Minimizing precision-weighted sensory prediction errors via memory formation and switching in motor adaptation

Abstract

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 attributethis errorto a change in our body, and updatethe 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 proposethat a decision-making process comparesthe models' prediction errors, weighted bytheir 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, boththe prediction's mean estimate and uncertainty are updatedto minimizefuture 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.