The time course of moral perception: an ERP investigation of the moral pop-out effect
Bibliographic record
Abstract
Humans are highly attuned to perceptual cues about their values. A growing body of evidence suggests that people selectively attend to moral stimuli. However, it is unknown whether morality is prioritized early in perception or much later in cognitive processing. We use a combination of behavioral methods and electroencephalography to investigate how early in perception moral words are prioritized relative to non-moral words. The behavioral data replicate previous research indicating that people are more likely to correctly identify moral than non-moral words in a modified lexical decision task. The electroencephalography data reveal that words are distinguished from non-words as early as 200 ms after onset over frontal brain areas and moral words are distinguished from non-moral words 100 ms later over left-posterior cortex. Further analyses reveal that differences in brain activity to moral vs non-moral words cannot be explained by differences in arousal associated with the words. These results suggest that moral content might be prioritized in conscious awareness after an initial perceptual encoding but before subsequent memory processing or action preparation. This work offers a more precise theoretical framework for understanding how morality impacts vision and behavior.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".