Stereoscopic depth constancy for physical objects and their virtual counterparts
Bibliographic record
Abstract
Stereopsis plays an important role in depth perception; if so, disparity-defined depth should not vary with distance. However, studies of stereoscopic depth constancy often report systematic distortions in depth judgments over distance, particularly for virtual stimuli. Our aim was to understand how depth estimation is impacted by viewing distance and display-based cue conflicts by replicating physical objects in virtual counterparts. To this end, we measured perceived depth using virtual textured half-cylinders and identical three-dimensional (3D) printed versions at two viewing distances under monocular and binocular conditions. Virtual stimuli were viewed using a mirror stereoscope and an Oculus Rift head-mounted display (HMD), while physical stimuli were viewed in a controlled test environment. Depth judgments were similar in both virtual apparatuses, which suggests that variations in the viewing geometry and optics of the HMD have little impact on perceived depth. When viewing physical stimuli binocularly, judgments were accurate and exhibited stereoscopic depth constancy. However, in all cases, depth was underestimated for virtual stimuli and failed to achieve depth constancy. It is clear that depth constancy is only complete for cue-rich physical stimuli and that the failure of constancy in virtual stimuli is due to the presence of the vergence-accommodation conflict. Further, our post hoc analysis revealed that prior experience with virtual and physical environments had a strong effect on depth judgments. That is, performance in virtual environments was enhanced by limited exposure to a related task using physical objects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".