Does aging impair first impression accuracy? Differentiating emotion recognition from complex social inferences.
Bibliographic record
Abstract
Young adults can be surprisingly accurate at making inferences about people from their faces. Although these first impressions have important consequences for both the perceiver and the target, it remains an open question whether first impression accuracy is preserved with age. Specifically, could age differences in impressions toward others stem from age-related deficits in accurately detecting complex social cues? Research on aging and impression formation suggests that young and older adults show relative consensus in their first impressions, but it is unknown whether they differ in accuracy. It has been widely shown that aging disrupts emotion recognition accuracy, and that these impairments may predict deficits in other social judgments, such as detecting deceit. However, it is unclear whether general impression formation accuracy (e.g., emotion recognition accuracy, detecting complex social cues) relies on similar or distinct mechanisms. It is important to examine this question to evaluate how, if at all, aging might affect overall accuracy. Here, we examined whether aging impaired first impression accuracy in predicting real-world outcomes and categorizing social group membership. Specifically, we studied whether emotion recognition accuracy and age-related cognitive decline (which has been implicated in exacerbating deficits in emotion recognition) predict first impression accuracy. Our results revealed that emotion recognition accuracy did not predict first impression accuracy, nor did age-related cognitive decline impair it. These findings suggest that domains of social perception outside of emotion recognition may rely on mechanisms that are relatively unimpaired by aging.
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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 | 0.001 | 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) | 0.001 | 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".