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Computational PsychiatryNew Perspectives on Mental Illness$
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A. David Redish and Joshua A. Gordon

Print publication date: 2016

Print ISBN-13: 9780262035422

Published to MIT Press Scholarship Online: May 2017

DOI: 10.7551/mitpress/9780262035422.001.0001

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Computational Cognitive Neuroscience Approaches to Deconstructing Mental Function and Dysfunction

Computational Cognitive Neuroscience Approaches to Deconstructing Mental Function and Dysfunction

Chapter:
(p.101) 6 Computational Cognitive Neuroscience Approaches to Deconstructing Mental Function and Dysfunction
Source:
Computational Psychiatry
Author(s):

Michael J. Frank

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262035422.003.0006

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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