Simulated Reference Frame: A Cost-Effective Solution to Improve Spatial Orientation in VR
Bibliographic record
Abstract
Virtual Reality (VR) is increasingly used in spatial cognition research, as it offers high experimental control in naturalistic multimodal environments, which is hard to achieve in real-world settings. Although recent technological advances offer a high level of photorealism, locomotion in VR is still restricted because people might not perceive their self-motion as they would in the real world. This might be related to the inability to use embodied spatial orientation processes, which support automatic and obligatory updating of our spatial awareness. Previous research has identified the roles reference frames play in retaining spatial orientation. Here, we propose using visually overlaid rectangular boxes, simulating reference frames in VR, to provide users with a better insight into spatial direction in landmark-free virtual environments. The current mixed-method study investigated how different variations of the visually simulated reference frames might support people in a navigational search task. Performance results showed that the existence of a simulated reference frame yields significant effects on participants completion time and travel distance. Though a simulated CAVE translating with the navigator (one of the simulated reference frames) did not provide significant benefits, the simulated room (another simulated reference frame depicting a rest frame) significantly boosted user performance in the task as well as improved participants preference in the post-experiment evaluation. Results suggest that adding a visually simulated reference frame to VR applications might be a cost-effective solution to the spatial disorientation problem in VR.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".