Recent theories of voluntary control predict that multiple motor strategies can be precomputed and expressed throughout movement. We examined online decisional processing in humans by asking them to make reaching movements with obstacles located just to the sides of a direct path between start and end targets. On random trials, the limb was perturbed with one of four mechanical loads that varied in direction and amplitude. Notably, we observed two different strategies when we applied a perturbation (left medium-sized) that deviated the participants' hand directly toward an obstacle. In some trials, subjects directed their hand between the obstacles and in other trials to the left of the obstacles. Importantly, changes in the muscle stretch response between these two strategies were observed in <60 ms after perturbation, during the R2 long-latency epoch (∼45–75 ms). As predicted, the selected strategy depended on the estimated position of the limb when it was perturbed. In our second experiment, we presented either one or three potential goal targets. Movements initially directed to the closest target could be quickly redirected to other potential targets after a perturbation. Differences in muscle stretch responses for redirected movements were observed ∼75 ms after perturbation during the R3 long-latency epoch (∼75–105 ms). The results show that decisional processes are rapidly implemented during movement execution. In addition, our data suggest a hierarchical process with corrective responses on “how” to attain a behavioral goal expressed during the R2 epoch and responses on “what” goal to attain during the R3 epoch.

Recent advances in movement neuroscience have consistently highlighted that the nervous system performs sophisticated feedback control over very short time scales (<100 ms for upper limb). These observations raise the important question of how the nervous system processes multiple sources of sensory feedback in such short time intervals, given that temporal delays across sensory systems such as vision and proprioception differ by tens of milliseconds. Here we show that during feedback control, healthy humans use dynamic estimates of hand motion that rely almost exclusively on limb afferent feedback even when visual information about limb motion is available. We demonstrate that such reliance on the fastest sensory signal during movement is compatible with dynamic Bayesian estimation. These results suggest that the nervous system considers not only sensory variances but also temporal delays to perform optimal multisensory integration and feedback control in real-time. SIGNIFICANCE STATEMENT Numerous studies have demonstrated that the nervous system combines redundant sensory signals according to their reliability. Although very powerful, this model does not consider how temporal delays may impact sensory reliability, which is an important issue for feedback control because different sensory systems are affected by different temporal delays. Here we show that the brain considers not only sensory variability but also temporal delays when integrating vision and proprioception following mechanical perturbations applied to the upper limb. Compatible with dynamic Bayesian estimation, our results unravel the importance of proprioception for feedback control as a consequence of the shorter temporal delays associated with this sensory modality.