Exploring the effects of visual perspective on the ERP components of empathy for pain
Bibliographic record
Abstract
Previous neurophysiological research suggests that there are event-related potential (ERP) components associated with empathy for pain: an early affective component (N2) and two late cognitive components (P3/LPP). The current study investigated whether and how the visual perspective from which a painful event is observed affects these ERP components. Participants viewed images of hands in pain vs. not in pain from a first-person or third-person perspective. We found that visual perspective influences both the early and late components. In the early component (N2), there was a larger mean amplitude during observation of pain vs no-pain exclusively when images were shown from a first-person perspective. We suggest that this effect may be driven by misattributing the on-screen hand to oneself. For the late component (P3), we found a larger effect of pain on mean amplitudes in response to third-person relative to first-person images. We speculate that the P3 may reflect a later process that enables effective recognition of others’ pain in the absence of misattribution. We discuss our results in relation to self- vs other-related processing by questioning whether these ERP components are truly indexing empathy (an other-directed process) or a simple misattribution of another’s pain as one’s own (a self-directed process).
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".