The recognition that computational ideas from reinforcement learning are relevant to the study of neural circuits has taken the cognitive neuroscience community by storm. A central tenet of these models is that discrepancies between actual and expected outcomes can be used for learning. Neural correlates of such prediction-error signals have been observed now in midbrain dopaminergic neurons, striatum, amygdala and even prefrontal cortex, and models incorporating prediction errors have been invoked to explain complex phenomena such as the transition from goal-directed to habitual behavior. Yet, like any revolution, the fast-paced progress has left an uneven understanding in its wake. Here, we provide answers to ten simple questions about prediction errors, with the aim of exposing both the strengths and the limitations of this active area of neuroscience research.