Display conditions that influence wayfinding in virtual environments
Bibliographic record
Abstract
As virtual environments may be used in training and evaluation for critical real navigation tasks, it is important to investigate the factors influencing navigational performance in virtual environments. We have carried out controlled experiments involving two visual factors known to induce or sustain vection, the illusory perception of self-motion. The first experiment had subjects navigate mazes with either a narrow or wide field of view. We measured the percentage of wrong turns, the total time taken for each attempt, and we examined subjects' drawings of the mazes. We found that a wide field of view can have a substantial effect on navigational abilities, even when the wide field of view does not offer any additional clues to the task, and really only provides a larger view of blank walls on the sides. The second experiment evaluated the effect of perspective accuracy in the scene by comparing the use of displays that were corrected for changing head position against those that were not corrected. The perspective corrections available through headtracking did not appear have any influence on navigational abilities. Another component of our study suggests that during navigation in a virtual environment, memory for directions may not be as effective as it could be with supplemental symbolic representations.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".