Not So Fast: The (Not-Quite-Complete) Dissociation Between Accuracy and Confidence in Thin-Slice Impressions
Bibliographic record
Abstract
After decades of research highlighting the fallibility of first impressions, recent years have featured reports of valid impressions based on surprisingly limited information, such as photos and short videos.Yet beneath mean levels of accuracy lies tremendous variance-some snap judgments are well-founded, others wrongheaded. An essential question for perceivers, therefore, is whether and when to trust their initial intuitions about others. In three studies of first impressions based on photos and videos, the authors examined accuracy for Big Five trait judgments as well as corresponding reports of confidence. Overall, perceivers showed a limited ability to intuit which of their impressions were more accurate than others, although a curvilinear effect emerged: In the relatively few cases where perceivers reported an absolute lack of confidence, their accuracy was indeed comparatively low. Across the studies, judgment confidence was shaped by sources at the judgment level and the judge level that were unrelated to accuracy.
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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 | 0.006 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".