We review the current state of knowledge on the computational and neural mechanisms of
reinforcement-learning with a particular focus on fronto-striatal circuits. We divide the …

Accumulating evidence shows that the neural network of the cerebral cortex and the basal
ganglia is critically involved in reinforcement learning. Recent studies found functional …

In recent years, ideas from the computational field of reinforcement learning have
revolutionized the study of learning in the brain, famously providing new, precise theories of …

Reinforcement learning is ubiquitous. Unlike other forms of learning, it involves the
processing of fast yet content-poor feedback information to correct assumptions about the …

A central problem in cognitive neuroscience is how animals can manage to rapidly master
complex sensorimotor tasks when the only sensory feedback they use to improve their …

Highlights•Reinforcement-learning algorithms are a useful framework for investigating the
neurobiology of action selection for reward.•Multiple approaches have been taken in the …

The field of reinforcement learning has greatly influenced the neuroscientific study of
conditioning. This article provides an introduction to reinforcement learning followed by an …

Growing evidence suggests that the prefrontal cortex (PFC) is organized hierarchically, with
more anterior regions having increasingly abstract representations. How does this …

Reinforcement learning is the process by which an agent learns to predict long-term future
reward. We now understand a great deal about the brain's reinforcement learning …

Reinforcement learning algorithms have provided some of the most influential computational
theories for behavioral learning that depends on reward and penalty. After briefly reviewing …