Hater_etal_The Social Meta-Accuracy Model_JPSP_preprint
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Le résumé fourni par la source
To what extent do individuals differ in understanding how others see them and who is particularly good at it? Answering these questions about the “good meta-perceiver” is relevant given the beneficial outcomes of meta-accuracy. However, there likely is more than one type of the good meta-perceiver: one who knows the specific impressions they make more than others do (dyadic meta-accuracy) and one who knows their reputation more than others do (generalized meta-accuracy). To identify and understand these good meta-perceivers, we introduce the Social Meta-Accuracy Model (SMAM) as a statistical and conceptual framework and apply the SMAM to four samples of first impression interactions. As part of our demonstration, we also investigated the routes to and the correlates of both types of good meta-perceivers. Results from SMAM show that, overall, people were able to detect the unique and general first impressions they made, but there was little evidence for individual differences in dyadic meta-accuracy in a first impression. In contrast, there were substantial individual differences in generalized meta-accuracy, and this ability was largely explained by being transparent (i.e., good meta-perceivers were seen as they saw themselves). We also observed some evidence that good generalized meta-perceivers in a first impression tend to be extraverted and popular. This work demonstrated that the SMAM is a useful tool for identifying and understanding both types of good meta-perceivers and paves the way for future work on individual differences in meta-accuracy in other contexts.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Hater_etal_The Social Meta-Accuracy Model_JPSP_preprint
- Date Crossref
- 19/06/2023
- Éditeur
- Center for Open Science
- Type
- posted-content
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Les institutions déclarées
Une affiliation ne permet pas de déduire la nationalité d’un auteur.