The dependence of incentive value attribution on the anticipation of rewards within a given context is explained via a normative Bayesian account of how rewards map to incentive values.

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.

Open data provides an opportunity to perform new analyses on preexisting data, but trade-offs are required to limit an increase in false positives.

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.

Paranoia is underwritten by variation in prior beliefs about how the world will change and how to learn from those changes.

Dopamine attenuates exploration during decision-making via a reduction of neural tracking of uncertainty.

Keeping flexible adaptable representations of speech categories at different time scales allows the brain to maintain stable perception in the face of varying speech sound characteristics.

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.