Effects of incidental emotions on moral dilemma judgments: An analysis using the CNI model.
Bibliographic record
Abstract
Effects of incidental emotions on moral dilemma judgments have garnered interest because they demonstrate the context-dependent nature of moral decision-making. Six experiments (N = 727) investigated the effects of incidental happiness, sadness, and anger on responses in moral dilemmas that pit the consequences of a given action for the greater good (i.e., utilitarianism) against the consistency of that action with moral norms (i.e., deontology). Using the CNI model of moral decision-making, we further tested whether the three kinds of emotions shape moral dilemma judgments by influencing (a) sensitivity to consequences, (b) sensitivity to moral norms, or (c) general preference for inaction versus action regardless of consequences and moral norms (or some combination of the three). Incidental happiness reduced sensitivity to moral norms without affecting sensitivity to consequences or general preference for inaction versus action. Incidental sadness and incidental anger did not show any significant effects on moral dilemma judgments. The findings suggest a central role of moral norms in the contribution of emotional responses to moral dilemma judgments, requiring refinements of dominant theoretical accounts and supporting the value of formal modeling approaches in providing more nuanced insights into the determinants of moral dilemma judgments. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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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 | 0.006 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".