Do accurate personality impressions benefit early relationship development? The bidirectional associations between accuracy and liking.
Bibliographic record
Abstract
Does forming accurate impressions benefit emerging relationships? In 2 longitudinal studies (Study 1: 235 participants, 534 dyads, 2 time points; Study 2: 122 participants, 3,023 dyads, 7 time points), we examined whether more accurate personality impressions among new acquaintances fostered greater liking over time for both the perceiver and target (accuracy fosters liking hypothesis). Further, we examined whether greater perceiver and target liking also fostered accuracy over time (liking fosters accuracy hypothesis). Overall, we found partial support for the accuracy fosters liking hypothesis: Greater accuracy (distinctive accuracy about a target's unique self-reported personality traits) fostered greater perceiver but not target liking over time. However, we did not find support for the liking fosters accuracy hypothesis, as neither perceiver nor target liking fostered greater accuracy over time. Going further, for the accuracy hypothesis, there was preliminary evidence that the strength of the associations depended on partner liking, such that accuracy was more beneficial for perceiver liking when target liking was high, and vice versa. These associations emerged above and beyond the strong, expected associations between liking and forming normative, positive personality impressions. In sum, these studies demonstrate that accurate personality impressions may indeed be adaptive, benefitting early relationship development, at least from the perspective of the perceiver. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".