Principles of sensorimotor learning

Key Points Learning movement skills involves a number of interacting components, such as information extraction, decision making, different classes of control, motor learning and its representations. Skilled performance requires the effective and efficient gathering and processing of sensory information that is relevant to an action. Decision-making processes involve determining what information to extract during the unfolding task and, based on this information, when to make the next movement and which movement to make. Classes of control used to optimize motor performance include predictive, reactive and biomechanical control. Processes of motor learning can be distinguished by the types of information that the motor system uses as a learning signal. These include error-based learning, reinforcement learning, observational learning and use-dependent learning. Representations in motor learning reflect the internal assumptions about the task structure and constrain the way in which learning occurs in response to errors. Such representations can be conceptualized in two ways, either as mechanistic or normative models. Acquiring new motor skills involves a range of learning processes that are related to the gathering of task-relevant sensory information, decision making and the selection of strategies. Wolpert and colleagues review recent research in human motor learning with an emphasis on the computational mechanisms that are involved. The exploits of Martina Navratilova and Roger Federer represent the pinnacle of motor learning. However, when considering the range and complexity of the processes that are involved in motor learning, even the mere mortals among us exhibit abilities that are impressive. We exercise these abilities when taking up new activities — whether it is snowboarding or ballroom dancing — but also engage in substantial motor learning on a daily basis as we adapt to changes in our environment, manipulate new objects and refine existing skills. Here we review recent research in human motor learning with an emphasis on the computational mechanisms that are involved.