Brain responses to the vicarious facilitation of pain by facial expressions of pain and fear
Bibliographic record
Abstract
Observing pain in others facilitates self-pain in the observer. Vicarious pain facilitation mechanisms are poorly understood. We scanned 21 subjects while they observed pain, fear and neutral dynamic facial expressions. In 33% of the trials, a noxious electrical stimulus was delivered. The nociceptive flexion reflex (NFR) and pain ratings were recorded. Both pain and fear expressions increased self-pain ratings (fear > pain) and the NFR amplitude. Enhanced response to self-pain following pain and fear observation involves brain regions including the insula (INS) (pain > fear in anterior part), amygdala, mid-cingulate cortex (MCC), paracentral lobule, precuneus, supplementary motor area and pre-central gyrus. These results are consistent with the motivational priming account where vicarious pain facilitation involves a global enhancement of pain-related responses by negatively valenced stimuli. However, a psychophysiological interaction analysis centered on the left INS revealed increased functional connectivity with the aMCC in response to the painful stimulus following pain observation compared to fear. The opposite connectivity pattern (fear > pain) was observed in the fusiform gyrus, cerebellum (I-IV), lingual gyrus and thalamus, suggesting that pain and fear expressions influence pain-evoked brain responses differentially. Distinctive connectivity patterns demonstrate a stronger effect of pain observation in the cingulo-insular network, which may reflect partly overlapping networks underlying the representation of pain in self and others.
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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 | 0.000 |
| 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) | 0.002 | 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".