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There is unprecedented interest in computational and decision neuroscience approaches to psychiatry that can provide novel, mechanistic insights about mental illness. Most such applications have emphasized static, trait-like differences across psychiatric populations or from health, but this may not fully capture clinical reality or need. Almost all psychiatric disorders are characterized by some stereotyped and dynamic shifts in their clinical features (symptom exacerbation, relapse) that are of primary interest in treatment. Addiction in particular, considered a preeminent disorder of choice, is almost exclusively defined in its chronic stages by its temporal course, whereby individuals transition between periods of abstinence and drug use. In recent work, we have found that different decision variables (in this case capturing preferences for uncertain outcomes) might track these changing clinical features, suggesting that a multi-dimensional set of decision variables can provide a more complete picture. At the neural level, this distinction implies that clinically-relevant differences might lie outside of the global integrated value signal, perhaps instead more upstream at the level of attribute coding, even for very conceptually related decision processes. A more refined approach that considers the temporal aspect of psychiatric illness might facilitate the real-world clinical utility of computational and decision neuroscience. More broadly these findings reveal an under-appreciated degree of state-dependence in some decision variables and suggest that psychiatric populations could be leveraged in theory development as well.
Anna Konova, PhD
Assistant Professor, Dept. of Psychiatry & UBHC
Core Faculty, Brain Health Institute
Rutgers University – New Brunswick