Category: Cognitive Science / Cognitive Processes

Symposium

Social Anxiety and Dynamic Social Reinforcement Learning in a Volatile Environment

Friday, November 17
3:30 PM - 5:00 PM
Location: Cobalt 502, Level 5, Cobalt Level

Keywords: Social Anxiety | Cognitive Vulnerability | Cognitive Processes
Presentation Type: Symposium

Background: Human behavior is driven by seeking rewards and avoiding punishments, so difficulties learning about rewards and punishments can lead to maladaptive behavior. Aberrant monetary reinforcement learning has been implicated in depression, schizophrenia, and other disorders, and researchers are beginning to find evidence for altered learning of social rewards and punishments in social anxiety disorder. To our knowledge, no studies to date have examined how these individuals respond when the probabilities of social reward and punishment change. The present study examines how social anxiety affects probabilistic social reward and punishment learning and dynamic updating of learned probabilities in a volatile environment.


Methods: 222 participants completed an online study, including questionnaires and a version of Cyberball modified to assess probabilistic social reinforcement learning and dynamic updating of learned probabilities. Mixed-effects models were used to analyze throw patterns as a function of social anxiety. To assess the weight given to new information in a volatile social environment, dynamic learning rates were calculated by applying Q-learning algorithms to overlapping sets of throws over the course of the game.


Results: The modified Cyberball task appears to be a valid measure of social reward learning; throws to the rewarder were significantly higher than to other avatars (Β=2.25, p=0.037), and fewer throws to the neutral avatar who had been the punisher in the previous block (Β=-0.035, p=0.020).


Conclusion: Analyses indicate that social anxiety may be characterized by difficulty updating learned social probabilities from punishing to neutral, and some impaired learning of social reward. Analyses on learning rates will also be discussed to provide insight into the nature of these dynamic reinforcement learning differences.

Miranda Beltzer

Doctoral Student
University of Virginia

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