Model-Based Cognitive Neuroscience Approaches to Computational Psychiatry Clustering and Classification

• Published in: Clinical psychological science Vol. 3; no. 3; pp. 378 - 399
• Main Authors: Wiecki, Thomas V., Poland, Jeffrey, Frank, Michael J.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.05.2015
• Subjects: RDoC; computational psychiatry; drift diffusion model; computational cognitive neuroscience; response inhibition
• ISSN: 2167-7026; 2167-7034
• DOI: 10.1177/2167702614565359

Psychiatric research is in crisis. We highlight efforts to overcome current challenges by focusing on the emerging field of computational psychiatry, which might enable the field to move from a symptom-based description of mental illness to descriptors based on objective computational multidimensional functional variables. We survey recent efforts toward this goal and describe a set of methods that together form a toolbox to aid this research program. We identify four levels in computational psychiatry: (a) behavioral tasks that index various psychological processes, (b) computational models that identify the generative psychological processes, (c) parameter-estimation methods concerned with quantitatively fitting these models to subject behavior by focusing on hierarchical Bayesian estimation as a rich framework with many desirable properties, and (d) machine-learning clustering methods that identify clinically significant conditions and subgroups of individuals. As a proof of principle, we apply these methods to two different data sets. Finally, we highlight challenges for future research.