Model specification.
pain
Models 1–4 were defined using different combinations of parameters for reward sensitivity and the mapping of expected values to the drift rate. A ‘static’ reward sensitivity means that pain increase and pain decrease were defined as –1 and 1, respectively (see We used hierarchical Bayesian modeling to fit the reward learning models to the choices of the participants in the test trials. Hierarchical models estimate group and individual parameters simultaneously to mutually inform and constrain each other, which yields reliable estimates for both, individual and group level parameters (The fitted models were compared for their best predictive accuracy using For the best fitting model, we performed posterior predictive checks by simulating replicated data sets from posterior draws. As the test statistic for the posterior predictive check, we examined the proportion of choices in favor of the option associated with a higher chance to win pain relief (From the best fitting model, we used group level estimates for the main effect of ‘drug’ to compare model parameters between drug conditions using the 95% highest density interval (HDI) of the difference of their posterior distributions.The means of individual parameter posterior distributions were used to estimate prediction errors for single trials. To test whether these prediction errors predict endogenous pain modulation induced by the wheel of fortune task, we used linear mixed models with the fixed factors ‘prediction error’ and ‘drug’, and their interaction. A random intercept for each subject was included to account for repeated measures. Separate models for VAS ratings and behaviorally assessed pain perception as dependent variables were calculated.