I am a lecturer (assistant professor) in the Department of Psychology at Royal Holloway, University of London.
Social groups and tribes provide immense benefits but incur great costs. Understanding how social group memberships drive actions and perception of the world may help us to best harness these byproducts of social groups. Broadly speaking, I am interested in (i) how social group information about others affects our judgments and behaviors and (ii) how we come to infer who is and is not a member of our social groups in the first place, which can in turn affect our predictions about and behavior towards others. To answer these questions, I have applied computational models and used functional neuroimaging (fMRI), in-lab, field, and online experiments.
Ph.D. in Psychology, 2019
A.B. in Economics, Certificate in Neuroscience, 2009
Humans form social coalitions in every society, yet we know little about how we learn and represent social group boundaries. Here we derive predictions from a computational model of latent structure learning to move beyond explicit category labels and interpersonal, or dyadic similarity as the sole inputs to social group representations. Using a model-based analysis of functional neuroimaging data, we find that separate areas correlate with dyadic similarity and latent structure learning. Trial-by-trial estimates of ‘allyship’ based on dyadic similarity between participants and each agent recruited medial prefrontal cortex/pregenual anterior cingulate (pgACC). Latent social group structure-based allyship estimates, in contrast, recruited right anterior insula (rAI). Variability in the brain signal from rAI improved prediction of variability in ally-choice behavior, whereas variability from the pgACC did not. These results provide novel insights into the psychological and neural mechanisms by which people learn to distinguish “us” from “them”.