Recognizing Emotions on Static and Animated Avatar Faces
Bibliographic record
Abstract
Participants were shown static or animated versions of a FACS-compliant avatar face in the work of P. Ekman and W.V. Friesen (1978), and asked to identify the emotion that the face was displaying. In the first version of the face, happiness, sadness, and surprise were all recognized at high rates (80% or more) whatever the stimulus type, while anger and disgust had low recognition rates. The neutral face was not well recognized when viewed as a static image, but was recognized significantly more often when animated. In a second experiment, small changes made to "tweak" the neutral and angry faces were only partially successful. About half the people recognized the static angry face; far fewer recognized the animated version; and most people wrongly identified the neutral face, both in its static and its animated version. More surprisingly, the recognition rates for happiness, sadness and surprise dropped significantly during the second experiment, for both the static and the animated faces. This may be due to changes in the way the stimuli were presented between the first and the second experiment. These results suggest that people are sensitive to small, seemingly innocuous changes in the presentation of avatar faces
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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 | 0.003 |
| 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) | 0.002 | 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".