The visual representation of pain facial expressions: a high-definition transcranial direct current stimulation study
Bibliographic record
Abstract
As pain and empathy neural signatures share a great deal of resemblance, this social competence has often been linked to the adequacy of pain estimation, a highly adaptive yet frequently inaccurate perceptual skill. It was recently suggested that the neurostimulation of the right inferior frontal gyrus (rIFG), a key region of this network, could temporarily alter empathic abilities. Nonetheless, the impact of these neural processes on pain perception in faces, namely the estimation bias and the ability to detect the expression’s fine variations (called sensitivity) remains unclear. We applied high-definition transcranial direct current stimulation over the rIFG of 22 participants but previous results of empathy diminution, as measured by the Multifaceted Empathy Test, were not replicated in the present study [𝘍(2, 42)=.57, 𝘱=.57]. Visual representations (VRs) of pain facial expressions were extracted using the data-driven method Reverse correlation. Pixel-by-pixel analyses were conducted: a repeated measure ANOVA showed no effect of stimulation conditions [𝘱’s≥.24] but a one-sample t-test confirmed the presence of facial features typically associated with pain percept in our participants’ VR [𝘛𝘤𝘳𝘪𝘵=2.3, 𝘬=461, 𝘱<.05]. These classification images were then submitted to the OpenFace algorithm which revealed classical pain signals were almost systematically activated (AU4, AU7, AU9 & AU10). Noticeably, these VRs were still rated as primarily expressing disgust, sadness, and anger by an independent group (𝘕=30). Additionally, our sample exhibited a clear underestimation tendency [𝜇=-1.54], a suboptimal sensitivity level [𝜇=-.40], and these two parameters were not correlated [𝘳=-.26, 𝘱=.24], thus replicating previous work. The degree of variability revealed in the visual representations, as well as the ambiguity they seem to generate, may reflect some overlap in emotional representations. It would be interesting to explore the variability in those overlaps and assess how it may influence individuals’ ability to estimate pain.
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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) | 0.002 | 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".