Efficient information for recognizing pain in facial expressions
Bibliographic record
Abstract
BACKGROUND: The face as a visual stimulus is a reliable source of information for judging the pain experienced by others. Until now, most studies investigating the facial expression of pain have used a descriptive method (i.e. Facial Action Coding System). However, the facial features that are relevant for the observer in the identification of the expression of pain remain largely unknown despite the strong medical impact that misjudging pain can have on patients' well-being. METHODS: Here, we investigated this question by applying the Bubbles method. Fifty healthy volunteers were asked to categorize facial expressions (the six basic emotions, pain and neutrality) displayed in stimuli obtained from a previously validated set and presented for 500 ms each. To determine the critical areas of the face used in this categorization task, the faces were partly masked based on random sampling of regions of the stimuli at different spatial frequency ranges. RESULTS: Results show that accurate pain discrimination relies mostly on the frown lines and the mouth. Finally, an ideal observer analysis indicated that the use of the frown lines in human observers could not be attributed to the objective 'informativeness' of this area. CONCLUSIONS: Based on a recent study suggesting that this area codes for the affective dimension of pain, we propose that the visual system has evolved to focus primarily on the facial cues that signal the aversiveness of pain, consistent with the social role of facial expressions in the communication of potential threats.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".