Pragmatic Computational Psychiatry: Effects of Mood, Anxiety, and Addiction on Computational Decision Processes and the Implications for Clinical Psychiatry.

Neuroscience has made tremendous progress in understanding the basic neural circuitry that underlies processes such as attention, memory, emotion processing, and decision-making. Yet, little progress has been made to utilize these insights, to apply them to psychiatric populations or to change clinical care. The development of new diagnostics or therapeutics based on neuroscience approaches to understand the pathophysiology of these illnesses has stalled (1). With the development of a new diagnostic classification for mental disorders (2), neuroscience has had virtually no impact on contributing to the delineation and definition of the disorder categories. Computational psychiatry (3-6) encompasses a range of different approaches and methods, which include, but are not limited to, (a) biophysically based models developed to test cellular-level and synaptic hypotheses, (b) connectionist models that give insight into large-scale neural-system-level disturbances in schizophrenia, and© models that provide a formalism for observations of complex behavioral deficits, such as negative symptom. Three examples of studies will be presented focused on (a) using computational approaches to refine prediction in drug addiction (7), (b) delineating the motivational and motor component of behavioral dysfunctions in depression (8), and© examining the role of anxiety in differentiating random fluctuations from changes in the environment. These examples have direct implications for prognosis and treatment selection and may help to establish the utility of pragmatic computational psychiatry.

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