Do We Know When Our Impressions of Others Are Valid? Evidence for Realistic Accuracy Awareness in First Impressions of Personality
Bibliographic record
Abstract
Do people have insight into the validity of their first impressions or accuracy awareness? Across two large interactive round-robins, those who reported having formed a more accurate impression of a specific target had (a) a more distinctive realistically accurate impression, accurately perceiving the target’s unique personality characteristics as described by the target’s self-, parent-, and peer-reports, and (b) a more normatively accurate impression, perceiving the target to be similar to what people generally tend to be like. Specifically, if a perceiver reported forming a more valid impression of a specific target, he or she had in fact formed a more realistically accurate impression of that target for all but the highest impression validity levels. In contrast, people who generally reported more valid impressions were not actually more accurate in general. In sum, people are aware of when and for whom their first impressions are more realistically accurate.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".