Neural dissociation between computational and perceived measures of curvature
Bibliographic record
Abstract
There is substantial evidence to suggest that preference for visual curvature is a reliable phenomenon. Yet, little is known about the ways in which the encoding of curvature in the brain contributes to hedonic evaluation while participants are actively engaged in making choices about objects varying in curvature. To address this question, we reanalyzed fMRI data collected while participants made aesthetic judgments (beautiful vs. not beautiful) and approach-avoidance decisions (enter vs. exit) in relation to measures of (a) computational curvature, (b) perceived curvature, (c) perceived angularity, and (d) aesthetic pleasure in the domain of architecture. Our results show that a region in early visual cortex (BA 17) encompassing largely areas V2-V3 is sensitive to variation in computational curvature across both beauty judgments and approach-avoidance decisions, whereas a region encompassing the fusiform gyrus (BA 37) exhibits sensitivity to perceived curvature only when participants made beauty judgments. These results contribute to our understanding of the neurobiological basis of curvature preference by demonstrating that the sensitivity of the visual cortex to computational curvature is context invariant, whereas the sensitivity of the fusiform gyrus to perceived curvature varies by context.
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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 | 0.000 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".