Real human touch: performer-facilitated touch enhances presence and embodiment in immersive performance
Bibliographic record
Abstract
Despite being an embodied medium, virtual reality (VR) prioritizes vision and sound over the other senses. While touch has been demonstrated to foster a sense of presence and embodiment, most haptic research in VR focuses on uncanny vibration motors or limited experiences of touch with simple props. Meanwhile, immersive performances such as Eve 3.0 incorporate performer-facilitated touch in novel ways to evoke a complete and social experience of human touch in VR. In response, we conducted a mixed-methods study to investigate the experience of performer-facilitated touch in a 360° video segment from the immersive performance Eve 3.0. Using a 3 × 2 factorial design, we compared touch from a diary prop and performer in festival and laboratory settings. We found that performer-facilitated touch increased realistic behaviours and questionnaire measures of social presence, embodiment, and tactile realism. The setting also had a significant effect with festival participants demonstrating significantly more behaviours indicating presence, particularly in the no-touch condition. Participant descriptions reveal that in addition to touch, a rich narrative and vivid visuals of social interaction were just as important in immersing participants in the experience and making them feel present. We find that participant experiences are a co-creation situated at the intersection of artefact and context that require a willing suspension of disbelief. The authentic setting and performance artefact afforded a deep understanding of the rich and complex experience of human touch in immersive performance.
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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 | 0.002 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".