Emotional anti-faces reveal contrastive coding of facial expressions
Bibliographic record
Abstract
It is widely thought that facial expressions are recognized in relation to one of six or more basic prototypes, with cross cultural and neuropsychological studies supporting these prototypes as the fundamental building blocks of emotional representation (Ekman, 1999). However, little work has examined directly whether there is a non-arbitrary underlying logic based on physical movements of the face that can explain why expressions look the way they do. Why do we raise our brows in fear and wrinkle our noses in disgust? According to evolutionary accounts, facial movements serve adaptive functions to regulate an organism's interaction with the environment. Confirming this view, we recently demonstrated that production of fear and disgust expressions has opposing effects on sensory intake, with fear increasing and disgust decreasing visual field size, saccadic velocity, and nasal inspiration (Susskind et al, 2008, Nature Neuroscience 11, 843–850). We reasoned that these opposing physical actions have come to represent important social cues for recognizing emotions in the face. Specifically, we hypothesized that the brain would represent expressions as perceptual opposites through opponent neural coding. Employing a computational model of facial appearance, we created a set of photorealistic expression prototypes and their visual-statistical opposites (i.e., emotional anti-faces). Categorization data revealed that not only do emotional anti-faces physically oppose basic emotion prototypes, but subjectively convey opposing emotional meanings. We next tested the effects of perceptually adapting to emotional faces and anti-faces on expression discrimination. As predicted by opponent coding, adapting to facial expressions impaired expression discrimination, and adapting to their anti-faces enhanced expression discrimination. Analogous results were found for discrimination of fear, disgust, happiness, and sadness. These results provide evidence for a new theory that emotional expressions are decoded not only as discrete categories, but by opponent representations that highlight contrasting facial actions.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".