The social groups to which we belong -- families, friends, teams, companies, organizations -- have a pervasive influence on our decision making. Indeed, people often abandon their own personal judgments and choices in favor of conforming to a group majority. In some cases, such alignment with the group may protect against social rejection and grant access to shared resources. However, research has shown that people also conform even when there is no social or economic gain for them. One explanation for this result is that the act of reaching consensus with one's group might be valuable in itself -- an idea that is supported by recent neuroscientific research showing that the brain's reward circuit is involved in social conformity. This project combines computational cognitive modeling with functional magnetic resonance imaging (fMRI) and behavioral economic procedures to better understand the subjective value of conforming and dissenting decisions. By uncovering the motivating and reinforcing properties of majority alignment, this research will shed light on a fundamental aspect of organizational, professional and personal interactions. Among the broader impacts is a priority to share data and code to benefit research in cognitive and social neuroscience, to involve the participation of women, underrepresented groups and high school students, and to inform the development of tools that can improve the social competency of impaired individuals.
This project uses model-based cognitive neuroscience to investigate the motivating and reinforcing properties of social conformity. The main approach is to make the desire to conform compete with the prospect of economic gain, and to assess the transfer of conformity-based valence to other features of the environment. A second objective is to investigate how the relative size and perceived competence of a majority opinion influences the value associated with consensus and dissent. A third objective is to explore a common neural representation for conformity and conventional reward. All of the studies use variations of standard tasks employed in research on economic decision-making. Reinforcement learning models are used to make quantitative predictions about behavioral choices and neural activity. Model-based fMRI analyses are combined with connectivity and multi-voxel pattern analyses to investigate interactions between social and motivational neural networks, and to explore a distributed overlapping neural code for social and economic currencies. By formalizing consensus-seeking behavior as reinforcement learning, the project contributes in several ways to the behavioral and neuroscientific literatures on social conformity. In particular, the reinforcement learning framework provides a mechanism for how apparently inconsequential consensus decisions may be motivated by previously acquired valence, and for how that valence may then be transferred to other contextual and interpersonal features. This approach bridges a critical gap, at both neural and behavioral levels, between complex socio-cognitive representations and basic mechanisms of reward-based learning and decision-making.
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.
