Seeing is Not Thinking: Testing Capabilities of VR to Promote Perspective-Taking
Bibliographic record
Abstract
Virtual Reality (VR) technologies offer compelling experiences by allowing users to immerse themselves in simulated environments interacting through avatars. However, despite its ability to evoke emotional responses, and seeing 'through the eyes' of the displayed other, it remains unclear to what extent VR actually fosters perspective-taking (PT) or thinking about others' thoughts and feelings. It might be that the common belief that one can "become someone else" through VR is misleading, and that engaging situations through a different viewpoint does not produce a different cognitive standpoint. To test this, we conducted a 2 (perspective, first-person or third-person) by 2 (perspective-taking task or no task) to examine effects on perspective taking, measured via audio-recordings afforded by the think-aloud protocol. Our data demonstrate that while first-person perspective (1PP) facilitates perceived embodiment, it has no appreciable influence on perspective-taking. Regardless of 1PP or third-person perspective (3PP), perspective-taking was substantially and significantly increased when users were given a specific task prompting them to actively consider a character's perspective. Without such tasks, it seems that participants default to their own viewpoints. These data highlight the need for intentional design in VR experiences to consider content rather than simply viewpoint as key to authentic perspective-taking. To truly harness VR's potential as an "empathy machine," developers must integrate targeted perspective-taking tasks or story prompts, ensuring that cognitive engagement is an active component of the experience.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".