Are You Awed Yet? How Virtual Reality Gives Us Awe and Goose Bumps
Bibliographic record
Abstract
"Awe" is a category of emotion within the spectrum of self-transcendent experiences. Awe has wellness benefits, with feelings of social interconnectivity and increased life satisfaction. However, awe experiences remain rare in our everyday lives, and rarer in lab environments. We posit that Virtual Reality (VR) may help to make self-transcendent and potentially transformative experiences of awe more accessible to individuals. Here, we investigated how interactive VR as a positive technology may elicit awe, and how features of aesthetic beauty/scale, familiarity, and personalization (self-selection of travel destinations) may induce awe. In this mixed-methods study, participants used an interactive VR system to explore Earth from ground and orbit. We collected: introspective interviews and self-report questionnaires with participants' experience of awe; information on personality traits and gender; and we recorded physiological goose bumps on the skin (using an arm-mounted goose bump camera instrument), which is a documented marker of an awe experience. Results showed that on a scale of 0-100 for self-reported awe, four different interactive VR environments yielded an average awe rating of 79.7, indicating that interactive VR can indeed induce awe. 43.8% of participants experienced goose bumps: awe ratings positively correlated with the occurrence of goose bumps with those who experienced goose bumps having showed significantly higher ratings of awe than those who did not. Most (64%) of the goose bumps occurred when participants self-selected their VR environment. Participant statements from the interviews were characteristic of an awe-inspiring experience, revealed themes of social connection, and usability problems with the VR interface. Personality traits yielded no clear correlation to awe ratings, and females appear to experience more goose bumps than males. In summary: (1) Interactive VR can elicit awe, especially within familiar, self-selected environments; (2) Physiological goose bumps can be recorded to provide reliable, non-intrusive indications of awe; (3) Care must be taken to design interaction interfaces that do not impede awe; and (4) While personality traits are not correlated to awe ratings, goose bumps were experienced more frequently among females. We aim to conduct future studies using custom VR environments, interfaces, and additional physiological measures to provide further insight into awe.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".