Abstract
Studies on generalization of learned visuomotor perturbations has generally focused on whether learning is coded in extrinsic or intrinsic reference frames. This dichotomy, however, is challenged by recent findings showing that learning is represented in a mixed reference frame. Overlooked in this framework is how learning is the result of multiple processes, such as explicit re-aiming and implicit motor adaptation. Therefore the proposed mixed representation may simply reflect the superposition of explicit and implicit generalization functions, each represented in different reference frames. Here, we characterized the individual generalization functions of explicit and implicit learning in relative isolation to determine if their combination could predict the overall generalization function when both processes are in operation. We modified the form of feedback in a visuomotor rotation task to isolate explicit and implicit learning, and tested generalization across different limb postures to dissociate the extrinsic and intrinsic representations. We found that explicit generalization occurred predominantly in an extrinsic reference frame but the amplitude was reduced with postural changes, whereas implicit generalization was phase-shifted according to a mixed reference frame representation and amplitude was maintained. A linear combination of individual explicit and implicit generalization functions accounted for nearly 85% of the variance associated with the generalization function in a typical visuomotor rotation task, where both processes are in operation. This suggests that each form of learning results from a mixed representation with distinct extrinsic and intrinsic contributions, and the combination of these features shape the generalization pattern observed at novel limb postures.
New and noteworthy Generalization following learning in visuomotor adaptation tasks can reflect how the brain represents what it learns. In this study, we isolated explicit and implicit forms of learning, and showed that they are derived from a mixed reference frame representation with distinct extrinsic and intrinsic contributions. Furthermore, we showed that the overall generalization pattern at novel workspaces is due to the superposition of independent generalization effects developed by explicit and implicit learning processes.
Acknowledgements
We would like to thank Carlo Campagnoli for helpful discussions and comments on the manuscript. This work was supported by the National Institute of Neurological Disorders and Stroke (Grant R01 NS-084948) and the National Science Foundation under Grant No. 1838462.