People overweight extreme events in decision-making and overestimate their frequency. Previous theoretical work has shown that this apparently irrational bias could result from utility-weighted sampling–a decision mechanism that makes rational use of limited computational resources (Lieder, Hsu, & Griffiths, 2014). Here, we show that utility-weighted sampling can emerge from a neurally plausible associative learning mechanism. Our model explains the over-weighting of extreme outcomes in repeated decisions from experience (Ludvig, Madan, & Spetch, 2014), as well as the overestimation of their frequency and the underlying memory biases (Madan, Ludvig, & Spetch, 2014). Our results support the conclusion that utility drives probability-weighting by biasing the neural simulation of potential consequences towards extreme values.