Department of Cognitive, Linguistic, & Psychological Sciences
Computational psychiatry has made important advances and proof-of-principle demonstrations, but it still seems far away from influencing routine clinical practice. Why? I will argue that the field has had difficulty recognizing the variability among mental health problems—and, consequently, the need to model context and temporal dynamics for many problems. Modeling context and temporal dynamics is challenging conceptually and logistically; it would be much easier not to do so. I will suggest three heuristics for deciding whether such modeling is necessary for a given mental health problem. As a case study, I will apply a critical lens to my own developing research program on rumination and worry and their relations to depression and anxiety disorders. I will argue that modeling time and context is indeed critical for these disorders. I will draw out the implications for my research, with an eye toward general principles for modeling problems of sufficient complexity that they are best understood as interacting elements unfolding in context over time.
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