Physiological acrophobia evaluation through in vivo exposure in a VR CAVE
Bibliographic record
Abstract
Acrophobia (i.e., the fear of heights) is commonly treated using Virtual Reality (VR) applications. Patients that suffer from this clinical condition can experience extreme levels of anxiety, stress, and discomfort, even at relatively low heights. VR computer-assisted virtual environments (CAVEs) have been found to be highly immersive and successful in the treatment of acrophobia. The general method of evaluating therapy progress is through self-reported questionnaire measures. However, these are subject to participant bias. Physiological measures, on the other hand, could provide a more objective way of assessing acrophobia. To our knowledge, psychophysiological measures are not commonly used in the evaluation of acrophobes and their therapy progress within CAVEs. Thus, we present a CAVE application for acrophobia treatment, which includes a physiological feedback mechanism to assess patient progress. It also permits patient movement to facilitate increased presence and immersion. In this application, players sequentially gain access to increasing heights as they successfully progress through lesser heights, as assessed through the evaluation of their physiological responses to VR stimuli.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".