Recent advancements in cognitive neuroscience have afforded a description of neural
responses in terms of latent algorithmic operations. However, the adoption of this approach …

The outcome of a decision is often uncertain, and outcomes can vary over repeated
decisions. Whether decision outcomes should substantially affect behaviour and learning …

INTRODUCTION The drive to seek and experience reward is conserved across species and,
in mammals, involves interactions between subcortical dopaminergic systems and limbic …

Brain dopamine (DA) has long been implicated in cognitive control processes, including
working memory. However, the precise role of DA in cognition is not well-understood, partly …

The ability to make inferences about the current state of a dynamic process requires ongoing
assessments of the stability and reliability of data generated by that process. We found that …

One of the most salient attributes of information is valence: whether a piece of news is good
or bad. Contrary to classic learning theories, which implicitly assume beliefs are adjusted …

Computational learning models are critical for understanding mechanisms of adaptive
behavior. However, the two major current frameworks, reinforcement learning (RL) and …

Current reinforcement-learning models often assume simplified decision processes that do
not fully reflect the dynamic complexities of choice processes. Conversely, sequential …

Regulating how fast to learn is critical for flexible behavior. Learning about the
consequences of actions should be slow in stable environments, but accelerate when that …

The panoply of cognitive, affective, motivational, and social functions that underpin everyday
human experience requires precisely choreographed patterns of interaction between …