Effect of Binocular Disparity on Detecting Target Motion during Locomotion
Bibliographic record
Abstract
During locomotion, optic flow provides important information for detection, estimation and navigation. On the other hand, binocular disparity, which carries compelling depth information, can potentially aid optic flow parsing. We explored the effect of binocular disparity on observers’ ability to detect object motion during simulated locomotion. Twelve participants were recruited and tested on our wide-field stereoscopic environment (WISE). The stimulus consisted of four spherical targets hovering in a pillar hallway, and it was presented in stereoscopic, synoptic (binocular but without disparity), and monocular viewing conditions. In each trial, one of the four targets moved either in depth (approaching or receding) or a direction parallel to the frontal plane (contracting or expanding). Participants detected the moving target during a simulated forward walking locomotion in a 4-alternative forced choice task. The locomotion speed was 1.4 m/s, and therefore the target motion was superimposed upon this optic flow. Adaptive staircases were adopted to obtain the thresholds of the target motion speed in each viewing condition. The results to date showed that participants’ thresholds in the stereoscopic condition were 20 - 40 % lower (better) than those in the synoptic condition when detecting approaching targets, t(7) = 3.85, p = .006, receding targets, t(7) = 2.83,p = .025, and contracting targets, t(7) = 2.57, p = .036. Furthermore, only when detecting expanding targets, threshold performance was significantly better in the synoptic condition than that in the monocular condition, t(7) = 2.67, p = .032. These results suggested that during locomotion, binocular disparity facilitates optic flow parsing.
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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 | 0.001 | 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".