What Does Computational Psychiatry Need to Explain to Capture Mechanisms of Psychopathology?
What Does Computational Psychiatry Need to Explain to Capture Mechanisms of Psychopathology?
Facts, Almost Facts, and Hints
This chapter provides specific research examples on the neurobiology of mental illness—using psychosis as a case in point—that may begin to rise to the level of “facts,” or at least “almost facts” or strong “hints,” about important etiological mechanisms that need to be explained to capture key components of at least some facets of mental illness. These examples are then used to illustrate where computational psychiatry approaches may help. In particular, there is an opportunity to provide links across different levels of analysis (e.g., behavior, systems level, specific circuits and even genetic influences) in ways that can lead to a more unified framework for understanding the apparent multitude of impairments present in psychosis, which may in turn lead to the identification of new treatment or even prevention targets. This chapter also discusses some of the known conundrums about the etiology of mental illness that need to be accounted for in computational frameworks, including the presence of heterogeneity within current diagnostic categories, the vast degree of comorbidity across current diagnostic categories, and the need to reconceptualize the dimensionality versus categorical nature of mental illness.
Keywords: Strüngmann Forum Report, cognitive control, comorbidity, computational psychiatry, dopamine, heterogeneity, prefrontal cortex, psychosis, reinforcement, working memory
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