Miranda Beltzer is a Ph.D. candidate in the Department of Psychology at the University of Virginia. Miranda’s dissertation research focuses on how socially anxious people learn from positive and negative social feedback, and whether these cognitive biases can change with a computerized intervention. Throughout graduate school, her research has centered on understanding social reinforcement learning processes, using mobile technology and reinforcement learning to develop scalable interventions towards increasing access to mental health treatment, and probing mental health stigma across the United States. She has also provided psychotherapy to diverse clients at the Mary Ainsworth Clinic, Western State Hospital, and the UVA Family Stress Clinic. Next year she will complete her dissertation research with the support of a Dean’s Dissertation Completion Fellowship and a P.E.O. Scholar Award.
Examining Social Reinforcement Learning Biases in Social Anxiety
Although social connectedness is critical to health and wellbeing, the 12% of Americans who experience social anxiety disorder in their lifetime avoid social situations, which can lead to pervasive impairments. Aberrant social reinforcement learning, or differences in learning from positive and negative social feedback, may underlie many of the cognitive, emotional, and behavioral difficulties in social anxiety disorder. A few studies have begun to probe aspects of social reinforcement learning, but this dissertation will serve as a more comprehensive, direct examination of this critical, understudied learning process. The proposed dissertation takes a computational approach to investigate how biases in social reinforcement learning may contribute to social anxiety and whether these learning processes can be changed with a targeted, online intervention.
The three studies that comprise this dissertation are drawn from a large data collection we recently completed. Studies 1 and 2 assess the extent to which socially anxiety is characterized by biased learning from social feedback in two domains relevant to social anxiety disorder: social interactions and social performance. Study 1 uses a social probabilistic learning task to assess how people use positive and negative social feedback to adjust their expectations of others. Study 2 examines the extent to which people use positive and negative social feedback to adjust their expectations of their own performance on a speech. Study 3 tests the degree to which the social reinforcement learning biases measured in Studies 1 and 2 are malleable through an online cognitive bias modification intervention. This dissertation seeks to advance knowledge about social anxiety disorder by pinpointing specific biases in social reinforcement learning (Studies 1 and 2), which may improve our ability to develop targeted treatments (Study 3).