The neurophysiological correlates of cognitive conflict associated with moral judgment of accidental harm transgressions: an event-related potentials study
Bibliographic record
Abstract
Judging someone who harmed another has been proposed to rely on distinct cognitive processes that evaluate the victim’s outcome and the intention to harm. When harm is accidental, cognitive tension may arise in judges because considerations about the victim’s harm may conflict with the examination of the perpetrator’s innocent intention. The goal of the present study was to characterize at the neural level the cognitive conflict that may be triggered by moral judgment of accidental harm. To this aim, we used electro-encephalography (EEG) while participants completed a lexical judgment task embedded in a moral judgment task. This manipulation was designed to trigger event-related potentials typically associated with conflict detection, namely the N400 component thought to originate from the anterior cingulate cortex. Participants (N = 31) listened to short scenarios featuring either an accidental or an intentional moral transgression. Each scenario was followed by a conclusion (for example, “Jake harmed Dan intentionally”) whose last word was either congruent or incongruent with the moral scenario. Participants had to judge the semantic congruency of the conclusion and sometimes had to judge how much the perpetrator should be punished. While participants were longer to judge incongruent targets, we did not characterize the expected N400. However, we observed a posterior P300 which was moderated by congruency and intention. The congruency effect on the P300 was larger for accidental relative to intentional harm scenarios, in line with the idea that processing accidental harm may require more cognitive resources to overcome a cognitive conflict between the intent-based and outcome-based processes.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".