Abstract
Currently, it is not fully understood how motor variability is regulated to ease of motor learning processes during reward-based tasks. This study aimed to assess the potential relationship between different dimensions of motor variability (i.e., the motor variability structure and the motor synergies variability) and the learning rate in a reward-based task developed using a two-axis force sensor in a computer environment. Forty-four participants performed a pretest, a training period, a posttest, and three retests. They had to release a virtual ball to hit a target using a vertical handle attached to a dynamometer in a computer-simulated reward-based task. The participants’ throwing performance, learning ratio, force applied, variability structure (detrended fluctuation analysis, DFA), and motor synergy variability (good and bad variability ratio, GV/BV) were calculated. Participants with higher initial GV/BV displayed greater performance improvements than those with lower GV/BV. DFA did not show any relationship with the learning ratio. These results suggest that exploring a broader range of successful motor synergy combinations to achieve the task goal can facilitate further learning during reward-based tasks. The evolution of the motor variability synergies as an index of the individuals’ learning stages seems to be supported by our study.
Highlights
IntroductionSome studies have supported the idea that different levels of variability should be manipulated according to the individual’s learning stage
Nervous System (CNS) modulates motor variability to enable the exploration of all the possible configurations provided by the large number of motor system degrees of freedom (DOF), enhancing the achievement of the required movement solution [3,4,5]
Apart from the GV/BV and the detrended fluctuation analysis (DFA), all the dependent variables showed significant differences caused by the training
Summary
Some studies have supported the idea that different levels of variability should be manipulated according to the individual’s learning stage It seems that there is a need for inducing higher-variability conditions to promote motor variations when exploration is required to learn a novel task. Reward-based learning is based on the idea that if an action is followed by a successful output, the tendency to repeat that same action is strengthened [9] In this learning condition, the output received by the individual who performs the action only indicates how successful that output was; and, it carries no other feedback information about the motor execution that allows the individual to modify their motor behavior [10]. Higher motor variability has been related to faster reward-based learning [2,11]
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