The effect of acute social stress on the recognition of facial expression of emotions
Bibliographic record
Abstract
This study investigates the effect of acute social stress on the recognition of facial expression of emotions in healthy young men. Participants underwent both a standardized psychosocial laboratory stressor (TSST-G) and a control condition. Then, they performed a homemade version of the facial expressions megamix. All six basic emotions were included in the task. First, our results show a systematic increase in the intensity threshold for disgust following stress, meaning that the participants' performance with this emotion was impaired. We suggest that this may reflect an adaptive coping mechanism where participants attempt to decrease their anxiety and protect themselves from a socio-evaluative threat. Second, our results show a systematic decrease in the intensity threshold for surprise, therefore positively affecting the participants' performance with that emotion. We suggest that the enhanced perception of surprise following the induction of social stress may be interpreted as an evolutionary adaptation, wherein being in a stressful environment increases the benefits of monitoring signals indicating the presence of a novel or threatening event. An alternative explanation may derive from the opposite nature of the facial expressions of disgust and surprise; the decreased recognition of disgust could therefore have fostered the propensity to perceive surprise.
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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 | 0.001 |
| 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) | 0.002 | 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".