Physiologically-Driven Audiovisual Changes in Virtual Reality Radiation Source Location
Bibliographic record
Abstract
The availability of virtual reality (VR) technology at the consumer level is enabling its adoption for training and educational purposes. In particular, VR is showing promise in scenarios that are difficult or impossible to replicate in real life due to physical constraints related to specialized equipment or facilities, and health hazards for trainees. This scenario is typical in nuclear energy training, where trainees are required to locate and manipulate radiation sources. Traditionally, radiation source manipulation is taught using safe radiation sources in laboratory settings, where trainees use a Geiger-Müller counter to detect and measure ionizing radiation to locate a radiation source in the least possible time to reduce radiation exposure. However, such an approach is not cost-effective and exposes trainees to radiation. This paper presents the development of a VR radiation source location scenario in which skin response and heart rate modify the flashing frequency and audio intensity of an emergency light and a siren, respectively. The main goal is to better understand how physiologically-driven audiovisual changes impact usability, cognitive load, and anxiety when locating hidden radiation sources. Our preliminary results indicate that the modification of audiovisual cues affects usability, cognitive load, anxiety, and completion time. Our findings, although preliminary, provide some important considerations for future work that are relevant for designing and developing VR experiences that could be tailored to each user.
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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 | 0.001 | 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".