Modulating observer's pain by manipulating the diagnosticity of face stimuli for the recognition of the expression of pain
Bibliographic record
Abstract
Recent findings suggest that the emotional valence of visual stimuli can modulate pain perception (Rhudy et al. 2005); unpleasant images increase pain reports, while pleasant images has the opposite effect. Here, we modulated the observer's perception of acute shock-pain by varying the information provided to recognize the pain facial expression (unpleasant stimuli). Last year at VSS, Roy et al. (Abstract 710) described the visual information subtending the recognition of the facial expression of pain. Based on these results, we created two masks: one revealing the more useful (top 5 %) information for the identification of pain expression (optimal mask) and one revealing the less useful information (bottom 5%—neutral mask). Twenty stimuli were created by applying these masks to ten different static facial expression of pain. A pilot study ensured that the optimally-masked stimuli led to the perception of negative emotions while the neutrally-masked stimuli led to the perception of positive emotions. Twenty-four normal volunteers received transcutaneous electrical stimulation of the sural nerve (30 ms) at the offset of each visual stimulus (1 s), and were asked to rate the intensity and the unpleasantness of shock-pain on visual analog scales. Preliminary results show that pain intensity and unpleasantness are judged less intense when the shock is given after the neutrally-masked stimuli than after the optimally-masked stimuli. These results are consistent with an effect of emotional valence on pain perception and may explain the hyperalgesic effects induced by the perception of pain in others reported in studies on pain empathy.
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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 | 0.002 |
| 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) | 0.003 | 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".