Recent research suggests that prosocial outcomes in sharing games arise from prefrontal control of self-maximizing impulses. We used continuous Theta Burst Stimulation (cTBS) to disrupt the functioning of two prefrontal areas, the right dorsolateral prefrontal cortex (DLPFC) and dorsomedial prefrontal cortex (DMPFC). We used cTBS in the right MT/V5, as a control area. We then tested subjects’ prosocial inclinations with an unsupervised Dictator Game in which they allocated real money anonymously between themselves and low and high socioeconomic status (SES) players. cTBS over the two prefrontal sites made subjects more generous compared to MT/V5. More specifically, cTBS over DLPFC increased offers to high SES players, while cTBS over DMPFC caused increased offers to low SES players. These data, the first to demonstrate an effect of disruptive neuromodulation on costly sharing, suggest that DLPFC and MPFC exert inhibitory control over prosocial inclinations during costly sharing, though they may do so in different ways. DLPFC may implement contextual control, while DMPFC may implement a tonic form of control. This study demonstrates that humans’ prepotent inclination is toward prosocial outcomes when cognitive control is reduced, even when prosocial decisions carry no strategic benefit and concerns for reputation are minimized.

Immersive, head-mounted virtual reality (HMD-VR) provides a unique opportunity to understand how changes in sensory environments affect motor learning. However, potential differences in mechanisms of motor learning and adaptation in HMD-VR versus a conventional training (CT) environment have not been extensively explored. Here, we investigated whether adaptation on a visuomotor rotation task in HMD-VR yields similar adaptation effects in CT and whether these effects are achieved through similar mechanisms. Specifically, recent work has shown that visuomotor adaptation may occur via both an implicit, error-based internal model and a more cognitive, explicit strategic component. We sought to measure both overall adaptation and balance between implicit and explicit mechanisms in HMD-VR versus CT. Twenty-four healthy individuals were placed in either HMD-VR or CT and trained on an identical visuomotor adaptation task that measured both implicit and explicit components. Our results showed that the overall timecourse of adaption was similar in both HMD-VR and CT. However, HMD-VR participants utilized a greater cognitive strategy than CT, while CT participants engaged in greater implicit learning. These results suggest that while both conditions produce similar results in overall adaptation, the mechanisms by which visuomotor adaption occurs in HMD-VR appear to be more reliant on cognitive strategies.

Modifying sensory aspects of the learning environment can influence motor behavior. While the effects of sensory manipulations on motor behavior have been widely studied, there still remains a great deal of variability across the field in terms of how sensory information has been manipulated or applied. Here, we briefly review and integrate the literature from each sensory modality to gain a better understanding of how sensory manipulations can best be used to enhance motor behavior. Then, we discuss two emerging themes from this literature that are important for translating sensory manipulation research into effective interventions. Finally, we provide future research directions that may lead to enhanced efficacy of sensory manipulations for motor learning and rehabilitation.

16Reinforcement learning enables the brain to learn optimal action selection, such as go or not go, 17 by forming state-action and action-outcome associations. Does this mechanism also optimize the 18 brain's willingness to learn, such as learn or not learn? Learning to learn by rewards, i.e., 19 reinforcement meta-learning, is a crucial mechanism for machines to develop flexibility in 20 learning, which is also considered in the brain without empirical examinations. Here, we show 21 that humans learn to learn or not learn to maximize rewards in visuomotor learning tasks. We also 22

Humans and animals develop learning-to-learn strategies throughout their lives to accelerate learning. One theory suggests that this is achieved by a metacognitive process of controlling and monitoring learning. Although such learning-to-learn is also observed in motor learning, the metacognitive aspect of learning regulation has not been considered in classical theories of motor learning. Here, we formulated a minimal mechanism of this process as reinforcement learning of motor learning properties, which regulates a policy for memory update in response to sensory prediction error while monitoring its performance. This theory was confirmed in human motor learning experiments, in which the subjective sense of learning-outcome association determined the direction of up- and down-regulation of both learning speed and memory retention. Thus, it provides a simple, unifying account for variations in learning speeds, where the reinforcement learning mechanism monitors and controls the motor learning process.