Fingerprint patterns of human brain activity reveal a dynamic mix of emotional responses during virtual intergroup...

The Stereotype Content Model (SCM) states that different social groups elicit different emotions according to their perceived level of competence and warmth. Because of this relationship between stereotypes and emotional states and because emotions are highly predictive of intergroup behaviors, emotional evaluation is crucial for research on intergroup relations. However, emotional assessment heavily relies on self-reports, which are often compromised by social desirability and challenges in reporting immediate emotional appraisals. In this study, we used machine learning to identify emotional brain patterns using functional magnetic resonance imaging. Subsequently, those patterns were used to monitor emotional reactions during virtual intergroup encounters. Specifically, we showed Finnish majority group members 360-videos depicting members of their ethnic ingroup and immigrant outgroups approaching and entering participants' personal space. All the groups showed different levels of perceived competence and warmth. In alignment with the SCM, our results showed that the groups perceived as low in competence and warmth evoked contempt and discomfort. Moreover, the ambivalent low-competent/high-warm group elicited both happiness and discomfort. Additionally, upon the protagonists' approach into personal space, emotional reactions were modulated differently for each group. Taken together, our findings suggest that our method could be used to explore the temporal dynamics of emotional responses during intergroup encounters.