Through the looking glass clearly: Accuracy and assumed similarity in well-adjusted individuals' first impressions.
Bibliographic record
Abstract
Do well-adjusted individuals have particularly accurate insight into what others are like or are they biased, primarily seeing their own characteristics in others? In the current studies, the authors examined how psychologically adjusted individuals tend to see new acquaintances, directly comparing their levels of distinctive accuracy (accurately perceiving others' unique characteristics), normative accuracy (perceiving others as similar to the average person), and assumed similarity (perceiving others as similar to the self). Across two interactive, round-robin studies, well-adjusted individuals, compared with less adjusted individuals, did not perceive new acquaintances' unique characteristics more accurately but did perceive new acquaintances, on average, as similar to the average person, reflecting an accurate understanding of what people generally tend to be like. Furthermore, well-adjusted individuals had a biased tendency to perceive their own unique characteristics in others. Of note, both pre-existing perceiver adjustment and target-specific liking independently predicted greater accuracy and assumed similarity in first impressions. In sum, well-adjusted individuals see through the looking glass clearly: although they erroneously see others as possessing their own unique characteristics, they accurately understand what others generally tend to be like.
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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 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".