Running perturbations reveal general strategies for step frequency selection
Bibliographic record
Abstract
Recent research has suggested that energy minimization in human walking involves both a fast preprogrammed process and a slow optimization process. Here, we studied human running to test whether these two processes represent control mechanisms specific to walking or a more general strategy for minimizing energetic cost in human locomotion. To accomplish this, we used free response experiments to enforce step frequency with a metronome at values above and below preferred step frequency and then determined the response times for the return to preferred steady-state step frequency when the auditory constraint was suddenly removed. In forced response experiments, we applied rapid changes in treadmill speed and examined response times for the processes involved in the consequent adjustments to step frequency. We then compared the dynamics of step frequency adjustments resulting from the two different perturbations to each other and to previous results found in walking. Despite the distinct perturbations applied in the two experiments, both responses were dominated by a fast process with a response time of 1.47 ± 0.05 s with fine-tuning provided by a slow process with a response time of 34.33 ± 0.50 s. The dynamics of the processes underlying step frequency adjustments in running match those found previously in walking, both in magnitude and relative importance. Our results suggest that the underlying mechanisms are fundamental strategies for minimizing energetic cost in human locomotion.
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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 | 0.001 |
| 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) | 0.001 | 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".