Virtual experience, real consequences: the potential negative emotional consequences of virtual reality gameplay
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.
Bibliographic record
Abstract
Abstract As virtual reality (VR) technology enters mainstream markets, it is imperative that we understand its potential impacts on users, both positive and negative. In the present paper, we build on the extant literature’s focus on the physical side effects of VR gameplay (e.g., cybersickness) by focusing on VR’s potential to intensify users’ experiences of negative emotions. We first conducted a preliminary survey to assess users’ emotional responses during VR gameplay, with the results suggesting that certain VR situations can in fact produce intense negative emotional experiences. We then designed an interactive scenario intended to elicit low to moderate amounts of negative emotion, wherein participants played out the scenario in either VR (using the HTC Vive) or on a laptop computer. Compared to the participants who enacted the scenario on the laptop, those in the VR condition reported higher levels of absorption, which in turn increased the intensity of their negative emotional response to the scenario. A follow-up questionnaire administered several hours later revealed that the intensified negative emotions resulting from VR had a significant positive correlation with negative rumination (i.e., harmful self-related thoughts related to distress). These results show that VR gameplay has the potential to elicit strong negative emotional responses that could be harmful for users if not managed properly. We discuss the practical and policy implications of our findings.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it