Effects of repeated walking in a perturbing environment: a 4-day locomotor learning study
Bibliographic record
Abstract
Previous studies have shown that when subjects repeatedly walk in a perturbing environment, initial movement error becomes smaller, suggesting that retention of the adapted locomotor program occurred (learning). It has been proposed that the newly learned locomotor program may be stored separately from the baseline program. However, how locomotor performance evolves with repeated sessions of walking with the perturbation is not yet known. To address this question, 10 healthy subjects walked on a treadmill on 4 consecutive days. Each day, locomotor performance was measured using kinematics and surface electromyography (EMGs), before, during, and after exposure to a perturbation, produced by an elastic tubing that pulled the foot forward and up during swing, inducing a foot velocity error in the first strides. Initial movement error decreased significantly between days 1 and 2 and then remained stable. Associated changes in medial hamstring EMG activity stabilized only on day 3, however. Aftereffects were present after perturbation removal, suggesting that daily adaptation involved central command recalibration of the baseline program. Aftereffects gradually decreased across days but were still visible on day 4. Separation between the newly learned and baseline programs may take longer than suggested by the daily improvement in initial performance in the perturbing environment or may never be complete. These results therefore suggest that reaching optimal performance in a perturbing environment should not be used as the main indicator of a completed learning process, as central reorganization of the motor commands continues days after initial performance has stabilized.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".