How We See Changes How We Feel: Investigating the Effect of Visual Point-of-View on Decision-Making in VR Environments
Bibliographic record
Abstract
Virtual reality (VR) can immerse users into engaging experiences, affording opportunities to study behaviour in simulated contexts such as decision-making processes. However, methodological research into designing meaningful VR experiences - experiences that promote appreciation and deeper understanding of a work - is still underdeveloped. In this two-part study, we investigate how visual point-of-view (POV) in VR impacts feelings of meaningfulness and empathy as well as objective decision-making processes. Our study revolves around a VR application that situates users in moral dilemmas from three different POVs. Data from the choices made is augmented with self-reported subjective data. We find that, from different POVs, users' subjective feelings do show change; users show greater empathy for virtual agents and have an increasingly meaningful experience from a first-person perspective, even if this is not always reflected in changes in their decisions. Finally, we discuss the implications of our findings in the context of VR application design.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".