Influence of switching rule on motor learning

Detecting task-relevant spatiotemporal modules and their relation to motor adaptation

How does the central nervous system (CNS) control our bodies, including hundreds of degrees of freedom (DoFs)? A hypothesis to reduce the number of DoFs posits that the CNS controls groups of joints or muscles (i.e., modules) rather than each joint or muscle independently. Another hypothesis posits that the CNS primarily controls motion components relevant to task achievements (i.e., task-relevant components). Although the two hypotheses are examined intensively, the relationship between the two concepts remains unknown, e.g., unimportant modules may possess task-relevant information. Here, we propose a framework of task-relevant modules, i.e., modules relevant to task achievements, while combining the two concepts mentioned above in a data-driven manner. To examine the possible role of the task-relevant modules, we examined the modulation of the task-relevant modules in a motor adaptation paradigm in which trial-to-trial modifications of motor output are observable. The task-relevant modules, rather than conventional modules, showed adaptation-dependent modulations, indicating the relevance of task-relevant modules to trial-to-trial updates of motor output. Our method provides insight into motor control and adaptation via an integrated framework of modules and task-relevant components.

Transition between individually different and common features in skilled drumming movements

Why do professional athletes and musicians exhibit individually different motion patterns? For example, baseball pitchers generate various pitching forms, e.g., variable wind-up, cocking, and follow-through forms. However, they commonly rotate their wrists and fingers at increasingly high speeds via shoulder and trunk motions. Despite the universality of common and individually different motion patterns in skilled movements, the abovementioned question remains unanswered. Here, we focus on a motion required to hit a snare drum, including the indirect phase of task achievement (i.e., the early movement and mid-flight phases) and the direct phase of task achievement (i.e., the hit phase). We apply tensor decomposition to collected kinematic data for the drum-hitting motion, enabling us to decompose high-dimensional and time-varying motion data into individually different and common movement patterns. As a result, individually different motion patterns emerge during the indirect phase of task achievement, and common motion patterns are evident in the direct phase of task achievement. Athletes and musicians are thus possibly allowed to perform individually different motion patterns during the indirect phase of task achievement. Additionally, they are required to exhibit common patterns during the direct phase of task achievement.

Acoustic stimulation increases implicit adaptation in sensorimotor adaptation

Sensorimotor adaptation is an important part of our ability to perform novel motor tasks (i.e., learning of motor skills). Efforts to improve adaptation in healthy and clinical patients using non-invasive brain stimulation methods have been hindered by inter-individual and intra-individual variability in brain susceptibility to stimulation. Here, we explore unpredictable loud acoustic stimulation as an alternative method of modulating brain excitability to improve sensorimotor adaptation. In two experiments, participants moved a cursor towards targets, and adapted to a 30º rotation of cursor feedback, either with or without unpredictable acoustic stimulation. Acoustic stimulation improved initial adaptation to sensory prediction errors in Study 1, and improved overnight retention of adaptation in Study 2. Unpredictable loud acoustic stimulation might thus be a potent method of modulating sensorimotor adaptation in healthy adults.

How do Elite Soccer Teams Perform to Ball Recovery? Effects of Tactical Modelling and Contextual Variables on the Defensive Patterns of Play

Researchers in soccer match analysis have been using limited procedures to express the dynamics of the game and mainly focus on the attack. Therefore, the aims of this paper were to detect the successful teams’ ball recovery defensive patterns of play and study the influence of tactical modelling, halves, match status, opponent quality and stage competition on those patterns. The sample consisted of 1323 situations of defensive ball possession of the semi-finalist teams from the 2014 FIFA World Cup play-offs, which was collected by a valid and reliable observational instrument (Soccer-Defence). The Kruskal-Wallis H, Mann-Whitney U, Chi-square, Z-, multinomial logistic regression tests and sequential analysis (p < .05; z > 1.96) were used accordingly to test the differences and associations among and within teams of tactical modelling, tactical-technical behaviours and contextual variables to ball recovery. We found that among teams ball recovery differed in duration; H(3) = 14.958, p = .002. Germany were more likely to perform ball recovery by the goalkeeper than Argentina (p = .04; OR = 0.47) or the Netherlands (p < .05; OR = 0.50). Nevertheless, Brazil was the least likely to concede a shot off goal. Teams facing lower-ranked opponents were 0.63 times less likely to perform ball recovery by interception (p <.001). Additionally, sequential analysis illustrated that teams varied between central and lateral high-pressure zones before ball recovery in lower zones of the field. Finally, coaches could use such findings to design training exercises, create their own style of play, and set strategies.

