Biological postural oscillations during facial expression of pain in virtual characters modulate early and late ERP components associated with empathy: A pilot study
Bibliographic record
Abstract
There is a surge in the use of virtual characters in cognitive sciences. However, their behavioural realism remains to be perfected in order to trigger more spontaneous and socially expected reactions in users. It was recently shown that biological postural oscillations (idle motion) were a key ingredient to enhance the empathic response to its facial pain expression. The objective of this study was to examine, using electroencephalography, whether idle motion would modulate the neural response associated with empathy when viewing a pain-expressing virtual character. Twenty healthy young adults were shown video clips of a virtual character displaying a facial expression of pain while its body was either static ( Still condition ) or animated with pre-recorded human postural oscillations ( Idle condition ). Participants rated the virtual human's facial expression of pain as significantly more intense in the Idle condition compared to the Still condition . Both the early (N2–N3) and the late (rLPP) event-related potentials (ERPs) associated with distinct dimensions of empathy, affective resonance and perspective-taking, respectively, were greater in the Idle condition compared to the Still condition . These findings confirm the potential of idle motion to increase empathy for pain expressed by virtual characters. They are discussed in line with contemporary empathy models in relation to human-machine interactions.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".