Facial expression recognition impairment following acute social stress
Bibliographic record
Abstract
Recently, von Dawans et al. (2012) showed that stress exposure increases facial trustworthiness judgements. Given the relation between trustworthiness and the presence/absence of subtle visual features related to happiness and anger (Oosterhof & Todorov, 2008), we verified if social stress modulates the visual perception of facial expressions. Twenty-nine men were submitted to a social stress (i.e. Trier Social Stress Test for Groups) or a control condition (i.e. identical setting save for the socio-evaluative threat component) in a counterbalanced order. Facial expression recognition was then measured using a homemade version of the ‘facial expression megamix’ (Young et al.,1997) in which each of the six basic facial expressions plus neutrality were morphed with each other at seven different percentages (from 14/86 in intervals of 12%). The task was to decide which expression the image most resembled. Recognition accuracy for each facial expression when it was dominant in the morph (i.e. over 50%) was first computed. We found that social stress modulates accuracy scores only for disgust (Mstress= 81%; Mcontrol=89%; t(28)=-3.20, p=0.028; bonferroni corrected). We also verified if the emotion signal necessary to detect each facial expression when they were part of the morph was modulated by stress. For each facial expression, we calculated, separately for each percentage level, the proportion of times that it was correctly identified as being part of the morph. This produced a curve on which we fitted a cumulated gaussian to find at what percentage level an expression was detected 50% of the time. Our results show that stress decreased the sensitivity to disgust (t(28)=3.55, p=0.007; bonferroni corrected). These results indicate that an acute social stress alters the perception of facial expressions, more specifically it decreases the sensitivity to the disgust expression. Meeting abstract presented at VSS 2015
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".