Reward-Based Improvements in Motor Control Are Driven by Multiple Error-Reducing Mechanisms.

Reward has a remarkable ability to invigorate motor behavior, enabling individuals to select and execute actions with greater precision and speed. However, if reward is to be exploited in applied settings, such as rehabilitation, a thorough understanding of its underlying mechanisms is required. In a series of experiments, we first demonstrate that reward simultaneously improves the selection and execution components of a reaching movement. Specifically, reward promoted the selection of the correct action in the presence of distractors, while also improving execution through increased speed and maintenance of accuracy. These results led to a shift in the speed-accuracy functions for both selection and execution. In addition, punishment had a similar impact on action selection and execution, although it enhanced execution performance across all trials within a block, that is, its impact was noncontingent to trial value. Although the reward-driven enhancement of movement execution has been proposed to occur through enhanced feedback control, an untested possibility is that it is also driven by increased arm stiffness, an energy-consuming process that enhances limb stability. Computational analysis revealed that reward led to both an increase in feedback correction in the middle of the movement and a reduction in motor noise near the target. In line with our hypothesis, we provide novel evidence that this noise reduction is driven by a reward-dependent increase in arm stiffness. Therefore, reward drives multiple error-reduction mechanisms which enable individuals to invigorate motor performance without compromising accuracy. While reward is well-known for enhancing motor performance, how the nervous system generates these improvements is unclear. Despite recent work indicating that reward leads to enhanced feedback control, an untested possibility is that it also increases arm stiffness. We demonstrate that reward simultaneously improves the selection and execution components of a reaching movement. Furthermore, we show that punishment has a similar positive impact on performance. Importantly, by combining computational and biomechanical approaches, we show that reward leads to both improved feedback correction and an increase in stiffness. Therefore, reward drives multiple error-reduction mechanisms which enable individuals to invigorate performance without compromising accuracy. This work suggests that stiffness control plays a vital, and underappreciated, role in the reward-based imporvemenets in motor control.