Happy but not so approachable: the social judgments of individuals with generalized social phobia
Bibliographic record
Abstract
BACKGROUND: We examined social approachability judgments in a psychiatric population that frequently experiences interpersonal difficulties and reduced social satisfaction, individuals with generalized social phobia (gSP). METHODS: Our objective was to broaden the understanding of the social cognitive tendencies of individuals with gSP by systematically investigating their interpretation of positive facial expressions. We hypothesized that approachability ratings would be lower for positive as well as negative emotional faces in the gSP group compared to the healthy comparison group. Each participant evaluated 24 emotional faces presented on a computer screen. Participants first labeled the faces as either happy, disgust, or angry in emotional expression, and then they rated each face's approachability. Analysis of variance and post hoc analyses were used to identify group, emotion, and group by emotion rating differences. RESULTS: Happy face approachability ratings were higher than disgust and anger in both groups. The central finding was that individuals with gSP rated happy faces as less approachable than the healthy participants and that degree of social anxiety was associated with lower approachability ratings within the gSP sample. Explicit approachability judgments of negative faces did not differ as predicted. CONCLUSIONS: Consistent with earlier indirect evidence of interpretation biases of positive social emotional information, this study reveals that individuals with gSP demonstrate explicit, subjective social interpretation biases of overtly positive social feedback. The therapeutic relevance of these results is discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".