Own-age and own-sex biases in recognition of aged faces
Bibliographic record
Abstract
It is surprising how easily we are able to recognize people whom we have not seen in many years, somehow compensating for the aging-related facial changes that occurred. We measured the limits of the ability to recognize faces across the lifespan by young versus old men and women. Images of five males and five females at young and middle ages were morphed in 10% increments to create aged face images across the lifespan. Fifty-eight participants (28 females) judged whether pairs of photographs were of the same or different identity. Women outperformed men for female faces, exhibiting a sex difference and own-sex bias. Additionally, older participants showed an own-age bias and outperformed their younger counterparts with older stimuli. It appears that the recognition of faces is affected by the own-age and own-sex biases, potentially allowing us to remember some people better than others, thus mediating our interaction with the world.
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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) | 0.003 | 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 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".