Does evaluating oneself cause evaluations of others while observing a social interaction? An experimental investigation of the cognitive and emotional consequences following a false-feedback task
Bibliographic record
Abstract
Introduction Despite decades of research on how people with social anxiety evaluate themselves and others, it remains unclear whether people who evaluate themselves negatively also evaluate others negatively. Findings from other-evaluation research are equivocal, perhaps attributable to methodology differences and inconsistent operationalization. Social-cognitive and cognitive-behavioural models suggest that negative self-evaluations may cause participants to subsequently evaluate a visibly anxious person negatively. We tested this hypothesis experimentally, using a video-recorded social interaction and novel false-feedback manipulation. Methods: 169 unselected participants completed baseline questionnaires and a 10-min impromptu conversation task with a confederate. We randomly assigned participants to receive positive, ambiguous, or negative false-feedback about their performance. Next, they evaluated their own performance and watched a recorded conversation between an anxious and confident speaker. Finally, they evaluated the anxious person’s performance. Results: Our manipulation was effective; participants in the negative-feedback condition rated themselves more negatively. However, no differences emerged between conditions on most cognitive and emotional outcomes. Discussion: Evaluating oneself negatively, on its own, may not lead people to evaluate a visibly anxious person in a recorded social interaction negatively in a single-session experiment within an unselected sample. Future studies should examine this relationship with a clinical sample across time and contexts.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".