Impacto del contorno en los juicios estéticos y en las decisiones de acercamiento-rechazo en arquitectura
Bibliographic record
Abstract
Impact of contour on aesthetic judgment and approach-avoidance decisions in architecture On average, we urban dwellers spend about 90% of our time indoors, and share the intuition that the physical features of the places we live and work in influence how we feel and act. However, there is surprisingly little research on how architecture impacts behavior, much less on how it influences brain function. To begin closing this gap, we conducted a functional magnetic resonance imaging study to examine how systematic variation in contour impacts aesthetic judgments and approach-avoidance decisions, outcome measures of interest to both architects and users of spaces alike. As predicted, participants were more likely to judge spaces as beautiful if they were curvilinear than rectilinear. Neuroanatomically, when contemplating beauty, curvilinear contour activated the anterior cingulate cortex exclusively, a region strongly responsive to the reward properties and emotional salience of objects. Complementing this finding, pleasantness—the valence dimension of the affect circumplex—accounted for nearly 60% of the variance in beauty ratings. Furthermore, activation in a distributed brain network known to underlie the aesthetic evaluation of different types of visual stimuli covaried with beauty ratings. In contrast, contour did not affect approach-avoidance decisions, although curvilinear spaces activated the visual cortex. The results suggest that the well-established effect of contour on aesthetic preference can be extended to architecture. Furthermore, the combination of our behavioral and neural evidence underscores the role of emotion in our preference for curvilinear objects in this domain. Key words: Neuroaesthetics, design, curvature, habitat theory.
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How this classification was reachedexpand
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 | 0.025 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.008 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".