Motor behaviors are shaped not only by current sensory signals but also by the history of recent experiences. For instance, repeated movements toward a particular target bias the subsequent movements toward that target direction. This process, called use-dependent plasticity (UDP), is considered a basic and goal-independent way of forming motor memories. Most studies consider movement history as the critical component that leads to UDP (Classen et al., 1998; Verstynen and Sabes, 2011). However, the effects of learning (i.e., improved performance) on UDP during movement repetition have not been investigated. Here, we used transcranial magnetic stimulation in two experiments to assess plasticity changes occurring in the primary motor cortex after individuals repeated reinforced and nonreinforced actions. The first experiment assessed whether learning a skill task modulates UDP. We found that a group that successfully learned the skill task showed greater UDP than a group that did not accumulate learning, but made comparable repeated actions. The second experiment aimed to understand the role of reinforcement learning in UDP while controlling for reward magnitude and action kinematics. We found that providing subjects with a binary reward without visual feedback of the cursor led to increased UDP effects. Subjects in the group that received comparable reward not associated with their actions maintained the previously induced UDP. Our findings illustrate how reinforcing consistent actions strengthens use-dependent memories and provide insight into operant mechanisms that modulate plastic changes in the motor cortex.SIGNIFICANCE STATEMENTPerforming consistent motor actions induces use-dependent plastic changes in the motor cortex. This plasticity reflects one of the basic forms of human motor learning. Past studies assumed that this form of learning is exclusively affected by repetition of actions. However, here we showed that success-based reinforcement signals could affect the human use-dependent plasticity (UDP) process. Our results indicate that learning augments and interacts with UDP. This effect is important to the understanding of the interplay between the different forms of motor learning and suggests that reinforcement is not only important to learning new behaviors, but can shape our subsequent behavior via its interaction with UDP.

Motor exploration, a trial-and-error process in search for better motor outcomes, is known to serve a critical role in motor learning. This is particularly relevant during reinforcement learning, where actions leading to a successful outcome are reinforced while unsuccessful actions are avoided. Although early on motor exploration is beneficial to finding the correct solution, maintaining high levels of exploration later in the learning process might be deleterious. Whether and how the level of exploration changes over the course of reinforcement learning, however, remains poorly understood. Here we evaluated temporal changes in motor exploration while healthy participants learned a reinforcement-based motor task. We defined exploration as the magnitude of trial-to-trial change in movements as a function of whether the preceding trial resulted in success or failure. Participants were required to find the optimal finger-pointing direction using binary feedback of success or failure. We found that the magnitude of exploration gradually increased over time when participants were learning the task. Conversely, exploration remained low in participants who were unable to correctly adjust their pointing direction. Interestingly, exploration remained elevated when participants underwent a second training session, which was associated with faster relearning. These results indicate that the motor system may flexibly upregulate the extent of exploration during reinforcement learning as if acquiring a specific strategy to facilitate subsequent learning. Also, our findings showed that exploration affects reinforcement learning and vice versa, indicating an interactive relationship between them. Reinforcement-based tasks could be used as primers to increase exploratory behavior leading to more efficient subsequent learning. NEW & NOTEWORTHY Motor exploration, the ability to search for the correct actions, is critical to learning motor skills. Despite this, whether and how the level of exploration changes over the course of training remains poorly understood. We showed that exploration increased and remained high throughout training of a reinforcement-based motor task. Interestingly, elevated exploration persisted and facilitated subsequent learning. These results suggest that the motor system upregulates exploration as if learning a strategy to facilitate subsequent learning.

Objective . Cerebellar transcranial direct current stimulation (ctDCS) is a neuromodulation scheme that delivers a small current to the cerebellum. In this work, we computationally investigate the distributions and strength of the stimulation dosage during ctDCS with the aim of determining the targeted cerebellar regions of a group of subjects with different electrode montages. Approach . We used a new inter-individual registration method that permitted the projection of computed electric fields (EFs) from individual realistic head models ( n = 18) to standard cerebellar template for the first time. Main results . Variations of the EF on the cerebellar surface were found to have standard deviations of up to 55% of the mean. The dominant factor that accounted for 62% of the variability of the maximum EFs was the skin–cerebellum distance, whereas the cerebrospinal fluid volume explained 53% of the average EF distribution. Despite the inter-individual variations, a systematic tendency of the EF hotspot emerges beneath the active electrode in group-level analysis. The hotspot can be adjusted by the electrode position so that the most effective stimulation is delivered to a group of subjects. Significance . Targeting specific cerebellar structures with ctDCS is not straightforward, as neuromodulation depends not only on the placement/design of the electrodes configuration but also on inter-individual variability due to anatomical differences. The proposed method permitted generalizing the EFs to a cerebellum atlas. The atlas is useful for studying the mechanisms of ctDCS, planning ctDCS and explaining findings of experimental studies.

Humans can acquire knowledge of new motor behavior via different forms of learning. The two forms most commonly studied have been the development of internal models based on sensory-prediction errors (error-based learning) and success-based feedback (reinforcement learning). Human behavioral studies suggest these are distinct learning processes, though the neurophysiological mechanisms that are involved have not been characterized. Here, we evaluated physiological markers from the cerebellum and the primary motor cortex (M1) using noninvasive brain stimulations while healthy participants trained finger-reaching tasks. We manipulated the extent to which subjects rely on error-based or reinforcement by providing either vector or binary feedback about task performance. Our results demonstrated a double dissociation where learning the task mainly via error-based mechanisms leads to cerebellar plasticity modifications but not long-term potentiation (LTP)-like plasticity changes in M1; while learning a similar action via reinforcement mechanisms elicited M1 LTP-like plasticity but not cerebellar plasticity changes. Our findings indicate that learning complex motor behavior is mediated by the interplay of different forms of learning, weighing distinct neural mechanisms in M1 and the cerebellum. Our study provides insights for designing effective interventions to enhance human motor learning.

Background. Stroke is one of the most common causes of physical disability worldwide. The majority of survivors experience impairment of movement, often with lasting deficits affecting hand dexterity. To date, conventional rehabilitation primarily focuses on training compensatory maneuvers emphasizing goal completion rather than targeting reduction of motor impairment. Objective. We aim to determine whether finger dexterity impairment can be reduced in chronic stroke when training on a task focused on moving fingers against abnormal synergies without allowing for compensatory maneuvers. Methods. We recruited 18 chronic stroke patients with significant hand motor impairment. First, participants underwent baseline assessments of hand function, impairment, and finger individuation. Then, participants trained for 5 consecutive days, 3 to 4 h/d, on a multifinger piano-chord-like task that cannot be performed by compensatory actions of other body parts (e.g., arm). Participants had to learn to simultaneously coordinate and synchronize multiple fingers to break unwanted flexor synergies. To test generalization, we assessed performance in trained and nontrained chords and clinical measures in both the paretic and the nonparetic hands. To evaluate retention, we repeated the assessments 1 day, 1 week, and 6 months post-training. Results. Our results showed that finger impairment assessed by the individuation task was reduced after training. The reduction of impairment was accompanied by improvements in clinical hand function, including precision pinch. Notably, the effects were maintained for 6 months following training. Conclusion. Our findings provide preliminary evidence that chronic stroke patient can reduce hand impairment when training against abnormal flexor synergies, a change that was associated with meaningful clinical benefits.

Background: Interventions and care that can evoke positive emotions and reduce apathy or agitation are important for people with dementia. In recent years, socially assistive robots used for better dementia care have been found to be feasible. However, the immediate responses of people with dementia when they are given multiple sensory modalities from socially assistive robots have not yet been sufficiently elucidated. Objective: This study aimed to quantitatively examine the immediate emotional responses of people with dementia to stimuli presented by socially assistive robots using facial expression analysis in order to determine whether they elicited positive emotions. Methods: This pilot study adopted a single-arm interventional design. Socially assistive robots were presented to nursing home residents in a three-step procedure: (1) the robot was placed in front of participants (visual stimulus), (2) the robot was manipulated to produce sound (visual and auditory stimuli), and (3) participants held the robot in their hands (visual, auditory, and tactile stimuli). Expression intensity values for “happy,” “sad,” “angry,” “surprised,” “scared,” and “disgusted” were calculated continuously using facial expression analysis with FaceReader. Additionally, self-reported feelings were assessed using a 5-point Likert scale. In addition to the comparison between the subjective and objective emotional assessments, expression intensity values were compared across the aforementioned 3 stimuli patterns within each session. Finally, the expression intensity value for “happy” was compared between the different types of robots. Results: A total of 29 participants (mean age 88.7, SD 6.2 years; n=27 female; Japanese version of Mini-Mental State Examination mean score 18.2, SD 5.1) were recruited. The expression intensity value for “happy” was the largest in both the subjective and objective assessments and increased significantly when all sensory modalities (visual, auditory, and tactile) were presented (median expression intensity 0.21, IQR 0.09-0.35) compared to the other 2 patterns (visual alone: median expression intensity 0.10, IQR 0.03-0.22; P<.001; visual and auditory: median expression intensity 0.10, IQR 0.04-0.23; P<.001). The comparison of different types of robots revealed a significant increase when all stimuli were presented by doll-type and animal-type robots, but not humanoid-type robots. Conclusions: By quantifying the emotional responses of people with dementia, this study highlighted that socially assistive robots may be more effective in eliciting positive emotions when multiple sensory stimuli, including tactile stimuli, are involved. More studies, including randomized controlled trials, are required to further explore the effectiveness of using socially assistive robots in dementia care.