The article "Principles of Sensorimotor Learning" by Daniel M. Wolpert, Jörn Diedrichsen, and J. Randall Flanagan reviews recent research on human motor learning, focusing on the computational mechanisms involved. Motor learning involves several interacting elements, including efficient sensory information gathering, decision-making, and the implementation of predictive and reactive control mechanisms. The authors discuss the role of error-based learning, reinforcement learning, and use-dependent learning in motor adaptation. They also explore the concept of motor primitives, which are neural control modules that can be combined to generate a wide range of behaviors. The article delves into the process of credit assignment, where errors are attributed to different sources, and the role of contextual and temporal cues in learning. Additionally, it examines the structure of motor representations and how they influence learning and generalization. The authors highlight the importance of Bayesian inference in improving sensory information accuracy and the role of the cerebellum in fast trial-by-trial error-based learning. The article concludes by discussing the neural correlates of motor learning and the potential for observational learning through the observation of others.The article "Principles of Sensorimotor Learning" by Daniel M. Wolpert, Jörn Diedrichsen, and J. Randall Flanagan reviews recent research on human motor learning, focusing on the computational mechanisms involved. Motor learning involves several interacting elements, including efficient sensory information gathering, decision-making, and the implementation of predictive and reactive control mechanisms. The authors discuss the role of error-based learning, reinforcement learning, and use-dependent learning in motor adaptation. They also explore the concept of motor primitives, which are neural control modules that can be combined to generate a wide range of behaviors. The article delves into the process of credit assignment, where errors are attributed to different sources, and the role of contextual and temporal cues in learning. Additionally, it examines the structure of motor representations and how they influence learning and generalization. The authors highlight the importance of Bayesian inference in improving sensory information accuracy and the role of the cerebellum in fast trial-by-trial error-based learning. The article concludes by discussing the neural correlates of motor learning and the potential for observational learning through the observation of others.