Awestruck: Natural interaction with virtual reality on eliciting awe
Bibliographic record
Abstract
In the study of transformative experiences, the feeling of awe is found to alter an individual's perception in positive, lasting manners. Our research aims to understand the potential for interactive virtual reality (VR) in eliciting awe, through a framework based on collection of physiological data alongside self-report and phenomenological observations that demonstrate awe. We conducted a mixed-methods experiment to test whether VR is effective in eliciting awe, and if this effect might be modulated by the type of natural interaction in the form of a “flight” lounger vs. “standing”. Results demonstrate both interaction paradigms were equally awe-inspiring, with overall physiological (in the form of goose bumps with a 43.8% incidence rate) and self-report data (overall awe rating of 79.7%), and females showing more physiological signs of awe than males. Observations revealed 360-degree interaction and operability of hand-held controllers could be improved, with the consequence of designing even more effective transformative experiences.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".