The face is not an empty canvas: how facial expressions interact with facial appearance
Bibliographic record
Abstract
Faces are not simply blank canvases upon which facial expressions write their emotional messages. In fact, facial appearance and facial movement are both important social signalling systems in their own right. We here provide multiple lines of evidence for the notion that the social signals derived from facial appearance on the one hand and facial movement on the other interact in a complex manner, sometimes reinforcing and sometimes contradicting one another. Faces provide information on who a person is. Sex, age, ethnicity, personality and other characteristics that can define a person and the social group the person belongs to can all be derived from the face alone. The present article argues that faces interact with the perception of emotion expressions because this information informs a decoder's expectations regarding an expresser's probable emotional reactions. Facial appearance also interacts more directly with the interpretation of facial movement because some of the features that are used to derive personality or sex information are also features that closely resemble certain emotional expressions, thereby enhancing or diluting the perceived strength of particular expressions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".