Humans and recurrent neural networks both allocate working memory resources to maximize utility, shaped by natural stimulus statistics and learned stimulus-reward association.

First framework integrates scalp and intracranial EEG to estimate whole-brain networks via state-space models and EM algorithm, outperforming traditional methods and showing cortical-subcortical flows in working memory.