Re-Examining Psychological Mechanisms Underlying Virtual Reality-Based Exposure for Spider Phobia
Bibliographic record
Abstract
The proposed study aims at expanding results from a previous study on mechanisms of change after exposure in virtual reality (VR) and documenting the impact of adding tactile and haptic feedback. It was predicted that change in the severity of spider phobia according to the Fear of Spiders Questionnaire (FSQ) would be significantly predicted by change in dysfunctional beliefs toward spiders and self-efficacy, over and above the variance explained by a physiological measure of fear during exposure (heart rate) and presence during the immersion. Participants (N = 59) were randomly assigned to the presentation of visual stimuli only, visual plus tactile stimuli, or visual, tactile plus haptic feedback stimuli. A standard multiple regression was conducted to predict change on the FSQ using the following predictors: beliefs about spiders, beliefs about one's own behavior when facing spiders, perceived self-efficacy, disgust, presence, and heart rate. Only changes in beliefs about spiders and in perceived self-efficacy significantly predicted the reduction in fear of spiders. This result enhances our understanding of the mechanisms involved in exposure conducted in VR. Analyses of variance also show that participants reported statistically significant changes in their clinical condition, with little added value to the addition of tactile and haptic feedback. The advantages of tactile and haptic stimulation are questioned, at least in the context of only one brief exposure session and the equipment used.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".