Contagious yawning in virtual reality is affected by actual, but not simulated, social presence
Bibliographic record
Abstract
Contagious yawning occurs in humans and a few other highly social animals following the detection of yawns in others, yet the factors influencing the propagation of this response remain largely unknown. Stemming from earlier laboratory research, we conducted five experiments to investigate the effects of social presence on contagious yawning in virtual reality (VR). We show that, similar to a traditional laboratory setting, having a researcher present during testing significantly inhibited contagious yawning in VR, even though participants were viewing a virtual environment and unable to see the researcher. Unlike previous research, however, manipulating the social presence in VR (i.e., embedding recording devices and humanoid avatars within the simulation) did not affect contagious yawning. These experiments provide further evidence that social presence is a powerful deterrent of yawning in humans, which warrants further investigation. More generally, these findings also have important applications for the use of VR in psychological research. While participants were quite sensitive to social stimuli presented in VR, as evidenced by contagious yawning, our results suggest a major difference in the influence of social factors within real-world and virtual environments. That is, social cues in actual reality appear to dominate and supersede those in VR.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".