Effects of system- and media-driven immersive capabilities on presence and affective experience
Bibliographic record
Abstract
Abstract Virtual reality (VR) is receiving widespread attention as a delivery tool for exposure therapies. The advantage offered by VR over traditional technology is a greater sense of presence and immersion, which magnifies user effects and enhances the effectiveness of exposure-based interventions. The current study systematically examined the basic factors involved in generating presence in VR as compared to standard technology, namely (1) system-driven factors that are exclusive to VR devices while controlling general factors such as field of view and image quality; (2) media-driven factors of the virtual environment eliciting motivational salience through different levels of arousal and valence (relaxing, exciting and fear evoking stimuli); and (3) the effects of presence on magnifying affective response. Participants (N = 14) watched 3 different emotionally salient videos (1 × fear evoking, 1 × relaxing and 1 × exciting) in both viewing modes (VR and Projector). Subjective scores of user experience were collected as well as objective EEG markers of presence (frontal alpha power, theta/beta ratio). Subjective and objective presence was significantly greater in the VR condition. There was no difference in subjective or objective presence for stimulus type, suggesting presence is not moderated by arousal, but may be reliant on activation of motivational systems. Finally, presence did not magnify feelings of relaxation or excitement, but did significantly magnify users’ experience of fear when viewing fear evoking stimuli. This is in line with previous literature showing strong links between presence and generation of fear, which is vital in the efficacy of exposure therapies.
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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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".