Altered mechanisms of adaptation in social anxiety: differences in adapting to positive versus negative emotional faces
Bibliographic record
Abstract
Social anxiety is characterised by fear of negative evaluation and negative perceptual biases; however, the cognitive mechanisms underlying these negative biases are not well understood. We investigated a possible mechanism which could maintain negative biases: altered adaptation to emotional faces. Heightened sensitivity to negative emotions could result from weakened adaptation to negative emotions, strengthened adaptation to positive emotions, or both mechanisms. We measured adaptation from repeated exposure to either positive or negative emotional faces, in individuals high versus low in social anxiety. We quantified adaptation strength by calculating the point of subjective equality (PSE) before and after adaptation for each participant. We hypothesised: (1) weaker adaptation to angry vs happy faces in individuals high in social anxiety, (2) no difference in adaptation to angry vs happy faces in individuals low in social anxiety, and (3) no difference in adaptation to sad vs happy faces in individuals high in social anxiety. Our results revealed a weaker adaptation to angry compared to happy faces in individuals high in social anxiety (Experiment 1), with no such difference in individuals low in social anxiety (Experiment 1), and no difference in adaptation strength to sad vs happy faces in individuals high in social anxiety (Experiment 2).
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".