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Computational Psychiatry


Computational Psychiatry publishes original research articles and reviews that involve the application, analysis, or invention of theoretical, computational and statistical approaches to mental function and dysfunction. Topics include brain modeling over multiple scales and levels of analysis, and the use of these models to understand psychiatric dysfunction, its remediation, and the sustenance of healthy cognition through the lifespan. The journal also has a special interest in computational issues pertaining to related areas such as law and education.

Computational Psychiatry is an Open Access journal.

Table of Contents

Vol. 1, 2017


pp. 1-1
Abstract | PDF (79.28 KB)
Computational Nosology and Precision Psychiatry
pp. 2-23
Abstract | PDF (820.79 KB)
Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package
pp. 24-57
Abstract | PDF (1.56 MB)
Estimating Dynamic Signals From Trial Data With Censored Values
pp. 58-81
Abstract | PDF (1.16 MB)
Implications of Information Theory for Computational Modeling of Schizophrenia
pp. 82-101
Abstract | PDF (341.4 KB)
Computational Modeling of Contrast Sensitivity and Orientation Tuning in First-Episode and Chronic Schizophrenia
pp. 102-131
Abstract | PDF (2.4 MB)
A Neural Model of Empathic States in Attachment-Based Psychotherapy
pp. 132-167
Abstract | PDF (720.9 KB)
Can Peripheral Blood-Derived Gene Expressions Characterize Individuals at Ultra-high Risk for Psychosis?
pp. 168-183
Abstract | PDF (793.65 KB)
A Theoretical Framework for Evaluating Psychiatric Research Strategies
pp. 184-207
Abstract | PDF (1.1 MB)
Learning and Choice in Mood Disorders: Searching for the Computational Parameters of Anhedonia
pp. 208-233
Abstract | PDF (1.04 MB)