A bias to underestimate pain is linked with mental representations of pain facial expressions
Bibliographic record
Abstract
Evaluating the pain experienced by someone else is a skill of high social and biological importance. Interestingly, underestimation bias in pain judgments are often observed. The present study aims at investigating the way an observer has encoded the appearance of facial expressions of pain in visual memory as one potential perceptual source for this bias. The mental representation of pain facial expressions was extracted in 49 participants using Reverse Correlation (Mangini & Biederman, 2004). On each trial, a base face embedded in white sinusoidal noise was presented, and participants were asked to judge, on a scale from 0 to ten, the degree to which it expressed pain. Participants were then presented with videos of individuals experiencing different levels of pain, after which they were asked to evaluate their pain. A region-of-interest analysis was then conducted to measure the salience with which three core facial features associated with pain expressions were coded in the mental representations (i.e. eyes narrowing, brow lowering, nose wrinkling/upper-lip raising). A correlation between the saliency of these three features and the underestimation bias of each participant was then calculated. The results confirm the presence of an underestimation bias in our sample (t(48)=-8.5, p<.001) and replicate previous findings showing that brow lowering and nose wrinkling/upper-lip raising are given more weight than eye narrowing in the average mental representation (Blais et al., 2019). The underestimation bias was also significantly correlated with the saliency of the brow lowering (r=0.32, p=.03) and the nose wrinkling/upper-lip raising (r=-0.44, p=.002) features, but not with the saliency of eye narrowing (r=-0.10, p=.48). Overall, these results indicate that perceptual factors may underlie the underestimation bias. Individuals that encode pain expressions by giving more importance to nose wrinkling/upper-lip raising than brow lowering show a higher tendency to underestimate the pain experienced by 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 | 0.002 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".