Positivity in peer perceptions over time: Personality explains variation at zero-acquaintance, popularity explains differential change.
Bibliographic record
Abstract
People have characteristic ways of perceiving others’ personalities. When judging others on several traits, some perceivers tend to form globally positive and others tend to form globally negative impressions. These differences, often termed perceiver effects, have mostly been conceptualized as a static construct that taps perceivers’ personal stereotypes about the average other. Here, we assessed perceiver effects repeatedly in small groups of strangers who got to know each other over the course of 2 to 3 weeks and examined the degree to which positivity differences were stable vs. developed systematically over time. Using second order latent growth curve modelling, we tested whether initial positivity (i.e., random intercepts) could be explained by several personality variables and whether change (i.e., random slopes) could be explained by these personality variables and by perceivers’ social experiences within the group. Across three studies (ns = 439, 257, and 311), personality variables characterized by specific beliefs about others, such as agreeableness and narcissistic rivalry, were found to explain initial positivity but personality was not reliably linked to changes in positivity over time. Instead, feeling liked and, to a lesser extent, being liked by one’s peers, partially explained changes in positivity. The results suggest that perceiver effects are best conceptualized as reflecting personal generalized stereotypes at an initial encounter but group-specific stereotypes that are fueled by social experiences as groups get acquainted. More generally, these findings suggest that perceiver effects might be a key variable to understanding reciprocal dynamics of small groups and interpersonal functioning.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".