randomly assigned to Exercise or Non Exercise groups. The Exercise group alternated between moderate cycling along a virtual course and training on a peripheral crowding task (5 minutes each, 1 hour total intervention), and the Non Exercise group alternated between passive viewing of the virtual cycling course and the vision task. The protocol was repeated across 5 consecutive days. Both groups exhibited reduced visual crowding after 5 days of training. However, there was no difference in perceptual learning magnitude or rate between groups. Translation of the animal exercise and visual cortex neuroplasticity results to humans may depend on a range of factors, such as baseline fitness levels and the measures used to quantify neuroplasticity.
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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".