Stability strategy restrictions do not elicit compensatory mechanisms during mediolaterally perturbed slow walking
Bibliographic record
Abstract
OBJECTIVE: Healthy individuals have the ability to overcome perturbations when walking without falling. They have multiple stability strategies at their disposal, but it remains unclear how these different strategies compensate for one another when one may be limited due to external factors. The objective of the current study was to determine how the different stability strategies compensate for one another when mediolateral perturbations were applied. METHODS: We performed both human experiments and computational modelling. The human experiments involved imposed restrictions on the different stability strategies while perturbations were applied and measuring the response of the other strategies. The stepping strategy was limited using visual feedback of step width projected onto the ground, the ankle strategy was restricted using narrow strips of rubber under the foot, and the trunk strategy was restricted using a brace. Similarly, in the computational model, we observed changes in the remaining strategies once one of the balance strategies was removed. RESULTS: In our gait study, we found that the limitation of one strategy did not result in compensatory behaviour in the remaining strategies. However, our computational model did exhibit compensatory behaviour when strategies were removed. CONCLUSION: These conflicting results suggest that compensatory behaviour has the potential to be beneficial for overcoming perturbations but is not utilized by healthy individuals. SIGNIFICANCE: The mismatch of experimental and modelling results suggests that human responses are not purely motivated by the continuation of forward motion but instead by a combination of objectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".