Bayesian models in cognitive science and artificial intelligence operate over domains such as vision, motor control and language processing by sampling from vastly complex probability distributions.

Such models cannot, and typically do not need to, calculate explicit probabilities.

Sampling naturally generates a variety of systematic probabilistic reasoning errors on elementary probability problems, which are observed in experiments with people.

Thus, it is possible to reconcile probabilistic models of cognitive and brain function with the human struggle to master even the most elementary explicit probabilistic reasoning.