Facing fate: Estimates of longevity from facial appearance and their underlying cues.
Bibliographic record
Abstract
Life span is associated with a number of physical and social factors, including health, personality, and socioeconomic status. Here, we examined whether life longevity could be predicted from facial appearance. We asked participants to view headshots from a 1923 university yearbook and to estimate how long they thought each person lived, finding that participants’ judgments predicted targets’ actual age of death. Additional participants rated the photos along a variety of characteristics previously found to predict longevity. Judgments of wealth were most closely related to perceived longevity, providing stronger predictive power than perceptions of health, attractiveness, or personality. These results demonstrate that longevity can be accurately judged from faces alone, and that assessments of traits related to actual longevity underlie the accurate perception of life span. SCIENTIFIC A BSTRACT Studies on social demographics have demonstrated that life span is influenced by a number of parameters related to one’s physical health and to the external environment. The current study examined whether longevity could be predicted from the face alone and, if so, the characteristics that relate to accurate judgments of longevity. Participants (N 212) viewed 100 portraits from a 1923 university yearbook and were asked to estimate how long each person lived. A structural equation model revealed that estimates of longevity significantly predicted targets’ actual age of death ( .30, t 2.27, p 0.02). To explore the mechanisms underlying these judgments, we examined perceptions of the faces along a set of variables related to actual life span. Perceptions of health and attractiveness ( .21, t 2.77, p .01), power ( .25, t 3.32, p .01) and wealth ( .52, t 6.48, p .01) predicted participants’ judgments of longevity, with perceived wealth showing the strongest relationship to estimated age of death. Overall, these results demonstrate that demographic factors that affect life span may also affect facial appearance, affording accurate judgments of longevity based on the face alone.
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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 | 0.004 |
| 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 | 0.001 |
| 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".