Time to move beyond a brainless exercise physiology: the evidence for complex regulation of human exercise performance
Bibliographic record
Abstract
In 1923, Nobel Laureate A.V. Hill proposed that maximal exercise performance is limited by the development of anaerobiosis in the exercising skeletal muscles. Variants of this theory have dominated teaching in the exercise sciences ever since, but 90 years later there is little biological evidence to support Hill's belief, and much that disproves it. The cardinal weakness of the Hill model is that it allows no role for the brain in the regulation of exercise performance. As a result, it is unable to explain at least 6 common phenomena, including (i) differential pacing strategies for different exercise durations; (ii) the end spurt; (iii) the presence of fatigue even though homeostasis is maintained; (iv) fewer than 100% of the muscle fibers have been recruited in the exercising limbs; (v) the evidence that a range of interventions that act exclusively on the brain can modify exercise performance; and (vi) the finding that the rating of perceived exertion is a function of the relative exercise duration rather than the exercise intensity. Here I argue that the central governor model (CGM) is better able to explain these phenomena. In the CGM, exercise is seen as a behaviour that is regulated by complex systems in the central nervous system specifically to ensure that exercise terminates before there is a catastrophic biological failure. The complexity of this regulation cannot be appreciated if the body is studied as a collection of disconnected components, as is the usual approach in the modern exercise sciences.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".