1. Pessimistically biased perception in panic disorder during risk learning
- Author
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Kyoung-Uk Lee, Sunghwan Kim, Minchul Kim, and Bumseok Jeong
- Subjects
media_common.quotation_subject ,Psychological intervention ,Anxiety ,03 medical and health sciences ,0302 clinical medicine ,Perception ,medicine ,Humans ,Set (psychology) ,Association (psychology) ,Agoraphobia ,Pathological ,media_common ,Panic disorder ,Bayes Theorem ,Cognition ,medicine.disease ,Anxiety Disorders ,030227 psychiatry ,Psychiatry and Mental health ,Clinical Psychology ,Panic Disorder ,medicine.symptom ,Psychology ,030217 neurology & neurosurgery ,Clinical psychology - Abstract
Background Despite the well-known association between anxiety and risk-avoidant decision making, it is unclear how pathological anxiety biases risk learning. We propose a Bayesian inference model with bias parameters of prior, learning, and perception during risk learning in individuals with pathological anxiety. Methods Patients with panic disorder (PD, n = 40) and healthy control subjects (n = 84) completed the balloon analog risk task (BART). By fitting our computational model of three bias parameters (prior belief, learning rate, and perceptual bias) to the participants' behavior, we estimated the degree of bias in risk learning and its relationship with anxiety symptoms. Results Relative to the healthy control subjects, the pathologically anxious participants exhibited a biased underestimation of perceptual evidence rather than differences in priors and learning rates. The degree of perceptual bias was correlated with the anxiety and depression symptom severity in the patients with PD. Furthermore, our proposed model was the winning model for BART data in an external data set from different patient groups. Conclusions Our results showed that individuals with pathological anxiety demonstrate perceptual bias in evidence accumulation, which may explain why patients with anxiety overestimate risk in their daily lives. This clarification highlights the importance of interventions focusing on perceptual bias, such as enhancing the clarity of favorable outcome probabilities.
- Published
- 2020