Beauty isn't special: Comparing the information capacity of beauty and other sensory judgments
Bibliographic record
Abstract
Information theory (bits) allows comparing beauty judgment to perceptual judgment on the same absolute scale. In one of the most influential articles in psychology, Miller (1956) found that classifying a stimulus into one of eight or more categories of the attribute transmits roughly 2.6 bits of information. That corresponds to 7 ± 2 categories. This number is both remarkably small and highly conserved across attributes and sensory modalities. This appears to be a signature of one-dimensional perceptual judgment. We wondered whether beauty can break this limit. Beauty judgments matter and play a key role in many of our real-life decisions, large and small. Mutual information is how much information about one variable can be obtained from observing another. We measured the mutual information of 50 participants' beauty ratings of everyday images. The mutual information saturated at 2.3 bits. We also replicated the results using different images. The 2.3 bits conveyed by beauty judgment are close to Miller's 2.6 bits of unidimensional perceptual judgment and far less than the 5 to 14 bits of a multidimensional perceptual judgment. By this measure, beauty judgment acts like a perceptual judgment, such as rating pitch, hue, or loudness.
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 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 | 0.004 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".