Anxiety Increases the Feeling of Presence in Virtual Reality
Bibliographic record
Abstract
Given previous studies indicating a significant correlation between anxiety and presence, the purpose of this investigation was to explore the direction of the causal relationship between them. The sample consisted of 31 adults suffering from snake phobia. The study featured a randomized within-between design with two conditions and three counterbalanced immersions: (a) a baseline control immersion (BASELINE), (b) an immersion in a threatening and anxiety-inducing environment (ANX), and (c) an immersion in a nonthreatening environment that should not induce anxiety (NOANX). In the NOANX environment, participants were immersed for 5 min in a virtual Egyptian desert. They were told that the environment was safe and contained no snakes. The ANX immersion was identical, except that participants were led to believe that a multitude of hidden and dangerous snakes were lurking in the environment. A period of distraction (reading a text on relaxation) separated the ANX and NOANX immersions. Experimenters recorded presence and anxiety in the middle of and after each VR immersion. These brief measures of presence supported our hypothesis and were significantly higher in the anxious immersion than in the baseline or the nonanxious immersion. This finding was not corroborated by the presence questionnaire, where scores varied significantly in the opposite direction. The results from the brief one-item measures of presence support the significant contribution of emotions felt during the immersion on the subjective feeling of presence. The mixed results with the presence questionnaire are discussed, along with psychological factors potentially involved in presence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".