Do We Know Who Values Us? Dyadic Meta-Accuracy in the Perception of Professional Relationships

Abstract:     
People often need to know what others think of them - whom do we approach to collaborate, to invite out, or to seek assistance? Research on meta-perceptions shows strong evidence for generalized meta-accuracy - knowing whether the rest of the world tends to see us, e.g., as extroverted or intelligent - but less for dyadic meta-accuracy - knowing how different people view us differently. In meta-judgments of individual traits and abilities, people generally assume they make the same impression on all interaction partners, rather than differentiating their unique impressions on each person. However, for more relational constructs such as friendship, liking, humor, considerateness, and interestingness, perceivers can typically differentiate others' unique evaluations. Previous theories argued that dyadic meta-accuracy resulted by 'accident' merely from the appropriate use of heuristics. In particular, people believe their evaluations will be reciprocated by others and, when this assumption is valid, they can introspect about their opinions of others to infer others' likely opinions of them. Our study is the first to show, provocatively, that presuming reciprocity does not entirely account for dyadic accuracy. This leaves open another theoretical mechanism for achieving accuracy - namely, that individuals are also able to read cues in their social landscape in order to judge the unique impressions that they make on others. We believe that prior work may have sounded the 'death knell' too early on dyadic meta-accuracy, and therefore on attempts to explain the relationship-relevant cues that people use to attain it. This finding has important implications by suggesting people have more insight into their own relationships than previously believed. Because dyadic meta-accuracy can help us know which relationships to pursue and which to avoid, these results have important implications for the formation of social networks.

Keywords: accuracy, networks, relationships

Working Paper Series