Empathy Toward Virtual Humans Depicting a Known or Unknown Person Expressing Pain
Bibliographic record
Abstract
This study is about pain expressed by virtual humans and empathy in users immersed in virtual reality. It focuses on whether people feel more empathy toward the pain of a virtual human when the virtual human is a realistic representation of a known individual, as opposed to an unknown person, and if social presence is related to users' empathy toward a virtual human's pain. The 42 participants were immersed in virtual reality using a large immersive cube with images retro projected on all six faces (CAVE-Like system) where they can interact in real time with virtual characters. The first immersion (baseline/control) was with a virtual animal, followed by immersions involving discussions with a known virtual human (i.e., the avatar of a person they were familiar with) or an unknown virtual human. During the verbal exchanges in virtual reality, the virtual humans expressed acute and very strong pain. The pain reactions were identical in terms of facial expressions, and verbal and nonverbal behaviors. The Conditions by Time interactions in the repeated measures analyses of variance revealed that participants were empathic toward both virtual humans, yet more empathic toward the known virtual human. Multivariate regression analyses revealed that participants' feeling of social presence--impression that the known virtual character is really there, with them--was a significant predictor of empathy.
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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 | 0.004 |
| 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 | 0.001 | 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".