The impact of an acute social stress on the use of visual information in facial expression categorization
Bibliographic record
Abstract
Langner et al. (2009) investigated the impact of social anxiety on the use of visual information in angry vs. neutral emotion recognition using Bubbles (Gosselin & Schyns, 2001). They found that both groups used mostly the same facial features in higher spatial frequencies, but that socially anxious participants relied more than nonanxious participants on low spatial frequencies. Here, we investigated if inducing an acute social stress alters the visual strategies for facial expression categorization using the Bubbles method. Twenty-five men were first 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. Sparse versions of emotional faces (angry, fearful, happy or disgust) were created by sampling facial information at random spatial locations and at four spatial frequency bands. Following the stress induction (or control) phase, participants categorized 500 sparsed stimuli on average. The accuracy was maintained at 62.5% for each facial expression by adjusting the number of bubbles on a trial-by-trial basis. The visual information useful for the task was determined using an analysis procedure that amounts to a multiple linear regression on the bubbles masks and on the participant’s response accuracy. This analysis was performed for each condition separately. The facial features useful in high spatial frequencies were similar for both conditions (e.g. the eyes for fear and angry, the mouth for happy). However, the stress condition induced a bias for the use of lower spatial frequencies for angry, fear and happy. For disgust, we replicated Smith et al. (2005) in the control condition, i.e. the importance of the nose winkler. However, in the stress condition, this bias disappeared in favour of the lips. Our results show that an social stress alters the perception of facial expressions of emotions. 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) | 0.000 | 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".