Less is more: Aesthetic liking is inversely related to metabolic expense by the visual system
Bibliographic record
Abstract
What makes us like a particular scene or object and dislike another? A variety of visual properties, the observers’ experience, familiarity, processing fluency, and self-relevance have been suggested to underlie aesthetic liking. Here we investigate whether the brain’s goal to reduce energy costs (Olshausen and Field 1997; Friston, 2010) explains the construction of aesthetic appreciation. We propose a simple, straightforward approach to explaining neural responses to visual stimuli with different levels of aesthetic preference: the total metabolic cost of firing of neurons within relevant regions of interest. We test this hypothesis in an in-silico model of the visual system (VGG19) as well as human observers and find strong evidence in both. Specifically, we compare the metabolic cost incurred by 4914 images of objects and scenes from the BOLD5000 dataset for a VGG19 network pretrained for object and scene categorization with randomly initialized versions of VGG19. We find a strong inverse relationship between aesthetic preferences for the images and their metabolic cost, but only in the network trained for categorization. We then test the same hypothesis in the human visual system by comparing aesthetic liking of visual stimuli to the metabolic activity measured with functional magnetic resonance imaging. Crucially, we find strong evidence for the hypothesized inverse relationship between metabolic expense and aesthetic liking in both early visual brain regions (V1 and V4) and high-level regions (FFA, OPA, PPA). These findings represent the first direct evidence for a physiological basis of visual aesthetics at the level of energy consumption by the visual system. Aesthetic pleasure may function as an adaptive homeostatic signal to help conserve energy resources for survival. Our metabolic account for aesthetic liking unifies empirical evidence for visual discomfort with theories of processing fluency, image complexity, expertise, and prototypicality for aesthetic liking in a simple, physiologically plausible framework.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".