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Paper Detail

Paper: PS-1A.70
Session: Poster Session 1A
Location: H Lichthof
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: State Anxiety Biases Precision Estimates in Volatile Environments
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Thomas Hein, MarĂ­a Herrojo-Ruiz, Goldsmiths University of London, United Kingdom
Abstract: In an uncertain world, we must learn the statistical regularities to make accurate predictions about upcoming events. Real-world experiences are rarely certain, and residual uncertainty may motivate growing levels of anxiety: the excessive worry over future outcomes. How alterations in processing uncertainty affect learning in individuals with high levels of trait anxiety have been previously studied, yet still little is known about how states of anxiety shape learning processes. To test this, a state anxious and control group performed a reward-based learning task in a volatile environmental setting while we recorded electrophysiological (EEG) data. By using a hierarchical Bayesian model of performance, we quantified the effect of state anxiety on Bayesian belief updating and estimates of precision-weighted prediction errors in the brain. Our results reveal that state anxiety is characterised by a lower learning rate, lower precision estimates about volatility, and higher precision estimates about beliefs concerning understanding of task probability mappings. EEG analysis provided additional evidence linking the anomalous precision estimates in state anxiety to brain regions previously described to be involved in poorer learning in this population. These findings extend prior computational work on trait anxiety, suggesting states of anxiety in healthy human participants bias computational learning mechanisms.