A novel method and software provides researchers with the capability to rapidly, flexibly, and robustly perform Bayesian parameter estimation of theoretically meaningful models in cognitive neuroscience that were heretofore intractable.
Arbitration is formalised as the relative precision of predictions afforded by reward and social learning systems and is represented in modality-specific dopaminergic and dopaminoceptive regions, including the midbrain and amygdala.
Integrating over multiple forms of statistical uncertainty associated with serological surveys can improve serosurvey design while also enabling that uncertainty to be appropriately propagated through epidemiological models.
Hierarchical modeling of internalizing symptoms and task performance reveals that difficulty adapting probabilistic learning to second-order uncertainty is common to anxiety and depression and holds across rewarding and punishing outcomes.
A human psychopharmacology study reveals that a drug that affects the dopamine and noradrenaline systems enhances people's ability to adapt their learning rate to suit the volatility of the environment.