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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".