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Related Experiment Video

Updated: May 26, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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Published on: May 15, 2020

Computational psychiatry.

P Read Montague¹, Raymond J Dolan, Karl J Friston

¹Virginia Tech Carilion Research Institute and Department of Physics, Virginia Tech, 2 Riverside Circle, Roanoke, VA 24016, USA. read@vt.edu

Trends in Cognitive Sciences

|December 20, 2011

Summary

This summary is machine-generated.

Computational psychiatry aims to understand mental illness by examining abnormal computations. This new field uses computational models to analyze decision-making in both healthy and disordered states.

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Area of Science:

Computational neuroscience
Cognitive science
Psychiatry

Background:

Computational models are widely used in neuroscience and cognitive science.
Mental illness assessment has not significantly benefited from computational approaches.
Diseases of the mind lack systematic conceptualization in computational terms.

Purpose of the Study:

Introduce the emerging field of computational psychiatry.
Define mental dysfunction through aberrant computations across multiple scales.
Explore computational frameworks for understanding decision-making in health and disease.

Main Methods:

Utilizing reinforcement learning frameworks.
Employing game theoretic models.
Analyzing decision-making processes.

Main Results:

Early efforts demonstrate the potential of computational approaches in psychiatry.
Reinforcement learning and game theory offer insights into aberrant decision-making.
This approach provides a novel lens for understanding mental dysfunction.

Conclusions:

Computational psychiatry is a promising new field for characterizing mental illness.
Further theory development is needed.
Large-scale computational phenotyping in humans is crucial for advancing the field.