Mimicry and the Detection of gradual changes of facial expressions: the case of Anger, Happiness, and Identity
Bibliographic record
Abstract
In a change detection task, participants were exposed to video morphs of a neutral face gradually evolving into the expression of either Anger or Happiness (Emotional) or a change in Identity (Non-emotional). Participants had to report a change in the display as soon as they detected it. Subsequently, they were asked to identify the type of change and rate the vividness of their experience. The results showed that overall electromyography (EMG) levels of the corrugator muscle selectively decreased in response to changes in Happiness. In contrast, the zygomaticus muscle exhibited a greater decrease in response to Identity changes. When looking at the EMG signal evolution through the video presentation, both muscles exhibited an early decrease in activity in response to Happiness. However, a significant decrease in the activity of the zygomaticus muscle was observed during the detection of Anger in a later time window, indicating that the processing of Anger requires more time. A similar decrease in zygomaticus muscle activity was observed during the detection of Identity changes; however, this occurred from an early time window.Additionally, patterns of EMG spontaneous responses obtained at identification and vividness were similar to those observed at detection. The corrugator muscle activity was elicited based on stimulus valence (e.g., positive vs negative). In contrast, the zygomaticus muscle activity was differentially elicited depending on whether the stimuli were emotional or non-emotional. We suggest that spontaneous facial reactions reflect emotional valuation (i.e., significance) and categorisation (e.g., grouping) processes in a complementary manner.
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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 | 0.004 |
| 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".