Are You Empathizing with Me? Exploring External Expressions of Empathy in Interpersonal VR Communication
Bibliographic record
Abstract
Empathy is central to social interaction, yet how it is externally expressed in virtual reality (VR) communication remains underexplored. In this study, we examined how directionality-aware cues of empathy, such as mimicry, eye contact, and body proximity, relate to cognitive and emotional empathy. We designed high- and low-empathy scenarios and recruited participants with acting experience to ensure clear emotional expressions. Our findings indicate that facial mimicry patterns differ by empathy type: cognitive empathy involves subtle, speech-related muscle movements, whereas emotional empathy is associated with more intense affective expressions. Interestingly, we also found that while facial expressions and lower-body mimicry tend to emerge unconsciously, upper-body mimicry occurs more consciously, suggesting distinct pathways of empathic embodiment. We also observed that vocal intensity mimicry and pitch variability serve as important indicators of empathy, and a consistent hand approach is closely linked to empathy. Additionally, emotional empathy fosters longer eye contact, whereas cognitive empathy stabilizes gaze and head movements. Finally, we constructed machine learning models to predict empathy from these external expressions. Our best classifier achieved an accuracy of 0.756 for cognitive empathy and 0.704 for emotional empathy, indicating the feasibility of objective assessment. These findings provide a deeper understanding of how empathy is manifested in VR communication and support the development of empathy-aware virtual agents and training systems.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".