Mina Cikara, Ph.D. - US grants

The last century has seen over 200 million people, 170 million of which were civilians, killed in acts of genocide, war, and other forms of group conflict. More mundane forms of intergroup aggression such as political conflicts pervade everyday life, and as a consequence may be at least as costly in total impact on the economy. Although individuals can be motivated to harm others because of personal as well as inter-group conflict, motivation to harm that originates from intergroup contexts may be especially dangerous. Such motivation can increase aggression because it allows harm to be justified as being morally necessary in the absence of any personal grievance. Moreover, the desire to aggress against one out-group member may generalize to their entire group. Thus, the motivation to aggress is especially important to understand as it unfolds in social groups. The investigator Mina Cikara (Harvard University) proposes that feeling pleasure in response to out-group pain is a natural response that makes it easier to learn a behavior which is otherwise repugnant to individuals: actively doing harm to others. If observing the pain of out-group members is consistently linked with feeling pleasure, people may learn over time to support and even act out in harmful ways toward out-group targets. This project takes a novel, interdisciplinary approach to understanding these questions by integrating social and cognitive psychology. This project also addresses a major gap in knowledge regarding the emergence and escalation of intergroup aggression, and can provide insights that enhance national security.

The model tested in this project posits that the capacity for intergroup aggression may have developed partly through basic learning principles. That is, basic reinforcement-learning processes that couple feeling pleasure and out-group pain may help people overcome a natural aversion to hurting others. A series of experiments using political, national, and ethnic identities test whether competitive out-groups (relative to in-group and neutral out-groups) are more likely to be targeted with aggression. The experimental contexts include an extended sequence of interactions and they test whether aggression escalates over the course of the interaction. Further experiments test whether aggression is reduced if learning is disrupted. This learning disruption takes place through either negative social feedback from in-group members or when each aggressive action requires evaluating the associated costs and benefits. This project builds on classic and contemporary theories of learning. As such, it makes several points of contact with other areas of scientific inquiry including behavioral neuroscience, economics, and biology, and with the knowledge gained from studying other animals. The results of this project have the potential to inform focused and inexpensive cognitive behavioral interventions to attenuate intergroup aggression. The findings may be of interest to political and educational institutions with the power to make and implement policy.

Social groups are woven tightly into the fabric of people’s lives. They shape how people perceive, punish, cooperate with, and learn from other people. This project seeks to understand how people discover the structure of social groups from patterns in the behavior of individuals. The project is centered on the concept of social structure learning. According this account, the brain uses statistical learning algorithms to sort individuals into latent groups on the basis of their behavioral patterns. These group representations are updated as more evidence is accumulated. The research extends the social structure learning model in several ways. One is to better understand the processes by which updating, subtyping, and subgrouping occur. Another is to establish how people balance the influence of explicit social categories against latent groupings. A third is to better understand how people resolve the challenge of cross-categorization. The project offers broad societal relevance by shedding light on the nature of social biases and stereotypes, ultimately pointing the way toward reducing discrimination.

This project advances basic understanding of social structure learning by using a combination of computational modeling and laboratory experiments. Computational models offer a formalization of hypotheses and make quantitative predictions about behavior. The project develops a computational model that makes specific predictions and captures several important features of social structure learning: (i) how people infer hierarchically-structured groups; (ii) how people use explicit social categories to guide their inferences about group structure; and (iii) how people infer multiple groupings of the same individuals. Integrating insights from these models into the study of social cognition allows for greater predictive precision and stimulates innovative strategies for stereotype change. The project also supports a summer internship program to involve students from diverse backgrounds, along with regular engagement in public outreach and education via print interviews, social media, blog posts, and public lectures.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.