Vul et al. (2014) analyzed an abstract computational architecture whose elementary operation was to sample from the posterior distribution on whether or not an action will yield a reward. The analysis found that as the cost of sampling increases, the resource-rational algorithm changes from expected utility maximization (i.e., infinitely many samples) to probability matching (i.e., using a single sample). For a wide range of realistic computational costs, the optimal number of samples was surprisingly close to one, indicating that—in contrast to expected-utility theory—resource-rationality is consistent with probability matching (Herrnstein & Loveland, 1975)