Computational Cognitive Neuroscience Approaches to Deconstructing Mental Function and Dysfunction
Computational Cognitive Neuroscience Approaches to Deconstructing Mental Function and Dysfunction
Advances in our understanding of brain function and dysfunction require the integration of heterogeneous sources of data across multiple levels of analysis, from biophysics to cognition and back. This chapter reviews the utility of computational neuroscience approaches across these levels and how they have advanced our understanding of multiple constructs relevant for mental illness, including working memory, reward-based decision making, model-free and model-based reinforcement learning, exploration versus exploitation, Pavlovian contributions to motivated behavior, inhibitory control, and social interactions. The computational framework formalizes these processes, providing quantitative and falsifiable predictions. It also affords a characterization of mental illnesses not in terms of overall deficit but rather in terms of aberrations in managing fundamental trade-offs inherent within healthy cognitive processing.
Keywords: Strüngmann Forum Report, computational psychiatry, computational cognitive neuroscience, reinforcement learning, decision making, dopamine, prefrontal cortex, basal ganglia
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