The influence of body orientation relative to gravity on egocentric distance estimates in virtual reality
Bibliographic record
Abstract
Virtual reality head mounted displays (VR-HMD) can immerse individuals into a variety of virtual environments while accounting for head orientation to update the virtual environment. VR-HMDs also allow users to explore environments while maintaining different body positions (e.g. sitting and laying down). How discrepancies between real world body position and the virtual environment impact the perception of virtual space or, additionally, how a visual upright with incongruent changes in head orientation affects space perception within VR has not been fully defined. In this study we sought to further understand how changes in head-on-body orientation (laying supine, laying prone, laying on left side and, being upright) while a steady visual virtual upright is maintained can affect the perception of distance. We used a new psychophysics perceptual matching based approach with two different probe configurations (“L” & “T” shape) to extract distance perception thresholds in the four previously mentioned positions at egocentric distances of 4, 5, and 6 virtual meters. Our results indicate that changes in observer orientation with respect to gravity impact the perception of distances with a virtual environment when it is maintained at a visual upright. Here we found significant differences between perceived distances in the upright condition compared to the prone and laying on left side positions. Additionally, we found that distance perception results were impacted by differences in probe configuration. Our results add to a body of work on how changes in head-on-body orientation can affect the perception of distance, while stressing that more research is still needed to fully understand how these changes with respect to gravity affect the perception of space within virtual environments.
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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 | 0.000 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".