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Poster C113 in Poster Session C - Friday, August 9, 2024, 11:15 am – 1:15 pm, Johnson Ice Rink
Computational Modeling of Human Associative Learning in a Complex Approach-Avoidance Learning Task
Franziska Usée1 (), Sebastian Schmidt1, Christiane A. Melzig1,2, Dirk Ostwald3; 1Philipps-Universität Marburg, 2Center for Mind, Brain and Behavior (CMBB), 3Otto von Guericke University
Despite its key role in the development, maintenance, and treatment of anxiety disorders, the detailed mechanisms of human avoidance learning remain elusive. Here, we report on a novel approach-avoidance learning paradigm that requires participants to learn associations between complex visual stimuli and combined positive and negative reward states. Using an agent-based behavioral modeling approach, we show that a Rescorla-Wagner learning-based agent with a prior expectation bias parameter best explains the learning behavior of 50 participants, paving the way for a more fine-grained computational understanding of the etiology of anxiety disorders.
Keywords: Avoidance learning, behavioral modeling, Rescorla-Wagner learning, visual foraging