Master Athletes and cognitive performance: What are the potential explanatory neurophysiological mechanisms?
Bibliographic record
Abstract
Regular physical activity has been recognized as an effective strategy for limiting the cognitive decline observed during aging. Much evidence has supported that maintaining a high level of physical activity and cardiorespiratory fitness is associated with better cognitive performances across the lifespan. From childhood to adulthood, a high level of physical activity will have a positive impact on cerebral health. More specifically, executive performance seems to be preferentially affected by the level of fitness. This is partly because the prefrontal cortex, which governs these functions, seems to be very sensitive to physical activity levels. Today many neurophysiological mechanisms that explain the improvement of the cognitive performance are relatively well identified. A question then arises as to what is the optimal dose of physical activity to observe these effects on our brain. An example of successful aging is the example of the Master Athletes. This category of people who have been training and competing throughout their lives, demonstrate high levels of fitness induced by a high level of physical activity. Some studies seem to confirm that Master Athletes have better cognitive performances than sedentary or less active subjects. The aim of this review is to identify studies assessing the cognitive performance of Master Athletes and report on the probable neurophysiological mechanisms that explain the cognitive benefits in this population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".