The Role of Binocular Vision in Stepping over Obstacles and Gaps in Virtual Environment
Bibliographic record
Abstract
Little is known about the role of stereopsis in locomotion activities, such as continuous walking and running. While previous studies have shown that stereopsis improves the accuracy of lower limb movements while walking in constrained spaces, it is still unclear whether stereopsis aids continuous locomotion during extended motion over longer distance. We conducted two walking experiments in virtual environments to investigate the role of binocular vision in avoiding virtual obstacles and traversing virtual gaps during continuous walking. The virtual environments were presented on a novel projected display known as the Wide Immersive Stereo Environment (WISE) and the participant locomoted through them on a linear treadmill. This experiment setup provided us with a unique advantage of simulating long-distance walking through an extended environment. In Experiment 1, along each 100-m path were thirty virtual obstacles, ten each at heights of 0.1 m, 0.2 m or 0.3 m, in random order. In Experiment 2, along each 100-m path were thirty virtual gaps, either 0.2 m, 0.3 m or 0.4 m across. During experimental sessions, participants were asked to walk at a constant speed of 2 km/h under both stereoscopic viewing and non-stereoscopic viewing conditions and step over virtual obstacles or gaps when necessary. By analyzing the gait parameters, such as stride height and stride length, we found that stereoscopic vision helped people to make more accurate steps over virtual obstacles and gaps during continuous walking.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".