The intuitive greater good: Testing the corrective dual process model of moral cognition.
Bibliographic record
Abstract
Building on the old adage that the deliberate mind corrects the emotional heart, the influential dual process model of moral cognition has posited that utilitarian responding to moral dilemmas (i.e., choosing the greater good) requires deliberate correction of an intuitive deontological response. In the present article, we present 4 studies that force us to revise this longstanding "corrective" dual process assumption. We used a two-response paradigm in which participants had to give their first, initial response to moral dilemmas under time-pressure and cognitive load. Next, participants could take all the time they wanted to reflect on the problem and give a final response. This allowed us to identify the intuitively generated response that preceded the final response given after deliberation. Results consistently show that in the vast majority of cases (+ 70%) in which people opt for a utilitarian response after deliberation, the utilitarian response is already given in the initial phase. Hence, utilitarian responders do not need to deliberate to correct an initial deontological response. Their intuitive response is already utilitarian in nature. We show how this leads to a revised model in which moral judgments depend on the absolute and relative strength differences between competing deontological and utilitarian intuitions. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.014 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".