Shifting evaluative construal: Common and distinct neural components of moral, pragmatic, and hedonic evaluations.
Bibliographic record
Abstract
(whether it feels good; Van Bavel et al., 2012). The current research examined the neurocognitive computations underlying these types of evaluations to understand how people construct affective judgments. Specifically, we examined whether different types of evaluations stem from a common neural evaluation system that incorporates different information in response to changing evaluation goals (moral, pragmatic, or hedonic), or distinct evaluation systems with different neurofunctional architectures. We found support for a hybrid evaluation system in which people rely on a set of brain regions to construct all three forms of evaluation but recruit additional distinct regions for each type of evaluation. The three types of evaluations all relied on common neural activity in affective structures such as the amygdala, the insula, and the hippocampus. However, moral evaluations involved greater neural activation in the orbitofrontal and cingulate cortex compared to pragmatic evaluations, and temporoparietal regions compared to hedonic evaluations. These results suggest that people use a hybrid system that includes common evaluation components as well as distinct ones to generate moral judgments. (PsycInfo Database Record (c) 2025 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".