Bibliographic record
Abstract
Ensemble perception, the extraction of a statistical summary of multiple instances of a feature, enables efficient processing of information. Here we investigated whether ensemble representations can be formed for facial attractiveness, a socially important complex feature. After verifying that our face stimuli produced by geometric morphing represented a valid continuum of attractiveness (Experiment 1), we asked participants to compare the average attractiveness of four faces with a single probe face. Whether the four faces were homogeneous or heterogeneous resulted in highly similar performance levels, suggesting the visual system could extract an ensemble representation of the attractiveness of a heterogeneous group of faces. Statistical simulations with human-level bias and noise indicated participants did not rely on subsampling one random face or the most/least attractive face from the array (Experiment 2). Ensemble perception of facial attractiveness was not affected by variance in the stimulus array (Experiment 3), did not depend on memory of individual faces in the array (Experiment 4), and could be extended to larger arrays with faces asymmetrically distributed around the set mean (Experiment 5). Our findings give further evidence to the prevalence of perception of statistical regularities in vision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.005 | 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 teacher head, 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".