Biology subjects

Takiyama, K.

Publications and source records attributed to Takiyama, K..

3 recordsLinked to original sources

bioRxiv2018

Optimal motor decision-making through competition with opponents

Although optimal decision-making is essential for sports performance and fine motor control, it has been repeatedly confirmed that humans show a strong risk-seeking bias, selecting a risky strategy over an optimal solution. Despite such evidence, the ideal method to promote optimal decision-making remains unclear. Here, we propose that interactions with other people can influence motor decision-making and improve risk-seeking bias. We developed a competitive reaching game (a variant of the "chicken game") in which aiming for greater rewards increased the risk of no reward and subjects competed for the total reward with their opponent. The game resembles situations in sports, such as a penalty kick in soccer, service in tennis, the strike zone in baseball, or take-off in ski jumping. In five different experiments, we demonstrated that, at the beginning of the competitive game, the subjects robustly switched their risk-seeking strategy to a risk-averse strategy. Following the reversal of the strategy, the subjects achieved optimal decision-making when competing with risk-averse opponents. This optimality was achieved by a non-linear influence of an opponents decisions on a subjects decisions. These results suggest that interactions with others can alter human motor decision strategies and that competition with a risk-averse opponent is key for optimizing motor decision-making.

neuroscience↗

bioRxiv2018

Detection of task-relevant and task-irrelevant motion sequences: application to motor adaptation in goal-directed and whole-body movements

Motor variability is inevitable in our body movements and is discussed from several various perspectives in motor neuroscience and biomechanics; it can originate from the variability of neural activities, it can reflect a large degree of freedom inherent in our body movements, it can decrease muscle fatigue, or it can facilitate motor learning. How to evaluate motor variability is thus a fundamental question in motor neuroscience and biomechanics. Previous methods have quantified (at least) two striking features of motor variability; the smaller variability in the task-relevant dimension than in the task-irrelevant dimension and the low-dimensional structure that is often referred to as synergy or principal component. However, those previous methods were not only unsuitable for quantifying those features simultaneously but also applicable in some limited conditions (e.g., a method cannot consider motion sequence, and another method cannot consider how each motion is relevant to performance). Here, we propose a flexible and straightforward machine learning technique that can quantify task-relevant variability, task-irrelevant variability, and the relevance of each principal component to task performance while considering the motion sequence and the relevance of each motion sequence to task performance in a data-driven manner. We validate our method by constructing a novel experimental setting to investigate goal-directed and whole-body movements. Furthermore, our setting enables the induction of motor adaptation by using perturbation and evaluating the modulation of task-relevant and task-irrelevant variabilities through motor adaptation. Our method enables the identification of a novel property of motor variability; the modulation of those variabilities differs depending on the perturbation schedule. Although a gradually imposed perturbation does not increase both task-relevant and task-irrelevant variabilities, a constant perturbation increases task-relevant variability.

neuroscience↗

bioRxiv2018

Influence of switching rule on motor learning

Humans and animals can flexibly switch rules to generate appropriate motor commands; for example, actions can be flexibly produced toward a sensory stimulus (e.g., pro-saccade or pro-reaching) or away from a sensory stimulus (e.g., anti-saccade or anti-reaching). Distinct neural activities are related to pro- and anti-movement actions; however, the effects of switching rules on motor learning are unclear. Here, we study the effect of switching rules on motor learning using pro- and anti-arm-reaching movements and a visuomotor rotation task. Although previous results support the perfect availability of learning effects under the same required movements, we show that the learning effects trained in pro-reaching movements are partially rather than perfectly available in anti-reaching movements even under the same required movement direction between those two conditions. The partial transfer is independent of the difference in the visual cue, the cognitive demand, and the actual movement direction between the pro- and anti-reaching movements. We further demonstrate that the availability of learning effects trained with pro-reaching movements is partial not only in anti-reaching movements but in reaching movements with other rules and the availability of learning effects trained with anti-reaching movements is also partial in pro-reaching movements. We thus conclude that the switching rule causes the availability of learning effects to be partial rather than perfect even under same planned movements.\n\nNew & Noteworthy\n\nMost motor learning experiments supported the involvement of planned movement directions in motor learning; the learning effects trained in a movement direction can be available at movement directions close to the trained one. Here, we show that the availability of motor learning effects is partial rather than perfect even under the same planned movements when rule is switched, which indicates that sports training and rehabilitation should include various situations under the same required motions.

neuroscience↗