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Related Experiment Video

Updated: Feb 11, 2026

Reducing State Anxiety Using Working Memory Maintenance
08:17

Reducing State Anxiety Using Working Memory Maintenance

Published on: July 19, 2017

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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;

Annual Review of Neuroscience
|May 1, 2018
PubMed
Summary
This summary is machine-generated.

Decision-making under uncertainty is complex. This review explores how anxiety and depression alter computations for decision-making, impacting threat avoidance and reward seeking.

Keywords:
anxietydecision makingdepressionreinforcement learningrewardthreat

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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.