Preserving Integrity in the Face of Performance Threat
Bibliographic record
Abstract
Self-affirmation produces large effects: Even a simple reminder of one's core values reduces defensiveness against threatening information. But how, exactly, does self-affirmation work? We explored this question by examining the impact of self-affirmation on neurophysiological responses to threatening events. We hypothesized that because self-affirmation increases openness to threat and enhances approachability of unfavorable feedback, it should augment attention and emotional receptivity to performance errors. We further hypothesized that this augmentation could be assessed directly, at the level of the brain. We measured self-affirmed and nonaffirmed participants' electrophysiological responses to making errors on a task. As we anticipated, self-affirmation elicited greater error responsiveness than did nonaffirmation, as indexed by the error-related negativity, a neural signal of error monitoring. Self-affirmed participants also performed better on the task than did nonaffirmed participants. We offer novel brain evidence that self-affirmation increases openness to threat and discuss the role of error detection in the link between self-affirmation and performance.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".