Emotional learning retroactively promotes memory integration for previously neutral events into episodic memory to foster future event predictions, through rapidly stimulating trial-specific reactivation of overlapping memory traces and reorganization of associated memories among the amygdala, hippocampal, and neocortical circuits.

A series of experiments shows how muscle fatigue impairs motor-skill learning on subsequent practice days beyond its effects on task execution.

Through mathematical analysis and computer simulations, the neuronal underpinnings of feasible models of sequence learning are delineated.

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

A comparison of different bioimage analysis pipelines reveals how deep learning can be used for automatized and reliable analysis of fluorescent features in biological datasets.

When a common error drives parallel implicit and explicit learning systems, increases in the explicit response will suppress implicit adaptation.

A domain-general structure learning mechanism, supported by anterior insula, moves beyond explicit category labels and dyadic similarity as the sole inputs to social group representations and predicts ally-choice behavior.

Relative to children and adults, adolescents placed greater weight on negative prediction errors during learning and these age-varying learning idiosyncrasies biased subsequent memory for information associated with valenced outcomes.

The P300, an electroencephalography (EEG) component known to be evoked by surprising events, predicts learning in a bidirectional manner that depends critically on the surrounding statistical context.

State anxiety alters the dynamics of beta oscillations during reward-dependent motor learning, thereby impairing proper updating of motor predictions when learning in unstable environments.