This review explores the relationship between lesion studies and computational models of motor control, arguing that they can mutually inform each other. It discusses how computational models can help identify distinct motor control processes and map them onto specific deficits in patients with lesions of the corticospinal tract, cerebellum, parietal cortex, basal ganglia, and medial temporal lobe. The review explains impairments in motor control, motor learning, and higher-order motor control using computational concepts such as state estimation, optimization, prediction, cost, and reward. Key functions of the cerebellum, parietal cortex, basal ganglia, and primary/motor cortices are highlighted, emphasizing their roles in system identification, state estimation, optimal control, and transforming beliefs into motor commands. The review also examines the computational problem in reaching, providing a detailed framework for understanding how the brain solves the optimization problem in motor control. Examples from experiments are used to illustrate the application of this framework, demonstrating how it can explain complex motor behaviors and the effects of lesions on these processes. Finally, the review discusses the challenges and limitations of the computational approach, particularly in understanding the relationship between learning better sensory predictions and learning better motor commands.This review explores the relationship between lesion studies and computational models of motor control, arguing that they can mutually inform each other. It discusses how computational models can help identify distinct motor control processes and map them onto specific deficits in patients with lesions of the corticospinal tract, cerebellum, parietal cortex, basal ganglia, and medial temporal lobe. The review explains impairments in motor control, motor learning, and higher-order motor control using computational concepts such as state estimation, optimization, prediction, cost, and reward. Key functions of the cerebellum, parietal cortex, basal ganglia, and primary/motor cortices are highlighted, emphasizing their roles in system identification, state estimation, optimal control, and transforming beliefs into motor commands. The review also examines the computational problem in reaching, providing a detailed framework for understanding how the brain solves the optimization problem in motor control. Examples from experiments are used to illustrate the application of this framework, demonstrating how it can explain complex motor behaviors and the effects of lesions on these processes. Finally, the review discusses the challenges and limitations of the computational approach, particularly in understanding the relationship between learning better sensory predictions and learning better motor commands.