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Computational Perspectives on Cognition in Anorexia Nervosa: A Systematic Review.
Marta Radzikowska1,2, Alexandra C Pike3, Sam Hall-McMaster1,4
1Max Planck Institute for Human Development, Berlin, Germany.
Computational Psychiatry (Cambridge, Mass.)
|April 14, 2025
Summary
Computational psychiatry is revealing cognitive mechanisms behind anorexia nervosa (AN). This review synthesizes research on reinforcement learning, decision-making, and cognitive control in AN, highlighting future directions for clinical relevance.
Area of Science:
- Neuroscience
- Psychiatry
- Computational Psychiatry
Background:
- Anorexia nervosa (AN) is a severe eating disorder characterized by behavioral, cognitive, and neural changes leading to low body weight.
- Understanding the cognitive underpinnings of AN symptoms like restrictive eating and body image preoccupation is crucial.
Purpose of the Study:
- To systematically review the progress in computational approaches to understanding the cognitive mechanisms of anorexia nervosa.
- To identify and appraise current themes in the computational study of AN.
Main Methods:
- Systematic review of articles using reproducible criteria.
- Identification and synthesis of five key themes in computational research on AN.
Main Results:
- Five major themes emerged: reinforcement learning, value-based decision-making, goal-directed vs. habitual control, cognitive flexibility, and theory-based accounts.
- Insights from each theme were appraised, alongside methodological considerations.
Conclusions:
- Computational approaches offer valuable insights into the cognitive mechanisms of anorexia nervosa.
- Future research should focus on establishing the clinical relevance of identified (neuro)computational changes in AN.
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