Browsing by Author "Augustat, Nick"

Now showing 1 - 3 of 3

Open Access
Dopamine D2 receptor antagonism modulates antidepressant placebo effects on reward sensitivity in healthy participants
(Philipps-Universität Marburg) Augustat, Nick; Lee, Eunhwi; Ahn, Woo-Young; Chuang, Li-Ching; Endres, Dominik; Müller, Erik Malte
Content: Open code and data for the manuscript "Augustat, N., Lee, E., Ahn, W. Y., Chuang, L. C., Endres, D., Mueller, E.: Dopamine D2 receptor antagonism modulates antidepressant placebo effects on reward sensitivity in healthy participants". Abstract: "Although antidepressant placebo effects are well-known, their underlying mechanisms are not fully understood yet. Given that (i) depression has been related to aberrant dopamine signaling and (ii) placebo effects in Parkinson’s disease have been linked to dopamine, antidepressant placebo effects may likewise be driven by dopamine. More specifically, positive treatment expectations may lead to changes in dopamine and enhance the sensitivity for reward, which is frequently reduced in depression and anhedonia. The goal of this study was to test whether treatment expectations generally affect dopamine-related reward sensitivity in healthy individuals. In a double-blind 2x2 design with N=272 participants, we verbally induced antidepressant treatment expectations (high vs. low), crossed with oral intake of a highly selective dopamine D2/3 receptor antagonist (sulpiride 400mg) vs. placebo. Afterwards, participants performed a probabilistic selection task capturing reward sensitivity, presumably related to dopamine functioning. We performed an extensive computational model fitting procedure, including recovery and comparison of 74 different reinforcement learning models using a hierarchical Bayesian sampling method, to enable a reliable parameter estimation. We found that high treatment expectations increased learning rate in the winning model, which was parametrized by a learning rate, forgetting rate, and inverse temperature. While learning rate was not modulated by sulpiride intake, sulpiride specifically decreased inverse temperature under high treatment expectations. Higher learning rates reflect higher reward sensitivity, while higher inverse temperatures indicate more random choices. Our results thus provide evidence that antidepressant treatment expectations particularly affect reward sensitivity, which is partially modulated by dopamine."
Open Access
Modeling Reward Learning Under Placebo Expectancies: A Q-Learning Approach
(2022-05-10) Augustat, Nick; Müller, Erik Malte; Endres, Dominik; Chuang, Li-Ching; Panitz, Christian; Stolz, Christopher
Open Access
Uncertainty of treatment efficacy moderates placebo effects on reinforcement learning
(Philipps-Universität Marburg) Augustat, Nick; Endres, Dominik; Müller, Erik Malte
This repository includes code and data necessary for running the online survey and task, and the analyses of the manuscript "Augustat, N., Endres, D., Müller, E. M.: Uncertainty of treatment efficacy moderates placebo effects on reinforcement learning". Abstract: The placebo-reward hypothesis postulates that positive effects of treatment expectations on health (i.e., placebo effects) and reward processing share common neural underpinnings. Moreover, experiments in humans and animals indicate that reward uncertainty increases striatal dopamine, which is presumably involved in placebo responses and reward learning. Therefore, treatment uncertainty, analogously to reward uncertainty, may affect reward learning after placebo treatment. Here, we address whether different degrees of uncertainty regarding the efficacy of a sham treatment affect reward learning. In an online between-subjects experiment with N=141 participants, we systematically varied the provided efficacy instructions before participants first received a sham treatment that consisted of listening to binaural beats and then performed a probabilistic reinforcement learning task. We fitted a Q-learning model including two different learning rates for positive (gain) and negative (loss) reward prediction errors and an inverse gain parameter to behavioral decision data in the reinforcement learning task. Our results yielded an inverted-U-relationship between provided treatment efficacy probability and learning rates for gain, such that higher levels of treatment uncertainty, rather than of expected net efficacy, affect presumably dopamine-related reward learning. These findings support the placebo-reward hypothesis and suggest harnessing uncertainty in placebo treatment for recovering reward learning capabilities.