Misperception of the distance of virtual augmentations
Bibliographic record
Abstract
Binocular disparity provides metric depth information, while monocular cues like occlusion offer depth order. In augmented reality (AR), conflicts between these cues can occur when virtual objects fail to be occluded by real-world surfaces, creating a depth cue conflict and subsequently impacting depth perception. The integration of occlusion and binocular disparity was investigated under this cue conflict in AR using distance-matching paradigms. These paradigms were applied within reach space (0.35–0.5 m) and beyond reach space (0.9–1.5 m). Observers matched the distance of a virtual letter ’A,’ superimposed on a physical surface, using a virtual probe. In addition to the probe, manual reach responses were also made with the index finger for the within reach space condition. Results revealed consistent underestimation of the letter’s distance when it was rendered beyond the surface, with errors proportional to distance. These biases persisted even when proprioceptive information was available, highlighting the robust influence of occlusion cue conflicts on perceived depth. Thus, designers must carefully plan and position virtual augmentations to avoid such errors and their impact on user interaction.
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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 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".