Model-Based Cognitive Neuroscience Approaches to Computational Psychiatry

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.