The relationship between eye and head movements during locomotion with visual pursuit tasks
Bibliographic record
Abstract
Purpose: In daily life, visual tasks from fixating and pursuit to search and discriminations, often have to be performed while the observer is in motion rather than immobile. Locomotion, however, induces various head movements (HM), which displace the eye and need to be compensated for in order for the eye to be directed appropriately. Here we studied the effects of locomotion on the accuracy of eye position during fixation and linear pursuit of moving spots. Methods: Observers were standing, walking or running on a treadmill. Translational and rotational HM (pitch, bob, yaw, heave) were measured with an OptiTrack motion capture system, and eye position was recorded with an EyeLink eye tracker, while observers attempted to keep their eyes on a stationary or horizontally or vertically oscillating spot with different amplitude and velocity. Results: Pitch and yaw angles remained constant for all pursuit movements when observers were standing, while these angles varied systematically with locomotion, especially for walking. Bob-pitch, and heave-yaw movements were correlated in most visual conditions, such as to compensate for each other's deviations. When comparing the influence of visual stimulus amplitude or velocity on pitch and yaw movements, standing and running gave fairly similar results, while walking resulted in increased downward pitch. Conclusion: While both kinds of locomotion introduced more pursuit errors and more variability, running was in many ways less disruptive than walking, providing some evidence for the contention that in the case of running, compensation for HM is especially well adapted (Bramble & Lieberman, 2004).
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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 | 0.005 |
| 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".