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Record W2604723155 · doi:10.1109/3dui.2017.7893343

Awestruck: Natural interaction with virtual reality on eliciting awe

2017· article· en· W2604723155 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser University
KeywordsVirtual realityNatural (archaeology)Computer scienceHuman–computer interactionGeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.091
GPT teacher head0.336
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations23
Published2017
Admission routes2
Has abstractyes

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