Anxiety, Depression, and Decision Making: A Computational Perspective

• Published in: Annual review of neuroscience Vol. 41; no. 1; pp. 371 - 388
• Main Authors: Bishop, Sonia J, Gagne, Christopher
• Format: Journal Article
• Language: English
• Published: United States Annual Reviews 08.07.2018; Annual Reviews, Inc
• Subjects: Anxiety; Avoidance behavior; Behavioral psychology; Computer applications; Decision making; depression; Mental depression; Mental health; Psychopathology; Reinforcement; reinforcement learning; reward; threat; reward; anxiety; depression; threat; decision making; reinforcement learning
• ISSN: 0147-006X; 1545-4126
• DOI: 10.1146/annurev-neuro-080317-062007

In everyday life, the outcomes of our actions are rarely certain. Further, we often lack the information needed to precisely estimate the probability and value of potential outcomes as well as how much effort will be required by the courses of action under consideration. Under such conditions of uncertainty, individual differences in the estimation and weighting of these variables, and in reliance on model-free versus model-based decision making, have the potential to strongly influence our behavior. Both anxiety and depression are associated with difficulties in decision making. Further, anxiety is linked to increased engagement in threat-avoidance behaviors and depression is linked to reduced engagement in reward-seeking behaviors. The precise deficits, or biases, in decision making associated with these common forms of psychopathology remain to be fully specified. In this article, we review evidence for which of the computations supporting decision making are altered in anxiety and depression and consider the potential consequences for action selection. In addition, we provide a schematic framework that integrates the findings reviewed and will hopefully be of value to future studies.