Empathy Toward Virtual Humans Depicting a Known or Unknown Person Expressing Pain
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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