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.

Modern machine learning methods offer new techniques for analyzing complex vocal behavior like ultrasonic mouse calls and birdsong.

Evidence for a selective sensorimotor brain mechanism responsible for translating sensory numerical information into the number of actions in a sequence.

A whole brain analysis using human neuroimaging data shows neural correlates of individual differences in conditioned fear.

Cocktail-party listening performance in normal-hearing listeners is associated with the ability to focus attention on a target stimulus in the presence of distractors.

Tailoring instructive experience to individual genetic biases improves vocal learning outcomes across genetic backgrounds in the Bengalese finch.

Multi-modal structural data fusion questions the specificity of fMRI-behavior associations by providing strong evidence relating human brain structure to a wide range of behavioral measures previously associated to functional connectivity.

Integration of automatic behavioral tracking with brain molecular profiling reveals the role of gene regulatory network plasticity in the regulation of behavioral phenotypes.

Individual differences in intelligence are based on information processing that is embedded in fine-scale structure in cortical systems for self-generated thought, which was revealed by resolving topographic idiosyncrasies with hyperalignment.

Responses to anesthetics differ among individuals and fluctuate stochastically despite constant drug concentration, however, the amount of noise driving transitions between the responsive and the unresponsive state is conserved.