Display size does not affect egocentric distance perception of naturalistic stimuli
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although people are quite accurate in visually perceiving absolute egocentric distances in real environments up to 20m, they usually underestimate distances in virtual environments presented through head-mounted displays (HMDs). Several previous studies examined different potential factors, but none of these factors could convincingly explain the observed distance compressionin HMDs. In this study, we investigated the potential influence of naturalistic stimulus presentation and display size -- a factor largely overlooked in previous studies. To this end, we used an indirect blindfolded walking task to previously-seen targets. Participants viewed photos of targets located at various distances on the ground through different-sized displays (HMD, 24" monitor, and 50" screen) and walked without vision to where they thought the location of the target was. Real-world photographs were used to avoid potential artifacts of computer-graphics stimuli. Displays were positioned to provide identical fields of view (32° x 24°). Distance judgments were unexpectedly highly accurate and showed no signs of distance compression for any of the displays. Moreover, display size did not affect distance perception, and performance was virtually identical to a real world baseline, where real-world targets were viewed through 32° x 24° field of view restrictors. A careful analysis of potential underlying factors suggests that the typically-observed distance compression for HMDs might be overcome by using naturalistic real-world stimuli. This might also explain why display size did not affect distance judgments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it