Changing What You Like: Modifying Contour Properties Shifts Aesthetic Valuations of Scenes
Why this work is in the frame
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Bibliographic record
Abstract
To what extent do aesthetic experiences arise from the human ability to perceive and extract meaning from visual features? Ordinary scenes, such as a beach sunset, can elicit a sense of beauty in most observers. Although it appears that aesthetic responses can be shared among humans, little is known about the cognitive mechanisms that underlie this phenomenon. We developed a contour model of aesthetics that assigns values to visual properties in scenes, allowing us to predict aesthetic responses in adults from around the world. Through a series of experiments, we manipulate contours to increase or decrease aesthetic value while preserving scene semantic identity. Contour manipulations directly shift subjective aesthetic judgments. This provides the first experimental evidence for a causal relationship between contour properties and aesthetic valuation. Our findings support the notion that visual regularities underlie the human capacity to derive pleasure from visual information.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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