“Taste typicality” is a foundational and multi-modal dimension of ordinary aesthetic experience
Bibliographic record
Abstract
Aesthetic experience seems both regular and idiosyncratic. On one hand, there are powerful regularities in what we tend to find attractive versus unattractive (e.g., beaches versus mud puddles). 1–4 On the other hand, our tastes also vary dramatically from person to person: 5–8 what one of us finds beautiful, another might find distasteful. What is the nature of such differences? They may in part be arbitrary—e.g., reflecting specific past judgments (such as liking red towels over blue ones because they were once cheaper). However, they may also in part be systematic—reflecting deeper differences in perception and/or cognition. We assessed the systematicity of aesthetic taste by exploring its typicality for the first time across seeing and hearing. Observers rated the aesthetic appeal of ordinary scenes and objects (e.g., beaches, buildings, and books) and environmental sounds (e.g., doorbells, dripping, and dialtones). We then measured "taste typicality" (separately for each modality) in terms of the similarity between each individual's aesthetic preferences and the population's average. The data revealed two primary patterns. First, taste typicality was not arbitrary but rather was correlated to a moderate degree across seeing and hearing: people who have typical taste for images also tend to have typical taste for sounds. Second, taste typicality captured most of the explainable variance in people's impressions, showing that it is the primary dimension along which aesthetic tastes systematically vary.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".