Using prediction errors to drive saccade adaptation: the implicit double-step task

A prediction-based error signal, neurally computed as the difference between predicted and observed movement outcomes, has been proposed as the driving force for motor learning. This suggests that the generation of predictive saccades to periodically paced targets—whose performance accuracy is actively maintained using this same error signal—invokes the motor-learning network. We examined whether a simple predictive-saccade task (implicit double-step adaptation, in which targets are gradually displaced outward to exaggerate normal hypometric movement errors) can stand in place of a traditional double-step saccade-adaptation task to induce an increase in saccade gain. We find that the implicit double-step adaptation task can induce significant gain-increase adaptation (of comparable magnitude to that of the standard double-step task) in normal control subjects. Unlike control subjects, patients with impaired cerebella are unable to adapt their saccades in response to this paradigm; this implies that the cerebellum is crucial for processing prediction-based error signals for motor learning.