Learning from the path not taken: Sensory prediction error computation and implicit motor adaptation proceed without movement

Abstract

ABSTRACTPrediction errors guide many forms of learning, providing teaching signals that help us improve our performance. Implicit motor adaptation, for instance, is driven by sensory prediction errors (SPEs), which occur when the expected and observed consequences of a movement differ. Traditionally, SPE computation is thought to require movement execution. However, recent work suggesting that the brain generates and accounts for sensory predictions based on motor imagery or planning alone calls this assumption into question. Here, by measuring implicit adaptation during a visuomotor task, we tested whether motor planning and well-timed sensory feedback are sufficient for SPE computation. Human participants were cued to reach to a target and were, on a subset of trials, rapidly cued to withhold these movements. Errors displayed both on trials with and without movements induced single-trial implicit learning. Learning following trials without movements persisted even when movement trials had never been paired with errors, and when the direction of movement and sensory feedback trajectories were decoupled. These observations demonstrate that the brain can compute SPEs without generating overt movements, leading to the adaptation of planned movements even when they are not performed.HIGHLIGHTSThe brain can learn to update movements that are not performed, representing a mechanism for learning based only on movement planning and sensory expectation.Supports a fundamental role for prediction in adaptation.Provides further support for the role of forward models in predictive motor control.