Guilt enhances the sense of control and drives risky judgments.
Bibliographic record
Abstract
In the present studies, we investigate the hypothesis that guilt influences risk taking by enhancing one's sense of control. Across multiple inductions of guilt, we demonstrate that experimentally induced guilt enhances optimism about risks for the self (Study 1), preferences for gambles versus guaranteed payoffs (Studies 2, 4, and 6), and the likelihood that one will engage in risk-taking behaviors (Study 5). In addition, we demonstrate that guilt enhances the sense of control over uncontrollable events, an illusory control (Studies 3, 4, and 5), and found that a model with illusory control as a mediator is consistent with the data (Studies 5 and 6). We also found that a model with feelings of guilt as a mediator but not generalized negative affect fits the data (Study 4). Finally, we examined the relative explanatory power of different appraisals and found that appraisals of illusory control best explain the influence of guilt on risk taking (Study 6). These results provide the first empirical demonstration of the influence of guilt on sense of control and risk taking, extend previous theorizing on guilt, and more generally contribute to the understanding of how specific emotions influence cognition and behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".