Compensation of the effects of eye and head movements during walking and running
Bibliographic record
Abstract
Purpose: A consequence of human locomotion (walking, running) is the occurrence of related eye (EM) and head (HM) movements, which could potentially distort locomotion-produced flow field information. This information is normally used to guide many visual tasks. We were interested in comparing visual performance during locomotion and standing for various tasks. Methods: We recorded EM and HM (EyeLink II eye tracker with scene camera) when observers were standing, walking or running on a treadmill while observing flow fields or other stimuli for various visual tasks that were projected on a large screen. In one experiment, baseline data were collected for fixations and pursuit movements. In another, accuracy of target pursuit was determined. In others, velocity discrimination thresholds or visual search efficiency were measured. Results: We analyzed horizontal and vertical EM and HM and compared the results for standing to those for the locomotion conditions. In most cases, performance during walking and running was comparable, and sometimes even better, than during standing. This was true even though HM were only partially offset by stabilizing EM, leaving considerable amounts of noisy distortions of the flow fields. Conclusion: The fact that visual performance suffered little during locomotion suggests that there exist various mechanisms, in addition to extra-retinal feedback, that can compensate for the extra noise produced by locomotion.
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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".