RESEARCH

Ubiquitous among humans is the experience of a self, the maintenance of beliefs about the self, and the identification with groups that share common backgrounds, interests, and goals. Research in the lab examines the mechanisms that subserve self-representation. For example, (1) What is the basis of self-knowledge, and what motivations modulate self-representation? (2) How do beliefs about self-change as a function of social contexts and experiences? (3) How do the myriad self-representations that define the self, from broader social groups to trait characteristics to bodily representations, motivate cognition and behavior?

One of the humans' greatest desires is to connect with other people. Yet despite people's strong motivation and need for connection, America and other countries face a growing loneliness epidemic. Fortunately, one way that people can combat the rise of loneliness and build social connections is through self-disclosure: sharing thoughts, feelings, and experiences with others. Ongoing work at the lab examines the following questions about self-disclosure: (1) What features of people's disclosures matter most for promoting connection? (2) How do "disclosers" transfer experiences to listeners’ brains (i.e. initiate neural synchrony) to create real-time, moment-to-moment understanding? (3) How can listeners do a better job of remembering others' disclosures and responding supportively?

How do people maintain beliefs when faced with conflicting evidence? Biases in people’s evaluations has traditionally been attributed to group-based motivated interpretations, that is, that people directly misinterpret or avoid information in order to arrive at conclusions that favor the ingroup. However, evidence from cognitive sampling models challenges these assumptions, demonstrating that biases can also emerge from processes that are more innocuous due to people drawing inferences from limited experiences. These models characterize people as naïve statisticians, capable of producing veridical representations of data encountered, but naïve to the processes that generate it. Current research at the lab integrates these two perspectives to elucidate how group-based biases manifest across the information processing chain, beginning with (1) a biased selection of information, leading to (2) skewed samples of information, which interacts with (3) motivated interpretations to produce evaluative biases.