Aesthetic value modulates gaze patterns on proto-object locations
Bibliographic record
Abstract
The experience of aesthetic pleasure is associated with reward-processing mechanisms in the human brain. Previous studies show that visual attention is biased towards objects associated with higher value in the environment. We investigate the relationship between natural gaze patterns and aesthetic valuations of scenes. To assess the contribution of salience-driven and reward-driven biases toward attentional allocation, we develop a model of gaze patterns that combines low-level, stimulus-driven salience with the aesthetic value of image regions. Our results show a positive relationship between gaze activity and aesthetic value. Moreover, we observe strong similarities in the spatial distribution of highly salient proto-object regions and scene regions with high aesthetic value, suggesting that salience plays an important role in evaluating the potential hedonic value of visual features. We further probe the relationship between intermediate-level representations of objects and aesthetic value by repurposing a deep neural network trained on object categorization to predict aesthetic value. We do this by replacing the top layer of the VGG-16 architecture with a linear layer with weights adjusted to predict aesthetic liking. Notably, the network weights in all layers except the new linear top layer are left unchanged. This network predicts subjective aesthetic valuations of scenes with high accuracy. This result highlights the intricate relationships between hedonic value, salience, and object perception. We hypothesize that expected hedonic value can function as the incentive to focus attention on salient image locations, likely to contain objects with ecological value. Rather than being a separate, affective visual process, aesthetic valuation is tightly intertwined with other aspects of visual cognition, such as attention and object recognition.
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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 | 0.001 |
| 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) | 0.003 | 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".