The multilevel organization of vicarious pain responses: Effects of pain cues and empathy traits on spinal nociception and acute pain
Bibliographic record
Abstract
The shared-representation model of empathy suggests that vicarious pain processes rely partly on the activation of brain systems underlying self-pain in the observer. Here, we tested the hypothesis that self-pain may be facilitated by the vicarious priming of neural systems underlying pain perception. Pictures illustrating painful agents applied to the hand or the foot (sensory information), or painful facial expressions (emotional information) were shown to 43 participants to test the effects of vicarious pain on the nociceptive flexion reflex (NFR) of the lower limb and pain intensity and unpleasantness produced by transcutaneous electrical stimulation applied over the sural nerve. Results confirmed the expected priming effects of vicarious pain on spinal and perceptual processes. However, for comparable pain intensity and arousal evoked by the pain pictures, the facilitation of the NFR and the self-pain unpleasantness measurements was more robust in response to pictures depicting pain sensory compared to emotional information. Furthermore, the facilitation of the NFR by pain pictures was positively correlated with the empathy trait of the observer. In contrast, the change in perceived shock-pain intensity was negatively correlated with empathic traits. This dissociation implies that low-level vicarious priming processes underlying pain facilitation may be downregulated at higher pain-processing stages in individuals reporting higher levels of empathy. We speculate that this process contributes to reducing self-other assimilation and is necessary to adopt higher-order empathic responses and altruistic behaviors.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".