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Modeling Reward Learning Under Placebo Expectancies: A Q-Learning Approach

Abstract

Although expectancy effects induced by placebo treatment are reported to attenuate depressive symptoms in the long run, mechanisms underlying situational dynamics are not well understood. Improved reward learning has been discussed as a candidate mediator for effects of positive expectancies on more positive mood. Here, we fitted a series of Q-learning models to measure the effect of sham antidepressant treatment vs. open-label placebo in a probabilistic reinforcement learning task. Treatment effects were observed mainly in those Q-learning models justified by the task structure. Additionally, interindividual variability remained the largest origin of unexplained variance in predictive match across models. These findings provide further support for the role of expectancies in reward learning. They also highlight the need for unraveling individual differences in cognitive mechanisms that account for differences in reward learning, and obtaining reliable estimates for them.

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