Knowing me, knowing you: Interpersonal similarity improves predictive accuracy and reduces attributions of harmful...

To benefit from social interactions, people need to predict how their social partners will behave. Such predictions arise through integrating prior expectations with evidence from observations, but where the priors come from and whether they influence the integration into beliefs about a social partner is not clear. Furthermore, this process can be affected by factors such as paranoia, in which the tendency to form biased impressions of others is common. Using a modified social value orientation (SVO) task in a large online sample (n = 697), we showed that participants used a Bayesian inference process to learn about partners, with priors that were based on their own preferences. Paranoia was associated with preferences for earning more than a partner and less flexible beliefs regarding a partner's social preferences. Alignment between the preferences of participants and their partners was associated with better predictions and with reduced attributions of harmful intent to partners. Together, our data and model expand upon theories of interpersonal relationships by demonstrating how dyadic similarity mechanistically influences social interaction by generating more accurate predictions and less threatening impressions.