Failures of depth magnitude estimation in virtual but not physical stimuli
Bibliographic record
Abstract
When asked to estimate depth magnitude in naturalistic, cluttered stimuli observers lacking experience with virtual reality (VR) exhibit little to no scaling of depth with binocular disparity. Here, we asked whether this failure reflects a general inability to generate metric depth estimates for these types of stimuli or if it is related to cue conflicts that limit the use of vergence to estimate viewing distance in VR. To this end, we rendered low (‘branch’) and high (‘thicket’) complexity stimuli in VR and 3D printed exact physical replicas. In the branch condition, two branches were presented on either side of a central reference branch. The thicket condition consisted of two mirrored clusters of overlapping branches centred around the reference. The total disparity ranged from 0.33 to 2.10 degrees. Separate groups of novice observers estimated the 3D volume of branches (N=28) or thickets (N=25) using a virtual ruler. Both types of stimuli were tested in VR and physically; the physical stimuli were tested monocularly and binocularly. Our VR results replicated previous outcomes: novice observers showed limited depth scaling irrespective of complexity. Their binocular functions were relatively flat and similar to those obtained monocularly. Very different results were obtained using physical stimuli depth, for which estimates scaled well with disparity. These results support our hypothesis that novice observers’ poor depth scaling is due to unreliable distance information in VR, which is needed to scale binocular disparity. Importantly, experienced observers show reasonable depth scaling for virtual stimuli, suggesting that they are able to ignore cue conflicts. These findings have important implications, both for cue integration studies, which typically involve experienced observers, and for the use of VR in studies of depth perception.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".