The effects of repeated facial motor and affective matching on Identification and detection of static and dynamic facial expressions
Bibliographic record
Abstract
Mimicry and imitation are thought to be important antecedents of embodied, face-based emotional perception. Simulationist models endorsing the “facial feedback hypothesis” state that proprioception from the face promote subjective feelings. However, subjective–affective- states also causally influence bodily signals. To gauge the differential contributions of facial feedback and affect, we investigated the effects of an emotional-imitation task on immediate emotion identification and a subsequent emotional change detection. Participants first performed an emotion identification task. They were presented with static faces and asked to adopt the same facial expression (Motor group) or try to feel the same emotion (Feel group). A control group passively observed the faces (Watch group). A forced-choice emotion identification followed each trial. After the imitation task, all participants performed a change detection task. First, faces gradually evolved from neutral to full-blown expressions, and participants pressed a button at the moment they perceived a change. In a second bloc, the same videos were reversed. Participants pressed a button when they thought the face had become neutral again. The main results demonstrated that face imitation affected emotion identification immediately. The Feel intervention affected performance only at a subsequent detection task. Moreover, Motor matching facilitated performance in the anger condition but affected the processing of Happiness. Affective Matching, however, affected performance in the Anger condition alone. We surmise that both interventions were task- and time-specific. We argue that voluntary facial imitation and affective matching differentially affect Anger processing, possibly because of emotion (down) regulation promoted by Facial Feedback and Simulation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".