Sad Faces and Fearful Bodies: A test of two models of emotion perception
Bibliographic record
Abstract
Perception of facial displays of emotion is influenced by context; error rates and reaction times increase when the emotion displayed by the face (e.g., fear) is incongruent with the emotion displayed by the body (e.g., anger) (Meeren et al., 2005). Two models of emotion perception invoke different mechanisms to explain context effects. Although both models predict that congruency effects will be maximal when emotions are similar, they do not always agree on which emotions are most similar. To compare the predictive validity of these two models we measured context effects for three emotions for which the two models make different predictions: sad, anger, and fear. Whereas the Dimensional model predicts largest effects when fear and anger are paired because both are negatively valenced and high in arousal, the Emotional Seed model predicts largest effects whenever fear or anger are paired with sad because sad faces are more physically similar to anger or fear faces than anger and fear faces are to each other (Susskind et al., 2007). Adults categorized each facial expression when presented on congruent and incongruent bodies. They were instructed to ignore the body. Stimuli were presented for 600ms in Experiment 1 (n = 24) and for an unlimited time in Experiment 2 (n = 17 to date). Accuracy, response times, and proportion of errors were analyzed. In Experiment 1, congruency effects were pervasive but strongest when sad faces were presented on fear bodies (p < .01) , followed by when fear faces were presented on sad bodies (p <.05). Congruency effects were dampened in Experiment 2, but were still strongest when sad faces were paired with fear bodies (p <.03). Collectively, these results question the predictive validity of both models and suggest that fear postures may hold a special status in emotion perception. Meeting abstract presented at VSS 2012
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".