Posterior parietal cortex encodes errors in a task-dependent manner while a large array of frontal regions predict subsequent behavioral changes in response to ambiguous errors.

Training enables adult humans to rapidly adapt their sound localization abilities to unilateral hearing loss by combining different strategies that rely on partially distinct neurophysiological substrates.

Computational modeling offers an explanation for why animals learn more quickly or slowly when their environment becomes more variable or stable.

Learning produces dynamic white matter volumetric changes in the rodent brain observed by voxel-based morphometry on longitudinal structural MRI for which estimated white matter volume reflects adaptive myelination in the cortex.

A novel approach based on eye-movement patterns reveals adaptive changes during viewing of faces and fear generalization.

The brain continuously updates the learned temporal relationship between motor commands and their associated somatosensory feedback, which determines the perceived intensity and ticklishness of self-touch.

Individual and situational differences in altruistic behavior do not reflect use of fundamentally different decision mechanisms, but instead differences in early perceptual or attentional processing of the choice-relevant information.

Convolutional neural networks are an effective tool for detecting archaic adaptive introgression from genomic data.

A novel, non-auditory learning paradigm reveals that songbirds can modify their vocal output based on somatosensory signals and that a common set of brain pathways underlies both this form of vocal learning and auditory-guided vocal learning.

A computational model of the opponent neural architecture the basal ganglia, in tandem with adaptive dopamine modulation, exhibits robust advantages over traditional learning algorithms and ties together seemingly aberrant behavioral patterns resulting from dopamine and environmental manipulations across species.