Implicit adaptation is driven by direct policy updates rather than forward-model-based learning

Abstract

The human motor system can rapidly adapt its motor output in response to errors. The prevailing theory of this process posits that the motor system adapts an internal forward model that predicts the consequences of outgoing motor commands, and that this forward model is then used to guide selection of motor output. However, although there is clear evidence for the existence of adaptive forward models to help track the state of the body, there is no real evidence that such models influence the selection of motor output. A possible alternative to the forward-model-based theory of adaptation is that motor output could be directly adjusted by movement errors ("direct policy learning"), in parallel with but independent of any updates to a predictive forward model. Here, we show evidence for this latter theory based on the properties of implicit adaptation under mirror-reversed visual feedback. We show that implicit adaptation still occurs under this extreme perturbation but acts in an inappropriate direction, following a pattern consistent with direct policy learning but not forward-model-based learning. We suggest that the forward-model-based theory of adaptation needs to be re-examined and that direct policy learning is a more plausible mechanism of implicit adaptation.