The Influence of Recent Experience on Perceptions of Attractiveness
Bibliographic record
Abstract
Adults rate average faces as more attractive than most of the faces used in the creation of the average. One explanation for this is that average faces appear as both more familiar and more attractive because they resemble internal face prototypes formed from experience. Here we evaluated that explanation by examining the influence of recent experience on participants' subsequent judgments of attractiveness. Participants first performed a memory task lasting 8 min in which all of the female faces to be remembered had their features placed in a low, average, or high position, depending on experimental condition. In what was described as a separate experiment, participants then moved the features of a female face with averaged features to their most attractive vertical location. The most attractive location was affected by the faces seen during the memory task, with participants who saw faces with features in the high position placing features in higher locations than participants who saw faces with features in either the low or average positions. The results demonstrate that perceptions of attractiveness are influenced by recent experience, and suggest that internal face prototypes are constantly being updated by experience.
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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 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".