Social neuroscience research has demonstrated that those who are like-minded are also “like-brained.” Studies have shown that people who share similar viewpoints have greater neural synchrony with one another, and less synchrony with people who “see things differently.” Although these effects have been demonstrated at the group level, little work has been done to predict the viewpoints of specific individuals using neural synchrony measures. Furthermore, the studies that have made predictions using synchrony-based classification at the individual level used expensive and immobile neuroimaging equipment (e.g. fMRI) in highly controlled laboratory settings, which may not generalize to real-world contexts. Thus, this study uses a simple synchrony-based classification method, which we refer to as the neural reference groups approach, to predict individuals’ dispositional attitudes from data collected in a mobile “pop-up neuroscience” lab. Using functional near infrared spectroscopy (fNIRS) data, we predicted individuals’ partisan stances on a sociopolitical issue by comparing their neural timecourses to data from two partisan neural reference groups. We found that partisan stance could be identified at above-chance levels using data from dorsomedial prefrontal cortex (dmPFC). These results indicate that the neural reference groups approach can be used to investigate naturally-occurring, dispositional differences anywhere in the world.

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.