From a computational perspective, the act of using a tool and making a movement involves solving three kinds of problems: we need to learn the costs that are associated with our actions as well as the rewards that we may experience at various sensory states. We need to learn how our motor commands produce changes in things that we can sense. Finally, we must learn how to actually produce the motor commands that are needed so that we minimize the costs and maximize the rewards. The various computational problems appear to require different kinds of error signals that guide their learning, and might rely on different kinds of contextual cues that allow their recall. Indeed, there may be different neural structures that compute these functions. Here we use this computational framework to review the motor control capabilities of two important patients who have been studied extensively from the neuropsychological perspective: HM, who suffered from severe amnesia; and BG, who suffered from apraxia. When viewed from a computational perspective, the capabilities and deficits of these patients provide insights into the neural basis of our ability to willfully move our limbs and interact with the objects around us.