The impact of color coding in Virtual Reality navigation tasks
Bibliographic record
Abstract
Colour coding is known to have a significant impact on navigation tasks in the real world by showing the way where to go or by highlighting certain regions in a building where specific activities are performed. Colour coding is frequently used in large buildings, such as hospitals, to help patients navigate and locate clinics. However, it is still not well known how colour coding works in virtual reality systems, or its influence on tasks such as navigation and situation awareness. In this experiment, we explore the impact of colour coding on a navigation task by comparing participants’ performances in a virtual world. Five different mazes with the similar level of complexity are attributed to various schemes. In the first experiment, participants are asked to find the exit in a virtual maze without any assistance; in the second and third experiments, participants are provided with a two-dimensional (2D) map with and without a global positioning system (GPS) as a guide to find the exit; in the fourth and fifth experiments, participants are provided with a 2D map with the selected colours embedded along the routes and with and without a GPS to indicate direction. The experimental results will provide evidence on how the colour-coding scheme influences user performance in a virtual world navigation task. This may have a strong influence on the future design of virtual reality training systems.
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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 | 0.002 | 0.034 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".