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.
Highlights
AND OUTLINE The “dopamine-hypothesis” of schizophrenia was initially built upon the observation that dopamine receptor antagonists, such as haloperidol, attenuate psychotic symptoms [1]
Thereby, we suggest how reinforcement learning abnormalities in schizophrenia may contribute to the formation of psychotic symptoms and may interact with cognitive deficits
Evidence showing that elevated dopamine levels are involved in the pathophysiology of psychotic symptoms and schizophrenia is primarily derived from neurochemical studies using positronemission-tomography (PET) with radioactive ligands targeting the brain’s dopamine system
Summary
AND OUTLINE The “dopamine-hypothesis” of schizophrenia was initially built upon the observation that dopamine receptor antagonists, such as haloperidol, attenuate psychotic symptoms [1]. Differences in the perceived properties of feedback stimuli per se (e.g., shifts in hedonic experience or salience) may influence the elicitation of prediction errors and potentially corrupt learning processes Based on these two main time points, we will proceed with a brief summary of two influential hypotheses with respect to the potential contribution of reinforcement learning to symptom dimensions and disease-specific features in schizophrenia. This hypothesis posits that prediction errors are not adequately used to learn values even though hedonic experience itself remains mainly intact This concept relates closely to the idea that reward feedback is not adequately transformed into motivational drive for goal-directed behavior [51] and has been proposed as a potential mechanism for the origin of negative symptoms [11]. In schizophrenia, such a shift may predominantly concern neutral stimuli and result in aberrant learning as pointed out in the aberrant salience hypothesis
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