Scene Contour Junctions Influence Visual Aesthetics
Bibliographic record
Abstract
Artists have used contour junctions to evoke aesthetic appreciation for works of art across different cultures (Cavanagh, 2005; Sayim & Cavanagh, 2011). Contour junctions are crucial for object recognition (Biederman, 1987), and scene categorization (Walther & Shen, 2014). Given their prominence in visual perception, junctions may also explain a shared human taste for visual aesthetics. The impact of contour junctions on aesthetic judgements has not yet been empirically studied. We examined whether the presence of different types of contour junctions predicts aesthetic preferences for real-world scenes. Line drawings were created by trained artists from photographs of natural scenes. Contour junctions were detected at the intersections of lines and characterized based on the angles between them into different types: X, T, Y, and Arrow junctions, according to methods described by Walther & Shen, 2014. Participants on Prolific were presented with images and asked the following question: “How much do you enjoy viewing this image?”. Aesthetic responses were collected on a 5-point Likert scale (1= not at all to 5= enjoy very much). A linear mixed-effects model was applied to control for individual differences. We found evidence that contour junctions predict human aesthetic preferences for scenes (R-squared = 0.39). Across different individuals, an increase in the number of T, Y and Arrow junctions in a scene was associated with higher aesthetic ratings for that scene, whereas the presence of X junctions was negatively associated with aesthetic ratings. Importantly, the effect of T junctions persisted for scene categories belonging to natural and urban environments. Our results show that informative scene regions are associated with aesthetic appreciation for real-world scenes. Contour junctions facilitate shape recognition which leads to perceptual fluency or ease of information processing. We may enjoy recognizing familiar visual anchors (e.g., right angles) when viewing scenes because they help us identify affordances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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; a candidate call from one teacher head, 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".