Computational Psychiatry Needs Time and Context

Abstract

Even the most ardent proponents of computational psychiatry admit that the field is far from influencing routine clinical practice. We propose one reason for this is that the field has had difficulty recognizing the variability among mental health problems—and the resulting need to model context and temporal dynamics for many problems. We develop three heuristics for estimating whether time and context are important to a mental health problem. Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry