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Motor skill learning involves a complex process of generating novel movement patterns guided by evaluative feedback, such as a reward. Previous literature has suggested anteroposteriorly separated circuits in the striatum to be implicated in early goal-directed and later automatic stages of motor skill learning, respectively. However, the involvement of these circuits has not been well elucidated in human de novo motor skill learning, which requires learning arbitrary action–outcome associations and value-based action selection. To investigate this issue, we conducted a human functional MRI (fMRI) experiment in which participants learned to control a computer cursor by manipulating their right fingers. We discovered a double dissociation of fMRI activity in the anterior and posterior caudate nucleus, which was associated with performance in the early and late learning stages. Moreover, cognitive and sensorimotor cortico-caudate interactions predicted individual learning performance. Our results suggest parallel corticocaudate networks operating in different stages of human de novo motor skill learning.

Spontaneous brain activity at rest is highly organized even when the brain is not explicitly engaged in a task. Functional connectivity (FC) in the alpha frequency band (α, 8–12 Hz) during rest is associated with improved performance on various cognitive and motor tasks. In this study we explored how FC is associated with visuo-motor skill learning and offline consolidation. We tested two hypotheses by which resting-state FC might achieve its impact on behavior: preparing the brain for an upcoming task or consolidating training gains. Twenty-four healthy participants were assigned to one of two groups: The experimental group (n = 12) performed a computerized mirror-drawing task. The control group (n = 12) performed a similar task but with concordant cursor direction. High-density 156-channel resting-state EEG was recorded before and after learning. Subjects were tested for offline consolidation 24h later. The Experimental group improved during training and showed offline consolidation. Increased α-FC between the left superior parietal cortex and the rest of the brain before training and decreased α-FC in the same region after training predicted learning. Resting-state FC following training did not predict offline consolidation and none of these effects were present in controls. These findings indicate that resting-state alpha-band FC is primarily implicated in providing optimal neural resources for upcoming tasks. •Learning and offline consolidation of mirror-drawing skills are evaluated.•EEG resting-state predicts learning but not offline consolidation.•Modulations of resting state are apparent at the alpha-band in left parietal areas.•Alpha-band resting-state provides the optimal neural resources for upcoming tasks.

Objective: This systematic review aims to analyze existing literature regarding the effects of transcranial direct current stimulation (tDCS) on the motor skills learning of healthy adults and discuss the underlying neurophysiological mechanism that influences motor skills learning. Methods: This systematic review has followed the recommendations of the Preferred Reporting Items for Systematic reviews and Meta-Analyses. The PubMed, EBSCO and Web of Science databases were systematically searched for relevant studies that were published from database inception to May 2022. Studies were included on the basis of the Participants, Intervention, Comparison, Outcomes and Setting inclusion strategy. The risk of bias was evaluated by using the Review manager 5.4 tool. The quality of each study was assessed with the Physiotherapy Evidence Database (PEDro) scale. Results: The electronic search produced 142 studies. Only 11 studies were included after filtering. These studies performed well in terms of distribution, blinding availability and selective reporting. They reported that tDCS significantly improved motor skill learning. Conclusion: The included studies demonstrated that tDCS can help healthy adults to improve the motor skills learning by activating different brain regions, such as the left primary motor cortex, left dorsolateral prefrontal cortex and right cerebellum. However, the number of included studies was limited, and the sample sizes were small. Therefore, additional studies are urgently needed to validate the results of current studies and further explore the underlying neurophysiological mechanisms of tDCS in the future.

Mutations in the human FOXP2 gene cause impaired speech development and linguistic deficits, which have been best characterised in a large pedigree called the KE family. The encoded protein is highly conserved in many vertebrates and is expressed in homologous brain regions required for sensorimotor integration and motor-skill learning, in particular corticostriatal circuits. Independent studies in multiple species suggest that the striatum is a key site of FOXP2 action. Here, we used in vivo recordings in awake-behaving mice to investigate the effects of the KE-family mutation on the function of striatal circuits during motor-skill learning. We uncovered abnormally high ongoing striatal activity in mice carrying an identical mutation to that of the KE family. Furthermore, there were dramatic alterations in striatal plasticity during the acquisition of a motor skill, with most neurons in mutants showing negative modulation of firing rate, starkly contrasting with the predominantly positive modulation seen in control animals. We also observed striking changes in the temporal coordination of striatal firing during motor-skill learning in mutants. Our results indicate that FOXP2 is critical for the function of striatal circuits in vivo , which are important not only for speech but also for other striatal-dependent skills.

Most daily activities are bimanual and their efficient performance requires learning and retention of bimanual coordination. Despite in-depth knowledge of the various stages of motor skill learning in general, how new bimanual coordination control policies are established is still unclear. We designed a new cooperative bimanual task in which subjects had to move a cursor across a complex path (a circuit) as fast and as accurately as possible through coordinated bimanual movements. By looking at the transfer of the skill between different circuits and by looking at training with varying circuits, we identified two processes in early bimanual motor learning. Loss of performance due to the switch in circuit after 15 min of training amounted to 20%, which suggests that a significant portion of improvements in bimanual performance is specific to the used circuit (circuit-specific skill). In contrast, the loss of performance due to the switch in circuit was 5% after 4 min of training. This suggests that learning the new bimanual coordination control policy dominates early in the training and is independent of the used circuit. Finally, switching between two circuits throughout training did not affect the early stage of learning (i.e., the first few minutes), but did affect the later stage. Together, these results suggest that early bimanual motor skill learning includes two different processes. Learning the new bimanual coordination control policy predominates in the first minutes whereas circuit-specific skill improvements unfold later in parallel with further improvements in the bimanual coordination control policy.

The mammalian cerebral cortex is typically organized in six layers containing multiple types of neurons, with pyramidal neurons (PNs) being the most abundant. PNs in different cortical layers have distinct morphology, physiology and functional roles in neural circuits. Therefore, their development and synaptic plasticity may also differ. Using transcranial two-photon microscopy, we followed the structural dynamics of dendritic spines on apical dendrites of layer (L) 2/3 and L5 PNs at different developmental stages. We show that the density and dynamics of spines are significantly higher in L2/3 PNs than L5 PNs in both adolescent (1 month old) and adult (4 months old) mice. While spine density of L5 PNs decreases during adolescent development due to a higher rate of spine elimination than formation, there is no net change in the spine density along apical dendrites of L2/3 PNs over this period. In addition, experiences exert differential impact on the dynamics of apical dendritic spines of PNs resided in different cortical layers. While motor skill learning promotes spine turnover on L5 PNs in the motor cortex, it does not change the spine dynamics on L2/3 PNs. In addition, neonatal sensory deprivation decreases the spine density of both L2/3 and L5 PNs, but leads to opposite changes in spine dynamics among these two populations of neurons in adolescence. In summary, our data reveal distinct dynamics and plasticity of apical dendritic spines on PNs in different layers in the living mouse cortex, which may arise from their distinct functional roles in cortical circuits.

Learning skills from kinesthetic demonstrations is a promising way of minimizing the gap between human manipulation abilities and those of robots. We propose an approach to learn sequential force interaction skills from such demonstrations. The demonstrations are decomposed into a set of movement primitives by inferring the underlying sequential structure of the task. The decomposition is based on a novel probability distribution which we call Directional Normal Distribution. The distribution allows infering the movement primitive’s composition, i.e., its coordinate frames, control variables and target coordinates from the demonstrations. In addition, it permits determining an appropriate number of movement primitives for a task via model selection. After finding the task’s composition, the system learns to sequence the resulting movement primitives in order to be able to reproduce the task on a real robot. We evaluate the approach on three different tasks, unscrewing a light bulb, box stacking and box flipping. All tasks are kinesthetically demonstrated and then reproduced on a Barrett WAM robot.

During motor learning, people often practice reaching in variety of movement directions in a randomized sequence. Such training has been shown to enhance retention and transfer capability of the acquired skill compared to the blocked repetition of the same movement direction. The learning system must accommodate such randomized order either by having a memory for each movement direction, or by being able to generalize what was learned in one movement direction to the controls of nearby directions. While our preliminary study used a comprehensive dataset from visuomotor learning experiments and evaluated the first-order model candidates that considered the memory of error and generalization across movement directions, here we expanded our list of candidate models that considered the higher-order effects and error-dependent learning rates. We also employed cross-validation to select the leading models. We found that the first-order model with a constant learning rate was the best at predicting learning curves. This model revealed an interaction between the learning and forgetting processes using the direction-specific memory of error. As expected, learning effects were observed at the practiced movement direction on a given trial. Forgetting effects (error increasing) were observed at the unpracticed movement directions with learning effects from generalization from the practiced movement direction. Our study provides insights that guide optimal training using the machine-learning algorithms in areas such as sports coaching, neurorehabilitation, and human-machine interactions.

[This corrects the article DOI: 10.1371/journal.pone.0225002.].

The development of more effective rehabilitative interventions requires a better understanding of how humans learn and transfer motor skills in real-world contexts. Presently, clinicians design interventions to promote skill learning by relying on evidence from experimental paradigms involving simple tasks, such as reaching for a target. While these tasks facilitate stringent hypothesis testing in laboratory settings, the results may not shed light on performance of more complex real-world skills. In this perspective, we argue that virtual environments (VEs) are flexible, novel platforms to evaluate learning and transfer of complex skills without sacrificing experimental control. Specifically, VEs use models of real-life tasks that afford controlled experimental manipulations to measure and guide behavior with a precision that exceeds the capabilities of physical environments. This paper reviews recent insights from VE paradigms on motor learning into two pressing challenges in rehabilitation research: 1) Which training strategies in VEs promote complex skill learning? and 2) How can transfer of learning from virtual to real environments be enhanced? Defining complex skills by having nested redundancies, we outline findings on the role of movement variability in complex skill acquisition and discuss how VEs can provide novel forms of guidance to enhance learning. We review the evidence for skill transfer from virtual to real environments in typically developing and neurologically-impaired populations with a view to understanding how differences in sensory-motor information may influence learning strategies. We provide actionable suggestions for practicing clinicians and outline broad areas where more research is required. Finally, we conclude that VEs present distinctive experimental platforms to understand complex skill learning that should enable transfer from therapeutic practice to the real world.