Research Overview
We aim to uncover the cognitive, affective, and neural processes behind the complex choices that form the basis of human social behavior. Our research focuses on two main questions, broadly construed:
How do people learn to navigate the social world?
The social world is rife with uncertainty. We do not have access to the inner workings of others' minds, so we must constantly assess how to behave towards another person given very limited information (e.g., is does this person seem trustworthy?). This problem becomes even harder when we consider the fact that all of our social lives operate against the backdrop of large, evolving, and complicated social networks, where our actions can reverberate far beyond a single interaction with a friend. We tackle how the mind and brain represent information about other people, how that information is leveraged to trust or to cooperate with the right people, and how these processes can go awry, leading to suboptimal behaviors. From trust and cooperation, to political polarization, we measure both individual interactions and the most complex social environments, our social networks.
Topics include: Altruism, anxiety, cognitive maps, computational modeling, cooperation, generative replay, information transmission, latent structure learning, naturalistic contexts, neuroimaging, political polarization, punishment, structure of social networks, trust
How do emotions bias social learning and behavior?
At the heart of every social thought, choice, or interaction is an emotion. A long tradition of affective science argues that emotion contributes to both the representation and computation of value. The lab extends these ideas by leveraging various tools, from machine learning to neuroimaging, to better understand how emotion and affect drive the way we represent and interact with others in our social world. Most recently, we have argued that affective prediction errors play a critical role in shaping social behavior, helping us to learn more efficiently.
Topics include: Affect, depression, EEG, emotion, empathy, learning, machine learning, physiological measurements, reward, social value