Evidence for a young adult face bias in accuracy and consensus of age estimates
Bibliographic record
Abstract
Adults' face processing may be specialized for the dimensions of young adult faces. For example, young and older adults exhibit increased accuracy in normality judgments and greater agreement in attractiveness ratings for young versus older adult faces. The present study was designed to examine whether there is a similar young adult face bias in facial age estimates. In Experiment 1, we created a face age continuum by morphing an averaged young adult face with an averaged older adult face in 5% increments, for a total of 21 faces ranging from 0 to 100% old. Young and older adults estimated facial age for three stimulus age categories [young (morphs 0-30%), middle-aged (morphs 35-65%), and older adult (morphs 70-100%)]. Both age groups showed the least differentiation in age estimates for young adult faces, despite showing greater consensus across participants in estimates for young faces. In Experiment 2, young and older adults made age estimates for individual young and older adult identities. Both age groups were more accurate and showed greater consensus in age estimates for young faces. Collectively, these results provide evidence for a bias in processing young adult faces beyond that which is often observed in recognition and normality/attractiveness judgment tasks.
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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 | 0.006 |
| 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) | 0.000 | 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".