Using conflict negativity to index psychological tension between impartiality and status-upholding principles
Bibliographic record
Abstract
People often endorse the moral principle that all human lives are equally valuable. At the same time, people often privilege high-status individuals over low-status individuals. These two inclinations come into conflict in a scenario involving the potential killing of a high-status person to save the lives of multiple low-status people. In the present study, participants viewed a series of sacrificial dilemmas in which the social status of the victims and beneficiaries was varied. We measured participants' choice (sacrifice vs. don't sacrifice), response time, and electroencephalographic activity, with an emphasis on conflict negativity (CN). Overall, we found no effects of victim/beneficiaries status on choice and response time. However, participants displayed a more pronounced CN effect when contemplating a high-status victim/low-status beneficiaries tradeoff than a low-status-victim/high-status beneficiaries tradeoff. Further analyses revealed that this effect was primarily driven by participants who endorsed deontological principles (e.g., "Some rules must never be broken, no matter the consequences"). In contrast, those who endorsed utilitarian principles displayed equivalent levels of conflict negativity, regardless of the social status of victims and beneficiaries. These findings shed light on the role of conflict in the phenomenology of moral decision making.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".