Lightness constancy can be very weak in an immersive VR environment
Bibliographic record
Abstract
Previous studies have revealed important differences between how viewers perceive real and virtual scenes. Virtual reality (VR) plays a growing role in performance-critical applications such as medical training and vision research, and so it is crucial to characterize perceptual differences between real and VR environments. We compared lightness constancy in real and VR environments. We used a demanding task that required observers to compensate for the orientation of a reference patch relative to a light source in a complex scene. On each trial the reference patch had reflectance 0.40 or 0.58, and a range of 3D orientations (azimuth -50º to 50º). Ten observers adjusted a grey match patch to match the perceived grey of the reference patch. We used a custom-built physical apparatus, and four VR conditions: All-Cues (replicated the physical apparatus); Reduced-Depth (zero disparity, no parallax); Shadowless (no cast shadows); and Reduced-Context (no surrounding objects). Scenes were rendered in Unity and shown in a Rift S headset. Surprisingly, constancy was weak, and approximately the same in all conditions. The mean Thouless ratio (0= no constancy, 1= perfect constancy) was 0.40, with no significant differences between conditions. The above-zero constancy in the Reduced-Context condition, with no cues to support constancy, suggested that observers learned environmental lighting cues in some conditions and transferred this knowledge to other conditions. Accordingly, we re-tested the All-Cue and Reduced-Context conditions in VR, with 10 new observers per condition, and each observer ran in just one condition. Here we found substantially reduced constancy (average Thouless ratio 0.14). We conclude that lightness constancy can be weak in VR, and that observers may use lighting information from real environments to guide performance in virtual environments. We are currently developing experiments with high-performance VR configurations to test whether constancy improves with more realistic rendering of lights and materials.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".