Hierarchical modeling of internalizing symptoms and task performance reveals that difficulty adapting probabilistic learning to second-order uncertainty is common to anxiety and depression and holds across rewarding and punishing outcomes.

A near-linear relationship between electrical stimulus duration and resultant joint torque in stick insects has been unveiled, enhancing control strategies in biohybrid robotics.

The dependence of incentive value attribution on the anticipation of rewards within a given context is explained via a normative Bayesian account of how rewards map to incentive values.

Psychophysical measurement and computational modeling show that sensory information cannot contribute directly to a cognitive judgment, but must first be integrated into resource-limited working memory.

A novel method and software provides researchers with the capability to rapidly, flexibly, and robustly perform Bayesian parameter estimation of theoretically meaningful models in cognitive neuroscience that were heretofore intractable.

Receptive fields for time in the hippocampus, like receptive fields for retinal space and receptive fields for numerosity, obey the Weber-Fechner law.

With a data-driven mathematical modelling approach, within-host ecological information alone can provide clues on the mechanistic basis of diverse malaria infection outcomes.

Keeping flexible adaptable representations of speech categories at different time scales allows the brain to maintain stable perception in the face of varying speech sound characteristics.

Applying computational modeling to quantify threat learning processes uncovers how variations in these conserved learning processes relate to anxiety severity and the neuroanatomical substrates moderating these associations.

Pharmacological fMRI study provides evidence that learning from the unexpected presence and absence of pain is mediated by different brain systems.