Stepping onto the unknown: reflexes of the foot and ankle while stepping with perturbed perceptions of terrain
Bibliographic record
Abstract
Unanticipated variations in terrain can destabilize the body. The foot is the primary interface with the ground and we know that cutaneous reflexes provide important sensory feedback. However, little is known about the contribution of stretch reflexes from the muscles within the foot to upright stability. We used intramuscular electromyography measurements of the foot muscles flexor digitorum brevis (FDB) and abductor hallucis (AH) to show for the first time how their short-latency stretch reflex response (SLR) may play an important role in responding to stepping perturbations. The SLR of FDB and AH was highest for downwards steps and lowest for upwards steps, with the response amplitude for level and compliant steps in between. When the type of terrain was unknown or unexpected to the participant, the SLR of AH and the ankle muscle soleus tended to decrease. We found significant relationships between the contact kinematics and forces of the leg and the SLR, but a person's expectation still had significant effects even after accounting for these relationships. Motor control models of short-latency body stabilization should not only include local muscle dynamics, but also predictions of terrain based on higher level information such as from vision or memory.
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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 | 0.002 |
| 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".