Preprint of "Differential associations of knowing and liking with accuracy and positivity bias in person perception"
Bibliographic record
Abstract
A great range of person perception phenomena may be conceptualized in terms of how much perceivers know about the targets, how much they like the targets and how these factors relate to the extent to which target descriptions reflect actual target characteristics and/or evaluative bias. We present a comprehensive empirical analysis of this interplay in two studies, the second (targets: N = 189, informants: N = 1352) being a pre-registered replication of the first (targets: N = 73, informants: N = 549). Using multilevel profile analyses, we investigated how liking and knowing are differentially associated with judgments’ normative accuracy (i.e., reflecting actual characteristics of the average target), distinctive accuracy (i.e., reflecting actual characteristics of specific targets), and positivity bias. Statistical effects were largely consistent across two independent validation measures (self-ratings vs. peer-ratings of personality), and across the two studies. Despite being positively correlated with one another, liking and knowing had opposing effects on person judgments: Knowing targets better was associated with greater distinctive and normative accuracy, and with lower positivity bias. In contrast, liking targets more was associated with lower distinctive and normative accuracy, but with greater positivity bias. The findings suggest that person judgments tend to reflect actual target characteristics as well as evaluative bias, and that the relative extents to which they do are predictable from what the perceivers say about their relationships with the targets (i.e., knowing and liking). Directions for future research are discussed.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.082 | 0.009 |
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".