Schizophrenia Bulletin Advance Access originally published online on September 12, 2008
Schizophrenia Bulletin 2008 34(6):1008-1011; doi:10.1093/schbul/sbn123
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© The Author 2008. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oxfordjournals.org.
Schizophrenia: A Computational Reinforcement Learning Perspective
Department of Psychology, Program in Neuroscience, University of Arizona, 1503 E University Boulevard, Tucson, AZ 85721
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As one of the most complex neurocognitive disorders, schizophrenia (SZ) is a devastating condition for which the underlying sources are far from being fully understood. Indeed, it is likely that there are multiple etiologies to the disease and heterogeneity within the population. Moreover, it is impossible to understand from a purely mechanistic basis how a patient would come to believe so strongly in delusions as to, for example, gouge out his own eyes.
Nevertheless, science marches forward, and the last 30 years or so have produced a wealth of knowledge regarding some of the risk factors, genetics, pharmacology, cognitive deficits, and underlying neurobiology associated with the disease.1–4 In part because of the efficacy of antipsychotic treatments via dopamine D2 receptor blockade,5 the majority of this research focuses on dysfunctions of the dopaminergic system, in both frontal cortex and basal ganglia, thought to be related to negative
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1 To whom correspondence should be addressed; tel: 520-626-4787, fax: 520-621-9306, e-mail: mfrank@u.arizona.edu.
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