Is moral beauty different from facial beauty? Evidence from an fMRI study
Bibliographic record
Abstract
Is moral beauty different from facial beauty? Two functional magnetic resonance imaging experiments were performed to answer this question. Experiment 1 investigated the network of moral aesthetic judgments and facial aesthetic judgments. Participants performed aesthetic judgments and gender judgments on both faces and scenes containing moral acts. The conjunction analysis of the contrasts 'facial aesthetic judgment > facial gender judgment' and 'scene moral aesthetic judgment > scene gender judgment' identified the common involvement of the orbitofrontal cortex (OFC), inferior temporal gyrus and medial superior frontal gyrus, suggesting that both types of aesthetic judgments are based on the orchestration of perceptual, emotional and cognitive components. Experiment 2 examined the network of facial beauty and moral beauty during implicit perception. Participants performed a non-aesthetic judgment task on both faces (beautiful vs common) and scenes (containing morally beautiful vs neutral information). We observed that facial beauty (beautiful faces > common faces) involved both the cortical reward region OFC and the subcortical reward region putamen, whereas moral beauty (moral beauty scenes > moral neutral scenes) only involved the OFC. Moreover, compared with facial beauty, moral beauty spanned a larger-scale cortical network, indicating more advanced and complex cerebral representations characterizing moral beauty.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".