The use of visual information in the recognition of posed and spontaneous facial expressions
Bibliographic record
Abstract
Recognizing facial expressions is crucial for the success of social interactions, and the visual processes underlying this ability have been the subject of many studies in the field of face perception. Nevertheless, the stimuli used in the majority of these studies consist of facial expressions that were produced on request rather than spontaneously induced. In the present study, we directly compared the visual strategies underlying the recognition of posed and spontaneous expressions of happiness, disgust, surprise, and sadness. We used the Bubbles method with pictures of the same individuals spontaneously expressing an emotion or posing with an expression on request. Two key findings were obtained: Visual strategies were less systematic with spontaneous than with posed expressions, suggesting a higher heterogeneity in the useful facial cues across identities; and with spontaneous expressions, the relative reliance on the mouth and eyes areas was more evenly distributed, contrasting with the higher reliance on the mouth compared to the eyes area observed with posed expressions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".