Revisiting the Moral Forecasting Error – A Preregistered Replication and Extension of “Are We More Moral Than We Think?”
Bibliographic record
Abstract
Predictions about the future are often inaccurate, but the direction of prediction errors may vary. Contrary to research on the intention-behavior gap, where people fail to live up to their future ambitions, a study on “moral forecasting” found that people behaved more honestly than they predicted. Since this interesting prediction error has only been identified in a few studies and the direction of the effect may seem surprising, psychological science could benefit from a high-powered replication. In Experiment 1, we will conduct a close replication using the original cheating task and a general population sample from the same country as the original study (Canada). By extension, we will also include the “mind-game paradigm” as an established deception-free cheating task from behavioral economics, to assess task generalizability. If the primary hypothesis is supported, then we propose a second extension in Experiment 2, by examining whether a cognitive debiasing intervention can reduce the moral forecasting error in a general population sample from the US. As a final extension we will also examine the social dimension of lay beliefs, by assessing whether moral forecasts for other people exhibit the same prediction error as moral forecasts for oneself. In the current experiments, the planned sample size will provide 90% power to detect ¼ of the observed effect size from the original study.
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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 | 0.030 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".