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Probabilistic Reinforcement Learning in Patients With Schizophrenia: Relationships to Anhedonia and Avolition
Erin C Dowd1, Michael J Frank2, Anne Collins3
1Division of Biology and Biomedical Sciences, Neuroscience Program, Washington University in St. Louis.
Background:
Anhedonia (a reduced experience of pleasure) and avolition (a reduction in goal-directed activity) are common features of schizophrenia that have substantial effects on functional outcome, but are poorly understood and treated. Here, we examined whether alterations in reinforcement learning may contribute to these symptoms in schizophrenia by impairing the translation of reward information into goal-directed action.
Methods:
38 stable outpatients with schizophrenia or schizoaffective disorder and 37 healthy controls underwent fMRI during a probabilistic stimulus selection reinforcement learning task with dissociated choice- and feedback-related activation, followed by a behavioral transfer task allowing separate assessment of learning from positive versus negative outcomes. A Q-learning algorithm was used to examine functional activation relating to prediction error at the time of feedback and to expected value at the time of choice.
Results:
Behavioral results suggested a reduction in learning from positive feedback in patients; however, this reduction was unrelated to anhedonia/avolition severity. On fMRI analysis, prediction error-related activation at the time of feedback was highly similar between patients and controls. During early learning, patients activated regions in the cognitive control network to a lesser extent than controls. Correlation analyses revealed reduced responses to positive feedback in dorsolateral prefrontal cortex and caudate among those patients higher in anhedonia/avolition.
Conclusions:
Together, these results suggest that anhedonia/avolition are as strongly related to cortical learning or higher-level processes involved in goal-directed behavior such as effort computation and planning as to striatally mediated learning mechanisms.
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