The contribution of vision, proprioception, and efference copy in storing a neural representation for guiding trail leg trajectory over an obstacle
Bibliographic record
Abstract
Stepping over obstacles requires vision to guide the leading leg, but direct visual information is not available to guide the trailing leg. The neural mechanisms for establishing a stored obstacle representation and thus facilitating the trail leg trajectory in humans are unknown. Twenty-four subjects participated in one of three experiments, which were designed to investigate the contribution of visual, proprioceptive, and efference copy signals. Subjects stepped over an obstacle with their lead leg, stopped, and straddled the obstacle for a delay period before stepping over it with their trail leg while toe elevation was recorded. Subsequently, we calculated maximum toe elevation and toe clearance. First, we found that subjects could accurately scale trail leg toe elevation and clearance, despite straddling an obstacle for up to 2 min, similar to quadrupeds. Second, we found that when the lead leg was passively moved over an obstacle (eliminating an efference copy signal and altering proprioception) without vision, trail leg toe elevation and clearance were reduced, and variability increased compared with when subjects actively moved their lead leg. Trail leg toe measures returned to normal when vision was provided during the passive manipulation. Finally, we found that altering lead leg proprioceptive feedback by adding mass to the ankle had no effect on trail leg toe measures. Taken together, our results suggest that humans can store a neural representation of obstacle properties for extended periods of time and that vision appears to be sufficient in this process to guide trail leg trajectory.
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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) | 0.002 | 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".