Memory over 24 hours was impaired in Parkinson's patients off, rather than on, dopaminergic medication during reinforcement learning, whereas dopamine did not affect positive and negative reinforcement, in contrast to previous studies.

The human brain is capable of implementing inverse reinforcement learning, where an observer infers the hidden reward structure of a decision problem solely through observing another individual take actions.

Computational modeling suggests that feedback between striatal cholinergic neurons and spiny neurons dynamically adjusts learning rates to optimize behavior in a variable world.

A mathematical model built around the assumption that the desire to maintain internal homeostasis drives the behavior of animals, by affecting their learning processes, can explain many real-world behaviors, including some that might otherwise appear irrational.

Patients with DYT1 dystonia show aberrant risk-aversion in a simple decision-making task, in accordance with predictions of a reinforcement learning model of corticostriatal trial-and-error learning.

Neural confidence signals can take the role of reward signals and explain perceptual learning without external feedback as a form of internal reinforcement learning.

The finding that fly maggots, equipped with only 10,000 neurons, process reinforcement not only by value but also by specific quality reveals a basic operating principle of brains and challenges current models of memory organization.

Individual differences in generalization of aversive value (but not safety information) during human active avoidance learning specifically predict experience of anxiety and intrusive thoughts.

Veterans with PTSD show increased attention to a history of unexpected outcomes during loss learning, both as measured by computational model-derived behavioral parameters and in increased neural signaling in amygdala and insula.

A two-part neural network models reward-based training and provides a unified framework in which to study diverse computations that can be compared to electrophysiological recordings from behaving animals.