Better, Stronger, Faster: Self-Serving Judgment, Affect Regulation, and the Optimal Vigilance Hypothesis
Bibliographic record
Abstract
Self-serving judgments, in which the self is viewed more favorably than other people, are ubiquitous. Their dynamic variation within individuals may be explained in terms of the regulation of affect. Self-serving judgments produce positive emotions, and threat increases self-serving judgments (a compensatory pattern that restores affect to a set point or baseline). Perceived mutability is a key moderator of these judgments; low mutability (i.e., the circumstance is closed to modification) triggers a cognitive response aimed at affect regulation, whereas high mutability (i.e., the circumstance is open to further modification) activates direct behavioral remediation. Threats often require immediate response, whereas positive events do not. Because of this brief temporal window, an active mechanism is needed to restore negative (but not positive) affective shifts back to a set point. Without this active reset, an earlier threat would make the individual less vigilant toward a new threat. Thus, when people are sad, they aim to return their mood to baseline, often via self-serving judgments. We argue that asymmetric homeostasis enables optimal vigilance, which establishes a coherent theoretical account of the role of self-serving judgments in affect regulation.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".