eLife Assessment: Integrating Gaze, image analysis, and body tracking: Foothold selection during locomotion
Bibliographic record
Abstract
Relatively little is known about the way vision is use to guide locomo-tion in the natural world. What visual features are used to choose paths in natural complex terrain? How do walkers trade off different costs such as getting to the goal, minimizing energy, and satisfying stability constraints? To answer these questions, it is necessary to monitor not only the eyes and the body, but also to represent the three dimensional structure of the terrain. We used photogrammetry techniques to do this, and found substantial regularities in the choice of paths. Walkers avoid paths that involve changes in height and choose more circuitous and flatter paths. This stable tradeoff is related to the walker’s leg length and reflects both energetic and stability constraints. Gaze data and path choices suggest that subjects take into account the terrain approximately 5 steps ahead, and so are planning routes as well as particular footplants. Such planning ahead allows the minimization of energetic costs. Thus locomotor behavior in natural environments is controlled by decision mechanisms that attempt to optimize for multiple factors in the context of well-calibrated sensory and motor internal models.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".