Minimizing Precision-Weighted Sensory Prediction Errors via Memory Formation and Switching in Motor Adaptation.

Humans predict the sensory consequences of motor commands by learning internal models of the body and of environment perturbations. When facing a sensory prediction error, should we attribute this error to a change in our body, and update the body internal model, or to a change in the environment? In the latter case, should we update an existing perturbation model or create a new model? Here, we propose that a decision-making process compares the models' prediction errors, weighted by their precisions, to select and update either the body model or an existing perturbation model. When no model can predict a perturbation, a new perturbation model is created and selected. When a model is selected, both the prediction's mean estimate and uncertainty are updated to minimize future prediction errors and to increase the precision of the predictions. Results from computer simulations, which we verified in an arm visuomotor adaptation experiment with subjects of both sexes, account for short aftereffects and large savings after adaptation to large, but not small, perturbations. Results also clarify previous data in the absence of errors (error-clamp): motor memories show an initial lack of decay after a large perturbation, but gradual decay after a small perturbation. Finally, qualitative individual differences in adaptation were explained by subjects selecting and updating either the body model or a perturbation model. Our results suggest that motor adaptation belongs to a general class of learning according to which memories are created when no existing memories can predict sensory data accurately and precisely.SIGNIFICANCE STATEMENT When movements are followed by unexpected outcomes, such as following the introduction of a visuomotor or a force field perturbation, or the sudden removal of such perturbations, it is unclear whether the CNS updates existing memories or creates new memories. Here, we propose a novel model of adaptation, and investigate, via computer simulations and behavioral experiments, how the amplitude and schedule of the perturbation, as well as the characteristics of the learner, lead to the selection and update of existing memories or the creation of new memories. Our results provide insights into a number of puzzling and contradictory motor adaptation data, as well as into qualitative individual differences in adaptation.