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Front Psychiatry. 2013 Dec 23;4:172. doi: 10.3389/fpsyt.2013.00172.

Reinforcement learning and dopamine in schizophrenia: dimensions of symptoms or specific features of a disease group?

Author information

1
Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig , Germany ; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin , Berlin , Germany.
2
Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin , Berlin , Germany.

Abstract

Abnormalities in reinforcement learning are a key finding in schizophrenia and have been proposed to be linked to elevated levels of dopamine neurotransmission. Behavioral deficits in reinforcement learning and their neural correlates may contribute to the formation of clinical characteristics of schizophrenia. The ability to form predictions about future outcomes is fundamental for environmental interactions and depends on neuronal teaching signals, like reward prediction errors. While aberrant prediction errors, that encode non-salient events as surprising, have been proposed to contribute to the formation of positive symptoms, a failure to build neural representations of decision values may result in negative symptoms. Here, we review behavioral and neuroimaging research in schizophrenia and focus on studies that implemented reinforcement learning models. In addition, we discuss studies that combined reinforcement learning with measures of dopamine. Thereby, we suggest how reinforcement learning abnormalities in schizophrenia may contribute to the formation of psychotic symptoms and may interact with cognitive deficits. These ideas point toward an interplay of more rigid versus flexible control over reinforcement learning. Pronounced deficits in the flexible or model-based domain may allow for a detailed characterization of well-established cognitive deficits in schizophrenia patients based on computational models of learning. Finally, we propose a framework based on the potentially crucial contribution of dopamine to dysfunctional reinforcement learning on the level of neural networks. Future research may strongly benefit from computational modeling but also requires further methodological improvement for clinical group studies. These research tools may help to improve our understanding of disease-specific mechanisms and may help to identify clinically relevant subgroups of the heterogeneous entity schizophrenia.

KEYWORDS:

PET imaging; aberrant salience; computational modeling; dopamine; fMRI; prediction error; reinforcement learning; schizophrenia

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