Optical distortions in VR bias the perceived slant of moving surfaces
Bibliographic record
Abstract
The magnifying optics of virtual reality (VR) head-mounted displays (HMD) often cause undesirable pincushion distortion in the displayed imagery. Eccentrically increasing magnification radially displaces image-points away from the optical axis, causing straight lines to curve outwards. This, in turn, should affect the 3D perception of surface shape by warping binocular and monocular depth cues. Previous research has shown that distortion-induced biases in perceived slant do occur in static images. However, most use cases in VR involve moving images. Here we evaluate the impact of motion on biases in perceived slant. An HMD was used to present flat, textured surfaces that varied in slant and were either stationary, or translated laterally by the observer. In separate studies we varied the degree of distortion and evaluated the impact on perceived slant at several locations along the surface. We found that, irrespective of whether the surface was moving or stationary, distortion introduced significant bias into local slant estimates. The pattern of results is consistent with the surface appearing to be concave (as if viewing the inside surface of a bowl), as predicted from the warping of binocular and monocular cues. Importantly, the intermediate distortion level produced the same, but weaker, pattern of biases seen in the fully-distorted condition. When an appropriate level of pre-warping was applied, slant perception was veridical. Overall, our results highlight the importance of sufficiently correcting for optical distortions in VR HMDs to enable veridical perception of surface attitude.
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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 | 0.006 |
| 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) | 0.002 | 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".