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

We propose a stimulus-wise decomposition of the mutual information that is (1) principled (axiomatic justification), (2) tractable (estimated via diffusion models), and then we demonstrate it's application on a model of visual neurons.