Features of tasks and environments are often represented in the brain by neural firing rates. Representations must be decoded to enable downstream actions, and decoding takes time. We describe a toy model with a Poisson process encoder and an ideal observer Bayesian decoder, and show the decoding of rate-coded signals reproduces classic patterns of response time and accuracy observed in humans, including the Hick-Hyman Law, the Power Law of Learning, speed-accuracy trade-offs, and lognormally distributed response times. The decoder is equipped with a codebook, a prior distribution over signals, and an entropy stopping threshold. We argue that historical concerns of the applicability of such information-theoretic tools to neural and behavioral data arises from a confusion about the application of discrete-time coding techniques to continuous-time signals.