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Thirty-Third Annual Meeting of the Society for Research in Psychopathology

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15. Computational Mechanisms by Which Prior Knowledge of Threat Biases Perceptual Decision Making in Anxiety

Anxiety is characterized by its anticipatory nature clinically. However, our understanding of perceptual and attentional biases in anxiety is almost entirely based on behavioral or neural response to stimuli. According to the predictive coding theory, human brain generates context-based predictions about incoming sensory information. Under this framework, anticipatory anxiety can be studied in terms of prediction-related biases. We used hierarchical drift diffusion modeling (HDDM) to determine measures by which threatening cues bias perceptual decisions regarding upcoming threatening and neutral targets. Furthermore, we examined whether intolerance of uncertainty (IU), a dispositional characteristic of anxiety disorders, relates to these measures of bias transdiagnostically across a sample of individuals diagnosed with generalized anxiety disorder (N=23), social anxiety (N=24), panic disorder (N=3), and no anxiety disorder (N=34). Participants performed a decision-making task in which they used threatening or neutral cues to discriminate between threatening and neutral faces. Model comparison results showed that threatening cues biased the response towards a threatening face decision and reduced the decision-making threshold compared to neutral cues. IU was associated with an increased decision-making threshold, especially following highly predictive threatening cues. Our results clarify the computational mechanisms by which prior knowledge of threat biases perceptual decision making in anxiety.

Xian Zhang
Stony Brook University

Gabriella Imbriano
Stony Brook University

Jingwen Jin
Stony Brook University

Aprajita Mohanty
Stony Brook University

 


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