Failures in depth magnitude estimation in 3D displays
Bibliographic record
Abstract
Using naturalistic 3D ‘thicket’ and ‘branch’ stimuli we have shown that experienced observers generate accurate depth magnitude estimates for fused targets viewed in virtual reality (VR). However, we have observed that individuals with little to no experience with 3D displays exhibit striking errors in estimating depth from disparity. We conducted a set of experiments with inexperienced viewers to quantify and better understand their poor performance. Our first study was a replication of our experiment with experienced observers where novice participants viewed low and high complexity stimuli using a VR headset. In the ‘branch’ condition, two branches were presented, one on either side of a central reference branch. The more complex ‘thickets’ were composed of two clusters of overlapping branches centred on a reference branch. We varied the separation between the branches, and within the thickets, from 1.5 to 12 cm by displacing their components equally in front of and behind the fixation point. Sixteen inexperienced observers indicated the overall depth of the structures with a virtual ruler. In another study we evaluated the role of depth averaging by displacing the branches and thickets in a single direction relative to fixation. Unlike our previous results with experienced observers, in both experiments we found that the association between perceived depth and increasing disparity was weak. This, and the fact that all participants could perceive depth from all disparities in this range using a depth-order (near/far) discrimination task argues against an explanation based on depth averaging. We conclude that cue conflicts, particularly related to the contribution of vergence to estimation of viewing distance, interfere with inexperienced participants’ ability to compute depth from disparity. Our working hypothesis is that with extended experience observers learn to disregard such conflicts; how they do this is the focus of ongoing research.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".