Fast and slow processes underlie the selection of both step frequency and walking speed
Bibliographic record
Abstract
People prefer gaits that minimize their energetic cost. Research focused on step frequency selection suggests that a fast predictive process and a slower optimization process underlie this energy optimization. Our purpose in this study was to test whether the mechanisms controlling step frequency selection are used more generally to select one of the most relevant characteristics of walking - preferred speed. To accomplish this, we contrasted the dynamic adjustments in speed following perturbations to step frequency against the dynamic adjustments in step frequency following perturbations to speed. Despite the use of different perturbations and contexts, we found that the responses were very similar. In both experiments, subjects responded to perturbations by first rapidly changing their speed or step frequency towards their preferred pattern, and then slowly adjusting their gait to converge onto their preferred pattern. We measured similar response times for both the fast processes (1.4±0.3 versus 2.7±0.6 s) and the slow processes (74.2±25.4 versus 79.7±20.2 s). We also found that the fast process, although quite variable in amplitude, dominated the adjustments in both speed and step frequency. These distinct but complementary experiments demonstrate that people appear to rely heavily on prediction to rapidly select the most relevant aspects of their preferred gait and then gradually fine-tune that selection, perhaps using direct optimization of energetic cost.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".