Is fear in your head? A comparison of instructed and real-life expressions of emotion in the face and body.
Bibliographic record
Abstract
The majority of emotion perception studies utilize instructed and stereotypical expressions of faces or bodies. While such stimuli are highly standardized and well-recognized, their resemblance to real-life expressions of emotion remains unknown. Here we examined facial and body expressions of fear and anger during real-life situations and compared their recognition to that of instructed expressions of the same emotions. In order to examine the source of the affective signal, expressions of emotion were presented as faces alone, bodies alone, and naturally, as faces with bodies. The results demonstrated striking deviations between recognition of instructed and real-life stimuli, which differed as a function of the emotion expressed. In real-life fearful expressions of emotion, bodies were far better recognized than faces, a pattern not found with instructed expressions of emotion. Anger reactions were better recognized from the body than from the face in both real-life and instructed stimuli. However, the real-life stimuli were overall better recognized than their instructed counterparts. These results indicate that differences between instructed and real-life expressions of emotion are prevalent and raise caution against an overreliance of researchers on instructed affective stimuli. The findings also demonstrate that in real life, facial expression perception may rely heavily on information from the contextualizing body. (PsycINFO Database Record
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| 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".