Larger, but not better, motor adaptation ability inherent in medicated Parkinson's disease patients revealed by a smart-device-based study

Generating appropriate motor commands is an essential brain function. To achieve proper motor control in diverse situations, predicting future states of the environment and body and modifying the prediction are indispensable. The internal model is a promising hypothesis about brain function for generating and modifying the prediction. Although several findings support the involvement of the cerebellum in the internal model, recent results support the influence of other related brain regions on the internal model. A representative example is the motor adaptation ability in Parkinson’s disease (PD) patients. Although this ability provides some hints about how dopamine deficits and other PD symptoms affect the internal model, previous findings are inconsistent; some reported a deficit in the motor adaptation ability in PD patients, but others reported that the motor adaptation ability of PD patients is comparable to that of healthy controls. A possible factor causing this inconsistency is the difference in task settings, resulting in  different cognitive strategies in each study. Here, we demonstrate a larger, but not better, motor adaptation ability in PD patients than in healthy controls while reducing the involvement of cognitive strategies and concentrating on implicit motor adaptation abilities. This study utilizes a smart-device-based experiment that enables motor adaptation experiments anytime and anywhere with less cognitive strategy involvement. The PD patients showed a significant response to insensible environmental changes, but the response was not necessarily suitable for adapting to the changes. Our findings support compensatory cerebellar functions in PD patients from the perspective of motor adaptation.

A data-driven approach to decompose motion data into task-relevant and task-irrelevant components in categorical outcome

Decomposition of motion data into task-relevant and task-irrelevant components is an effective way to clarify the diverse features involved in motor control and learning. Several previous methods have succeeded in this type of decomposition while focusing on the clear relation of motion to both a specific goal and a continuous outcome, such as a 10 mm deviation from a target or 1 m/s hand velocity. In daily life, it is vital to quantify not only continuous but also categorical outcomes. For example, in baseball, batters must judge whether the opposing pitcher will throw a fastball or a breaking ball; tennis players must decide whether an opposing player will serve out wide or down the middle. However, few methods have focused on quantifying categorical outcome; thus, how to decompose motion data into task-relevant and task-irrelevant components when the outcome is categorical rather than continuous remains unclear. Here, we propose a data-driven method to decompose motion data into task-relevant and task-irrelevant components when the outcome takes categorical values. We applied our method to experimental data where subjects were required to throw fastballs or breaking balls with a similar form. Our data-driven approach can be applied to the unclear relation between motion and outcome, and the relation can be estimated in a data-driven manner. Furthermore, our method can successfully evaluate how the task-relevant components are modulated depending on the task requirements.

Speed-dependent and mode-dependent modulations of spatiotem-poral modules in human locomotion extracted via tensor decom-position

How the central nervous system (CNS) controls many joints and muscles is a fundamental question in motor neuroscience and related research areas. An attractive hypothesis is the module hypothesis: the CNS controls groups of joints or muscles (i.e., spatial modules) by providing time-varying motor commands (i.e., temporal modules) to the spatial modules rather than controlling each joint or muscle separately. Another fundamental question is how the CNS generates numerous repertoires of movement patterns. One hypothesis is that the CNS modulates the spatial and/or temporal modules depending on the required tasks. It is thus essential to quantify the spatial modules, the temporal modules, and the task-dependent modulation of these modules. Although previous attempts at such quantification have been made, they considered modulation either only in spatial modules or only in temporal modules. These limitations may be attributable to the constraints inherent to conventional methods for quantifying the spatial and temporal modules. Here, we demonstrate the effectiveness of tensor decomposition in quantifying the spatial modules, the temporal modules, and the task-dependent modulation of these modules without such limitations. We further demonstrate that tensor decomposition offers a new perspective on the task-dependent modulation of spatiotemporal modules: in switching from walking to running, the CNS modulates the peak timing in the temporal modules while recruiting more proximal muscles in the corresponding spatial modules.

Decomposing motion that changes over time into task-relevant and task-irrelevant components in a data-driven manner: application to motor adaptation in whole-body movements

Motor variability is inevitable in human body movements and has been addressed from various perspectives in motor neuroscience and biomechanics: it may originate from variability in neural activities, or it may reflect a large number of degrees of freedom inherent in our body movements. How to evaluate motor variability is thus a fundamental question. Previous methods have quantified (at least) two striking features of motor variability: smaller variability in the task-relevant dimension than in the task-irrelevant dimension and a low-dimensional structure often referred to as synergy or principal components. However, the previous methods cannot be used to quantify these features simultaneously and are applicable only under certain limited conditions (e.g., one method does not consider how the motion changes over time, and another does not consider how each motion is relevant to performance). Here, we propose a flexible and straightforward machine learning technique for quantifying task-relevant variability, task-irrelevant variability, and the relevance of each principal component to task performance while considering how the motion changes over time and its relevance to task performance in a data-driven manner. Our method reveals the following novel property: in motor adaptation, the modulation of these different aspects of motor variability differs depending on the perturbation schedule.