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- W4321202958 endingPage "105103" @default.
- W4321202958 startingPage "105103" @default.
- W4321202958 abstract "Making effective decisions during approach-avoidance conflict is critical in daily life. Aberrant decision-making during approach-avoidance conflict is evident in a range of psychological disorders, including anxiety, depression, trauma-related disorders, substance use disorders, and alcohol use disorders. To help clarify etiological pathways and reveal novel intervention targets, clinical research into decision-making is increasingly adopting a computational psychopathology approach. This approach uses mathematical models that can identify specific decision-making related processes that are altered in mental health disorders. In our review, we highlight foundational approach-avoidance conflict research, followed by more in-depth discussion of computational approaches that have been used to model behavior in these tasks. Specifically, we describe the computational models that have been applied to approach-avoidance conflict (e.g., drift-diffusion, active inference, and reinforcement learning models), and provide resources to guide clinical researchers who may be interested in applying computational modeling. Finally, we identify notable gaps in the current literature and potential future directions for computational approaches aimed at identifying mechanisms of approach-avoidance conflict in psychopathology." @default.
- W4321202958 created "2023-02-18" @default.
- W4321202958 creator A5005152508 @default.
- W4321202958 creator A5006945878 @default.
- W4321202958 creator A5011511052 @default.
- W4321202958 creator A5052379796 @default.
- W4321202958 date "2023-04-01" @default.
- W4321202958 modified "2023-10-17" @default.
- W4321202958 title "Quantifying aberrant approach-avoidance conflict in psychopathology: A review of computational approaches" @default.
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