Despite increasing interest in the role of reward in motor learning, the underlying mechanisms remain ill defined. In particular, the contribution of explicit processes to reward-based motor learning is unclear. To address this, we examined subjects' ( n = 30) ability to learn to compensate for a gradually introduced 25° visuomotor rotation with only reward-based feedback (binary success/failure). Only two-thirds of subjects ( n = 20) were successful at the maximum angle. The remaining subjects initially followed the rotation but after a variable number of trials began to reach at an insufficiently large angle and subsequently returned to near-baseline performance ( n = 10). Furthermore, those who were successful accomplished this via a large explicit component, evidenced by a reduction in reach angle when they were asked to remove any strategy they employed. However, both groups displayed a small degree of remaining retention even after the removal of this explicit component. All subjects made greater and more variable changes in reach angle after incorrect (unrewarded) trials. However, subjects who failed to learn showed decreased sensitivity to errors, even in the initial period in which they followed the rotation, a pattern previously found in parkinsonian patients. In a second experiment, the addition of a secondary mental rotation task completely abolished learning ( n = 10), while a control group replicated the results of the first experiment ( n = 10). These results emphasize a pivotal role of explicit processes during reinforcement-based motor learning, and the susceptibility of this form of learning to disruption has important implications for its potential therapeutic benefits. NEW & NOTEWORTHY We demonstrate that learning a visuomotor rotation with only reward-based feedback is principally accomplished via the development of a large explicit component. Furthermore, this form of learning is susceptible to disruption with a secondary task. The results suggest that future experiments utilizing reward-based feedback should aim to dissect the roles of implicit and explicit reinforcement learning systems. Therapeutic motor learning approaches based on reward should be aware of the sensitivity to disruption.
The motor system’s ability to adapt to environmental changes is essential for maintaining accurate movements. Such adaptation recruits several distinct systems: cerebellar sensory-prediction error learning, success-based reinforcement, and explicit control. Although much work has focused on the relationship between cerebellar learning and explicit control, there is little research regarding how reinforcement and explicit control interact. To address this, participants first learnt a 20° visuomotor displacement. After reaching asymptotic performance, binary, hit-or-miss feedback (BF) was introduced either with or without visual feedback, the latter promoting reinforcement. Subsequently, retention was assessed using no-feedback trials, with half of the participants in each group being instructed to stop aiming off target. Although BF led to an increase in retention of the visuomotor displacement, instructing participants to stop re-aiming nullified this effect, suggesting explicit control is critical to BF-based reinforcement. In a second experiment, we prevented the expression or development of explicit control during BF performance, by either constraining participants to a short preparation time (expression) or by introducing the displacement gradually (development). Both manipulations strongly impaired BF performance, suggesting reinforcement requires both recruitment and expression of an explicit component. These results emphasise the pivotal role explicit control plays in reinforcement-based motor learning.
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Reward has a remarkable ability to invigorate motor behaviour, enabling individuals to select 11 and execute actions with greater precision and speed. However, if reward is to be exploited 12 in applied settings such as rehabilitation, a thorough understanding of its underlying mech-13 anisms is required. Although reward-driven enhancement of movement execution has been 14 proposed to occur through enhanced feedback control, an untested alternative is that it is 15 driven by increased arm stiffness, an energy-consuming process that increases limb stability. 16First, we demonstrate that during reaching reward improves selection and execution per-17 formance concomitantly without interference. Computational analysis revealed that reward 18 led to both an increase in feedback correction during movement and a reduction in mo-19 tor noise near the target. We provide novel evidence that this noise reduction is driven by a 20 reward-dependent increase in arm stiffness. Therefore, reward drives multiple error-reduction 21 mechanisms which enable individuals to invigorate motor performance without compromising 22 accuracy. 23 24 1
A wealth of evidence describes the strong positive impact that reward has on motor control at the behavioural level. However, surprisingly little is known regarding the neural mechanisms which underpin these effects, beyond a reliance on the dopaminergic system. In recent work, we developed a task that enabled the dissociation of the selection and execution components of an upper limb reaching movement. Our results demonstrated that both selection and execution are concommitently enhanced by immediate reward availability. Here, we investigate what the neural underpinnings of each component may be. To this end, we aimed to alter the cortical excitability of the ventromedial prefrontal cortex and supplementary motor area using continuous theta-burst transcranial magnetic stimulation (cTBS) in a within-participant design (N = 23). Both cortical areas are involved in determining an individual's sensitivity to reward and physical effort, and we hypothesised that a change in excitability would result in the reward-driven effects on action selection and execution to be altered, respectively. To increase statistical power, participants were pre-selected based on their sensitivity to reward in the reaching task. While reward did lead to enhanced performance during the cTBS sessions and a control sham session, cTBS was ineffective in altering these effects. These results may provide evidence that other areas, such as the primary motor cortex or the premotor area, may drive the reward-based enhancements of motor performance.
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