Computational Mechanisms of Self-enhancement During Social Comparison and their Relationship to Internalizing Symptoms
Bibliographic record
Abstract
Internalizing disorders like anxiety and depression commonly feature cognitive biases in self-evaluation, particularly in the context of social comparison. Despite the role of such biases in the severity and prognosis of internalizing conditions, limited work has identified computational mechanisms underlying self- evaluative biases in social contexts. In a sample of N = 292 participants, the present study applied hierarchical Bayesian computational modeling to a trait-evaluation task where individuals choose whether positive and negative traits better describe themselves or a close friend. We found that individuals generally engage in self-enhancement, more efficiently processing information that supports positive self-schema. However this effect flips as individuals report more symptoms, such that it becomes more difficult to integrate evidence in support of a pos- itive self-concept. These findings suggest that altered process- ing of both positive and negative self-referential information is a transdiagnostic mechanism driving aberrant self-evaluation in internalizing disorders.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".