Binocular cues to depth and distance enhance tolerance to visual and kinesthetic mismatch
Bibliographic record
Abstract
In natural environments, motion parallax (from visual direction and optic flow) supports both depth and distance perception. What happens if we do not know how far we have moved or receive conflicting information? We manipulated motion gain using a VR headset and a two-phase task to assess perceived depth and distance. Observers first viewed a “fold” stimulus, a wall-oriented dihedral angle covered in Voronoi texture. The task was to adjust the dihedral angle until it appeared to be 90 degrees (perpendicular). We occluded the top and bottom edges of the fold and varied the width to make the edges of the fold uninformative. On each trial, following the angle adjustment, a second scene appeared which contained a pole that extended from a ground plane. In this phase, the task was to match the position of the pole to the remembered position of the apex of the previously seen fold. We tested observers binocularly and monocularly in two motion conditions (stationary and moving). When moving, observers swayed laterally through 20 cm in time to a 0.5 Hz metronome; the motion gain varied from 0.5 to 2.0 times the actual self-motion. We found that increased gain caused an increase in the adjusted angle or equivalently a decrease in associated depth of the fold, especially when viewed monocularly. In addition, perceived distance decreased with increasing gain, irrespective of viewing condition. That is, the fold was perceived as smaller and closer when gain was larger than 1. The effect of the gain manipulation was much weaker under binocular viewing. These data show that perceptual distortions due to differences between actual and virtual head motion are compensated for by binocular, and to a lesser extent monocular, depth and distance cues. These flexible compensatory mechanisms make the human visual system highly tolerant of visual/kinesthetic mismatch.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".