When we evaluate our sensory evidence to make decisions, we also evaluate its quality so that we can judge how like we are to make correct inferences about it — that is, we judge our perceptual confidence. This is something that we want our artificial systems to be able to do as well, of course. One might think that an optimal inference strategy would be the obvious choice for the nervous system to evaluate its own sensory noise. But is this what the brain is doing? And when we say ‘optimal’, are we making a correct guess at what the cost function ought to be? In this talk I’ll present some evidence to suggest both how we can go about answering these difficult questions, and that the answer might be that the brain is evaluating its own sensory noise in ways that might seem surprising. I’ll close with some implications that these findings may have for our design of intelligent artificial agents.