The temporal dynamics of third-party moral judgment of harm transgressions: answers from a 2-response paradigm
Bibliographic record
Abstract
Recent work supports the role of reasoning in third-party moral judgment of harm transgressions. The dynamics of the underlying cognitive processes supporting moral judgment is however poorly understood. In two preregistered experiments, we addressed this issue using a two-response paradigm. Participants were presented with moral scenarios twice: they had to provide their first judgment about an agent under both time pressure and interfering load, and were then asked to respond a second time at their own pace. In Experiment 1, participants were harsher toward a malevolent agent at the second response, assigning more moral wrongness and punishment to an agent who either attempted to harm or harmed intentionally. Experiment 2 replicated the effect of intention on response change in a paradigm contrasting accidental to intentional harm scenarios. Participants were not only harsher toward intentional transgressors at the second response, but they were also less harsh toward accidental transgressors at the second response. We discuss the possibility that decoding overall intent and assigning moral judgment based on the presence or absence of a malevolent intent may be a relatively costly process.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".