Detecting deception in pain expressions: the structure of genuine and deceptive facial displays
Bibliographic record
Abstract
Clinicians tend to assign greater weight to non-verbal expression than to patients' self-report when judging the location and severity of pain. Judgments can misrepresent the actual experience because patients can successfully alter their pain expressions. The present research provides a basis for discriminating genuine and deceptive pain expressions by expanding detailed accounts of facial expressions to include previously unexamined variables, including study of temporal patterns and contiguity of facial actions as well as the occurrence of specific deception cues. Low back patients' facial expressions (n=40) were videotaped at rest and while undergoing a painful straight leg raise with instructions to: (1) genuinely express their pain, or (2) pretend that it did not hurt. As well, they were asked to fake pain without moving. The Facial Action Coding System was used to describe and quantify facial activity. The different types of expression were compared on the frequency, type, intensity, temporal pattern and contiguity of facial actions, as well as on the frequency of specific deception cues. Findings confirmed the difficulty of discriminating the facial expressions, but indicated that faked pain expressions show a greater number of pain-related and non-pain-related actions, have a longer peak intensity and overall duration, and the facial actions observed tend to be less temporally contiguous than are those in genuine pain expressions. The differences between masked pain and neutral expressions were subtle, with a greater frequency of mouth opening and residual eyebrow movement in masked pain expressions. Thus, there is an empirical basis for discriminating genuine and deceptive facial displays.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".