Simulation: A strategy for mindreading similar but not dissimilar others?

Egocentric Projection is a Rational Strategy for Accurate Emotion Prediction

People need to accurately understand and predict others' emotions in order to build and maintain meaningful social connections. However, when they encounter new social partners, people often do not have enough information about them to make accurate inferences. Rather, they often resort to an egocentric heuristic, and make predictions about a target by using their own self-knowledge as a proxy. Is this egocentric heuristic a form of cognitive bias, or is it a rational strategy for real-world social prediction? If egocentrism provides a rational and effective solution to the challenging task of social prediction in naturalistic contexts, we should expect that a) egocentric predictions tend to be more accurate, and b) people rely on self-knowledge to a greater extent when it's more likely to be a good proxy. Using an emotion prediction task and personality measures, we assessed similarity and predictive accuracy between first-year college students and their new acquaintance roommate. Results demonstrated that, when people need to predict an unfamiliar target's emotions, self-knowledge can often effectively approximate knowledge about others, and thus support accurate predictions. Moreover, participants that were typical of the sample, whose self-knowledge can better approximate information about the target, relied more on self-knowledge in their predictions, and thus achieved higher accuracy. These findings suggest that people rationally tune their use of egocentrism based on whether it is likely to pay off. Overall, these findings demonstrate a rational side to a cognitive phenomenon usually framed as a cognitive pitfall, namely egocentric projection, when its natural decision context is taken into consideration.

Default egocentrism: an MVPA approach to overlap in own and others' socio-political attitudes

Understanding the socio-political attitudes of other people is a crucial skill, yet the neural mechanisms supporting this capacity remain understudied. This study used multivariate pattern analysis to examine patterns of activity in the default mode network (DMN) while participants assessed their own attitudes and the attitudes of other people. Classification analyses indicated that common patterns in DMN regions encode both own and others' support across a variety of contemporary socio-political issues. Moreover, cross-classification analyses demonstrated that a common coding of attitudes is implemented at a neural level. This shared informational content was associated with a greater perceived overlap between own attitude positions and those of others (i.e. attitudinal projection), such that higher cross-classification accuracy corresponded with greater attitudinal projection. This study thus identifies a possible neural basis for egocentric biases in the social perception of individual and group attitudes and provides additional evidence for self/other overlap in mentalizing.