Shifting evaluative construal: Common and distinct neural components of moral, pragmatic, and hedonic evaluations.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it