Abstract
The extent to which motor learning is generalized across the limbs is typically very limited. Here, we investigated how two motor learning hypotheses could be used to enhance the extent of interlimb transfer. According to one hypothesis, we predicted that reinforcement of successful actions by providing binary error feedback regarding task success or failure, in addition to terminal error feedback, during initial training would increase the extent of interlimb transfer following visuomotor adaptation (experiment 1). According to the other hypothesis, we predicted that performing a reaching task repeatedly with one arm without providing performance feedback (which prevented learning the task with this arm), while concurrently adapting to a visuomotor rotation with the other arm, would increase the extent of transfer (experiment 2). Results indicate that providing binary error feedback, compared with continuous visual feedback that provided movement direction and amplitude information, had no influence on the extent of transfer. In contrast, repeatedly performing (but not learning) a specific task with one arm while visuomotor adaptation occurred with the other arm led to nearly complete transfer. This suggests that the absence of motor instances associated with specific effectors and task conditions is the major reason for limited interlimb transfer and that reinforcement of successful actions during initial training is not beneficial for interlimb transfer. These findings indicate crucial contributions of effector- and task-specific motor instances, which are thought to underlie (a type of) model-free learning, to optimal motor learning and interlimb transfer.