Bibliographic record
Abstract
Can people accurately predict how they will act in a moral dilemma? Our research suggests that in some situations, they cannot, and that emotions play a pivotal role in this dissociation between behavior and forecasting. In the current experiment, individuals in a moral action condition cheated significantly less on a math task than participants in a forecasting condition predicted they themselves would cheat. Furthermore, we found that participants in the action condition displayed significantly more physiological arousal, as measured by preejection period, skin conductance response (SCR), and respiratory sinus arrhythmia (RSA), and that the underestimation effect was mediated by SCR and RSA together. This research suggests that the affective arousal present during real-life moral dilemmas may not be fully engaged during moral forecasting, and that this may account for the moral forecasting errors that individuals make. This research has the potential to inform past work in the field of moral psychology, which has largely ignored actual 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 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".