Less is more: Aesthetic liking is inversely related to metabolic expense by the visual system
Bibliographic record
Abstract
Energy efficiency is a major driving force in the evolution of organisms, and previous research implies that humans may have evolved pleasure-based signals to guide optimal actions. But could this energy-saving heuristic also apply to aesthetic pleasure? We test this hypothesis using both an in silico model of the visual system (VGG19) and human observers, finding strong evidence in both. First, we measure the proxy for metabolic cost incurred by VGG19-either pretrained for object and scene categorization or randomly initialized-as it processes 4,914 images of objects and scenes, revealing an inverse relationship between aesthetic preferences and metabolic cost, and only in the pretrained model. Next, we compare aesthetic ratings of visual stimuli to metabolic activity in the human visual system, measured via the blood oxygen level-dependent signal during functional magnetic resonance imaging. We observe the same inverse relationship between blood oxygen level dependent signals and aesthetic preferences in both early visual regions (V1, V2, and V4) and higher-level regions (fusiform face area, occipital place area, and parahippocampal place area). These findings suggest that aesthetic preferences may at least partially arise from an affective heuristic favoring low-energy states, and they offer a unified framework linking empirical evidence on visual discomfort with theories of processing fluency, image complexity, and prototypicality, providing a straightforward model for understanding aesthetic judgments.
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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 | 0.001 |
| 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".