Manipulation of Motion Parallax Gain Distorts Perceived Distance and Object Depth in Virtual Reality
Bibliographic record
Abstract
Virtual reality (VR) is distinguished by the rich, multimodal, im-mersive sensory information and affordances provided to the user. However, when moving about an immersive virtual world the vi-sual display often conflicts with other sensory cues due to design, the nature of the simulation, or to system limitations (for example impoverished vestibular motion cues during acceleration in racing games). Given that conflicts between sensory cues have been as-sociated with disorientation or discomfort, and theoretically could distort spatial perception, it is important that we understand how and when they are manifested in the user experience. To this end, this set of experiments investigates the impact of mismatch between physical and virtual motion parallax on the per-ception of the depth of an apparently perpendicular dihedral angle (a fold) and its distance. We applied gain distortions between visual and kinesthetic head motion during lateral sway movements and measured the effect of gain on depth, distance and lateral space compression. We found that under monocular viewing, observers made smaller object depth and distance settings especially when the gain was greater than 1. Estimates of target distance declined with increasing gain under monocular viewing. Similarly, mean set depth decreased with increasing gain under monocular viewing, except at 6.0 m. The effect of gain was minimal when observers viewed the stimulus binocularly. Further, binocular viewing (stereopsis) improved the precision but not necessarily the accuracy of gain perception. Overall, the lateral compression of space was similar in the stereoscopic and monocular test conditions. Taken together, our results show that the use of large presentation distances (at 6 m) combined with binocular cues to depth and distance enhanced humans' tolerance to visual and 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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".