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Anxiety, Depression, and Decision Making: A Computational Perspective.
Sonia J Bishop1,2, Christopher Gagne1
1Department of Psychology, University of California, Berkeley, California 94720, USA;
Decision-making under uncertainty is complex. This review explores how anxiety and depression alter computations for decision-making, impacting threat avoidance and reward seeking.
Area of Science:
- Cognitive Neuroscience
- Psychopathology
- Behavioral Economics
Background:
- Everyday actions involve uncertain outcomes, probabilities, values, and effort.
- Individual differences in estimating these factors and using model-free versus model-based decision-making influence behavior.
- Anxiety and depression are linked to decision-making difficulties, specifically threat avoidance and reduced reward seeking.
Purpose of the Study:
- To review evidence on altered decision-making computations in anxiety and depression.
- To examine the consequences of these alterations for action selection.
- To propose a framework integrating findings for future research.
Main Methods:
- Literature review of studies examining decision-making processes in anxiety and depression.
- Analysis of computational biases and their behavioral consequences.
- Synthesis of findings into a conceptual framework.
Main Results:
- Anxiety is associated with biases in threat evaluation and avoidance behaviors.
- Depression is linked to altered reward processing and reduced goal-directed actions.
- Specific computational deficits in decision-making are identified for each condition.
Conclusions:
- Anxiety and depression involve distinct, yet potentially overlapping, alterations in decision-making computations.
- Understanding these biases is crucial for specifying the precise deficits in psychopathology.
- The proposed framework can guide future research on decision-making in mental health.

