  It is inconceivable how chaotic the world would look to humans, faced with
innumerable decisions a day to be made under uncertainty, had they been lacking
the capacity to distinguish the relevant from the irrelevant---a capacity which
computationally amounts to handling probabilistic independence relations. The
highly parallel and distributed computational machinery of the brain suggests
that a satisfying process-level account of human independence judgment should
also mimic these features. In this work, we present the first rational,
distributed, message-passing, process-level account of independence judgment,
called $\mathcal{D}^\ast$. Interestingly, $\mathcal{D}^\ast$ shows a curious,
but normatively-justified tendency for quick detection of dependencies,
whenever they hold. Furthermore, $\mathcal{D}^\ast$ outperforms all the
previously proposed algorithms in the AI literature in terms of worst-case
running time, and a salient aspect of it is supported by recent work in
neuroscience investigating possible implementations of Bayes nets at the neural
level. $\mathcal{D}^\ast$ nicely exemplifies how the pursuit of cognitive
plausibility can lead to the discovery of state-of-the-art algorithms with
appealing properties, and its simplicity makes $\mathcal{D}^\ast$ potentially a
good candidate for pedagogical purposes.
