Differences in Regional Grey Matter Volume Predict the Extent to which Openness influences Judgments of Beauty and Pleasantness of Interior Architectural Spaces
Bibliographic record
Abstract
Abstract Hedonic evaluation of sensory objects varies from person to person. While this variability has been linked to differences in experience and personality traits, little is known about why stimuli lead to different evaluations in different people. We used linear mixed effect models to determine the extent to which the openness, contour, and ceiling height of interior architectural spaces influenced the beauty and pleasantness ratings of 18 participants. Then, by analyzing structural brain images acquired for the same group of participants we asked if any regional grey matter volume (rGMV) co‐varied with these differences in the extent to which openness, contour and ceiling height influence beauty and pleasantness ratings. Voxel‐based morphometry analysis revealed that the influence of openness on pleasantness ratings correlated with rGMV in the anterior prefrontal cortex (BA 10), and the influence of openness on beauty ratings correlated with rGMV in the temporal pole (BA 38) and posterior cingulate cortex (BA 31). There were no significant correlations involving contour or ceiling height. Our results suggest that regional variance in grey matter volume may play a role in the computation of hedonic valuation, and account for differences in the way people weigh certain attributes of interior architectural spaces.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".