Visual plasticity and exercise revisited: No evidence for a “cycling lane”
Bibliographic record
Abstract
Experiments using enriched environments have shown that physical exercise modulates visual plasticity in rodents. A recent study (Lunghi & Sale, 2015) investigated whether exercise also affects visual plasticity in adult humans. The plastic effect they measured was the shift in ocular dominance caused by 2 hr of monocular deprivation (e.g., by an eye patch). They used a binocular rivalry task to measure this shift. They found that the magnitude of the shift was increased by exercise during the deprivation period. This effect of exercise was later disputed by a study that used a different behavioral task (Zhou, Reynaud, & Hess, 2017). Our goal was to determine whether the difference in task was responsible for that study's failure to find an exercise effect. We set out to replicate Lunghi and Sale (2015). We measured ocular dominance with a rivalry task before and after 2 hr of deprivation. We measured data from two conditions in 30 subjects. On two separate days, they either performed exercise or rested during the deprivation period. Contrary to the previous study, we find no significant effect of exercise. We hypothesize that exercise may affect rivalry dynamics in a way that interacts with the measurement of the deprivation effect.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".