Virtually Connected: Do Shared Novel Activities in Virtual Reality Enhance Self-Expansion and Relationship Quality?
Bibliographic record
Abstract
According to self-expansion theory, sharing novel experiences with a romantic partner can help prevent boredom and maintain relationship quality. However, in today’s globalized modern world, partners spend less time together and are more likely to live apart than in previous generations, limiting opportunities for shared novel experiences. In two in-lab experiments, we tested whether shared novel activities in virtual reality (VR) could facilitate self-expansion, reduce boredom, and enhance relationship quality. In Study 1, couples (N = 183) engaged in a shared novel and exciting activity in either VR or over video. Participants in the VR condition reported greater presence (i.e., felt like they were in the same space as their partner) and were less bored during the interaction compared to the video condition, though no main effects emerged for reports of self-expansion or relationship quality (relationship satisfaction and closeness). Consistent with predictions, people who reported more presence, in turn, reported greater self-expansion, less boredom, and greater relationship quality. In Study 2, couples (N = 141) engaged in a novel and exciting or a mundane experience in VR. Results were mixed such that participants in the novel VR condition reported less boredom and greater closeness post-interaction, though no effects emerged for self-expansion or relationship satisfaction. In exploratory analyses accounting for immersion, couples who engaged in the novel virtual experience reported more self-expansion, less boredom, and greater closeness. The findings suggest that virtual interactions may have less potential than in-person interaction to promote self-expansion but offer interesting future directions given VR’s ability to enhance presence beyond video interactions.
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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 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".