The dimensions underlying first impressions of older adult faces are similar, but not identical, for young and older adult perceivers
Bibliographic record
Abstract
First impressions based on facial cues have the potential to influence how older adults (OAs), a vulnerable population, are treated by others. The present study used a data-driven approach to examine dimensions underlying first impressions of OAs and whether those dimensions vary by perceiver age. In Experiment 1, young adult (YA) and OA participants provided unconstrained, written descriptions in response to OA faces. From these descriptors, 18 trait categories were identified that were similar, but not identical, across age groups. In Experiment 2, YA and OA participants rated OA faces on the trait words identified for their age group in Experiment 1. In separate principal components analyses, dimensions of sternness and confidence emerged for both groups. In Experiment 3, YA and OA participants rated these same faces on new words encompassing traits, emotion cues, and other appearance cues. Correlations between these ratings and factor scores showed that sternness is analogous to approachability for both age groups. Confidence is analogous to competence for both age groups and related to perceived age/health/attractiveness. Confidence was related to shyness for YAs but dominance for OAs. The current research has implications for a lifespan perspective on first impressions and informs functional accounts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".