Changes in task parameters during walking prism adaptation influence the subsequent generalization pattern
Bibliographic record
Abstract
An understanding of the transfer (or generalization) of motor adaptations between legs and across tasks during walking has remained elusive due to limited research and mixed results. Here, we asked whether stepping sequences or task constraints introduced during walking prism-adaptation tasks influence generalization patterns. Forty subjects adapted to prism glasses in precision-walking or obstacle-avoidance tasks that required a specific stepping sequence to the center of two/three targets or laterally over an obstacle. We then tested for generalization, reflected by aftereffects in the nonadapted task. Our previous study using these tasks found that only one leg generalized. Here, we reversed the stepping sequence and found that only the opposite leg generalized in the subject group that adapted in a precision-walking task. The combination of stepping sequence and direction of prism shift caused subjects in two groups to collide with the obstacle early during adaptation, thus making the step prior to going over the obstacle more important. Both legs subsequently generalized. A fourth subject group experienced a three-target, precision-walking task, resulting in a balanced, right-left, left-right stepping sequence, meant to induce bilateral generalization. While only one leg generalized, foot placement aftereffects before stepping over the obstacle would have caused subjects to collide with it. Together with our previous study, the results suggest a contribution of stepping sequence during the adapted task on generalization patterns, likely driven by proprioceptive feedback. The results also support the idea that negative consequences during adaptation and/or perceived threat can influence generalization.
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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".