Facial Trustworthiness Perception Across the Adult Life Span
Bibliographic record
Abstract
OBJECTIVES: Trust is crucial for successful social interaction across the life span. Perceiver age, facial age, and facial emotion have been shown to influence trustworthiness perception, but the complex interplay between these perceiver and facial characteristics has not been examined. METHOD: Adopting an adult life-span developmental approach, 199 adults (aged 22-78 years) rated the trustworthiness of faces that systematically varied in age (young, middle-aged, and older) and emotion (neutral, happy, sad, fearful, angry, and disgusted) from the FACES Lifespan Database. RESULTS: The study yielded three key results. First, on an aggregated level, facial trustworthiness perception did not differ by perceiver age. Second, all perceivers rated young faces as the most trustworthy, and middle-aged and older (but not young) perceivers rated older faces as least trustworthy. Third, facial emotions signaling threat (fear, anger, and disgust) relative to neutral, happy, and sad expressions moderated age effects on facial trustworthiness perception. DISCUSSION: Findings from this study highlight the impact of perceiver and facial characteristics on facial trustworthiness perception in adulthood and aging and have potential to inform first impression formation, with effects on trait attributions and behavior. This publication also provides normative data on perceived facial trustworthiness for the FACES Lifespan Database.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".