Nice people are easy to smile with: Deliberate imitation of facial expressions depends on personal characteristics
Bibliographic record
Abstract
Facial expressions are important in social interactions, where they are often mutually exchanged in face-to-face situations but little is known about the factors that influence such deliberate expressions. Here, we investigated the effect of personality characteristics on voluntary facial expressions. A short statement describing a positive, negative, or neutral characteristic of a target person was followed by her smiling or frowning portrait. Participants were to imitate this expression. Reaction times and accuracy of the facial responses were derived from electromyography of M. corrugator and M. zygomaticus major ; in addition, EEG-derived event-related potentials (ERP) were obtained. Responses were faster and more accurate when facial expressions were congruent with the personal characteristics. Congruency effects in ERPs, were observed in the P200 component, the early posterior negativity (EPN), and the P300. Hence, personal characteristics can modulate the deliberate imitation of facial expressions, based on modulations of reflexive attention and proceeding through several cognitive processing levels. This is the first demonstration that deliberate expressions of emotions are influenced by affective knowledge about the communication partner.
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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 | 0.005 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".