Model-Based Cognitive Neuroscience Approaches to Computational Psychiatry

TLDR

Psychiatric research is in crisis. The authors highlight computational psychiatry as a means to shift from symptom‑based to objective, multidimensional computational descriptors, surveying recent efforts and outlining a toolbox to support this transition. They identify four levels—behavioral tasks, computational models, hierarchical Bayesian parameter estimation, and machine‑learning clustering—and demonstrate these methods on two datasets. They emphasize challenges that must be addressed in future research.

Abstract

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.