Moving in a box: Improving spatial orientation in virtual reality using simulated reference frames
Bibliographic record
Abstract
Despite recent advances in virtual reality, locomotion in a virtual environment is still restricted because of spatial disorientation. Previous research has shown the benefits of reference frames in maintaining spatial orientation. Here, we propose using a visually simulated reference frame in virtual reality to provide users with a better sense of direction in landmark-free virtual environments. Visually overlaid rectangular frames simulate different variations of frames of reference. We investigated how two different types of visually simulated reference frames might benefit in a navigational search task through a mixed-method study. Results showed that the presence of a reference frame significantly affects participants' performance in a navigational search task. Though the egocentric frame of reference (simulated CAVE) that translates with the observer did not significantly help, an allocentric frame of reference (a simulated stationary room) significantly improved user performance both in navigational search time and overall travel distance. Our study suggests that adding a variation of the reference frame to virtual reality applications might be a cost-effective solution to enable more effective locomotion in virtual reality.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".