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Poster B27 in Poster Session B - Thursday, August 8, 2024, 1:30 – 3:30 pm, Johnson Ice Rink
Absence of Systematic Effects of Trait Anxiety on Learning Under Uncertainty
Muhammad Hashim Satti1,2 (), Katharina Wille1, Matthew Nassar3,4, Radoslaw Cichy1, Nicolas Schuck5,6, Peter Dayan2,7,8, Rasmus Bruckner1,5; 1Freie Universität Berlin, Berlin Germany, 2Max Planck School of Cognition, Leipzig, Germany, 3Department of Neuroscience, Brown University, Providence, United States, 4Robert J. & Nancy D. Carney Institute for Brain Science, Brown University, Providence, United States, 5Institute of Psychology, Universität Hamburg, Hamburg, Germany, 6Max Planck Research Group NeuroCode, Max Planck Institute for Human Development, Berlin, Germany, 7Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 8University of Tübingen, Tübingen, Germany
Ignorance can be deadly, making learning essential to survival. However, learning also needs to be adjusted according to the prevailing uncertainty – with faster change or, in typical cases, a higher learning rate (LR), in environments that change quickly and a lower learning rate when the environment's latent state does not change. Failing to adjust the LR flexibly can lead to learning impairments – an affliction somewhat inconsistently found to affect behavior, particularly in individuals with high trait anxiety. We conducted five experiments (N=550 participants) using an online game-based variant of a predictive inference task to investigate whether high trait anxiety is associated with impaired LR adjustment. While finding model-based and model-agnostic evidence of uncertainty-related LR modulation across individuals, we did not find any relations to trait anxiety. We obtained consistent results in a control experiment with a binary reversal learning task. Using Bayes factors to test the null hypothesis, our results suggest that trait anxiety is not systematically associated with inflexible learning in uncertain and changing environments.
Keywords: anxiety learning uncertainty computational psychiatry