Anticipatory synergy adjustments reflect individual performance of feedforward force control

We grasp and dexterously manipulate an object through multi-1 digit synergy. In the framework of the uncontrolled manifold (UCM) hypothesis, multi-digit synergy is defined as the coordinated control mechanism of fingers to stabilize variable important for task success, e.g., total force. Previous studies reported anticipatory synergy adjustments (ASAs) that correspond to a drop of the synergy index before a quick change of the total force. The present study compared ASA’s properties with individual performances of feedforward force control to investigate a relationship of those. Subjects performed a total finger force production task that consisted of a phase in which subjects tracked target line with visual information and a phase in which subjects produced total force pulse without visual information. We quantified their multi-digit synergy through UCM analysis and observed significant ASAs before producing total force pulse. The time of the ASA initiation and the magnitude of the drop of the synergy index were significantly correlated with the error of force pulse, but not with the tracking error. Almost all subjects showed a significant increase of the variance that affected the total force. Our study directly showed that ASA reflects the individual performance of feedforward force control independently of target-tracking performance and suggests that the multi-digit synergy was weakened to adjust the multi-digit movements based on a prediction error so as to reduce the future error

Cross-Modal Matching Memory in Young Children : An Exploratory Study with a Long Retention Interval

Fifty-two young children (aged 4 years to 6 years, 4 months) were tested with cross-modal matching and within-modal matching of shapes with two delay conditions. Children were given either visual (V) or tactual (T) acquisition training and then tested for visual recognition (V-V, T-V) or tactual recognition (T-T, V-T) with two retention intervals (0-delay or 1-week-delay). T-V was remembered more easily than V-T with 0-delay, whereas V-T was easier with a 1-week-delay

Normalized Index of Synergy for Evaluating the Coordination of Motor Commands

<div><p>Humans perform various motor tasks by coordinating the redundant motor elements in their bodies. The coordination of motor outputs is produced by motor commands, as well properties of the musculoskeletal system. The aim of this study was to dissociate the coordination of motor commands from motor outputs. First, we conducted simulation experiments where the total elbow torque was generated by a model of a simple human right and left elbow with redundant muscles. The results demonstrated that muscle tension with signal-dependent noise formed a coordinated structure of trial-to-trial variability of muscle tension. Therefore, the removal of signal-dependent noise effects was required to evaluate the coordination of motor commands. We proposed a method to evaluate the coordination of motor commands, which removed signal-dependent noise from the measured variability of muscle tension. We used uncontrolled manifold analysis to calculate a normalized index of synergy. Simulation experiments confirmed that the proposed method could appropriately represent the coordinated structure of the variability of motor commands. We also conducted experiments in which subjects performed the same task as in the simulation experiments. The normalized index of synergy revealed that the subjects coordinated their motor commands to achieve the task. Finally, the normalized index of synergy was applied to a motor learning task to determine the utility of the proposed method. We hypothesized that a large part of the change in the coordination of motor outputs through learning was because of changes in motor commands. In a motor learning task, subjects tracked a target trajectory of the total torque. The change in the coordination of muscle tension through learning was dominated by that of motor commands, which supported the hypothesis. We conclude that the normalized index of synergy can be used to evaluate the coordination of motor commands independently from the properties of the musculoskeletal system.</p></div

Schematic of the experimental set-up.

<p>Subjects were seated on a chair, wearing a seat-belt to fix their trunks, and had force sensors placed in their right and left hands. The red squares denote electrodes used to record the electromyogram data. The screen on the desk showed the information corresponding to the target hand force production task (a), the target total torque production task (b), and the tracking-ability learning task (c). (d) One example of a target trajectory of total elbow torque. The target trajectory consists of 14 half-sine waves. The four half-sine waves in the green boxes are standard half-sine waves (amplitude: ± 3 N·m, period: 4 s), and the others are randomly generated half-sine waves (amplitude: mean ± 3 N·m, standard deviation 1 N·m, period: mean 4 s, standard deviation 0.4 s). The blue line indicates the target trajectory and the red lines indicate the ± 0.3 N·m margin.</p

Results of the measurement experiments of the target total torque production task.

<p>(a) The blue circle indicates individual left, right and total elbow torques of all subjects. (b) The UCM component and the ORT component of all subjects. (c) The index of synergy of all subjects. The asterisk indicates a significant difference (<i>P</i> < 0.05), and n.s. indicates a non-significant difference.</p

Concept of coordination of motor commands.

<p>(a) The simulation experiment shows that signal-dependent noise in the muscle contributes to the coordination of motor outputs. We propose a method that removes the effects of the musculoskeletal system for the assessment of the coordination of motor commands. (b) The simulation experiment validates our proposed method. (c) To show the coordination of motor commands of human subjects, the proposed method is applied to the measurement experiment in which the subjects perform the same task as the simulation experiment. (d) To empirically validate the proposed method, it is applied to the measurement experiment of the learning task.</p

Inputs for each muscle in the simulation experiments.

<p>Inputs for each muscle in the simulation experiments.</p

Change in the tracking performance through motor learning.

<p>(a) The duration of out of margin <i>t</i>_<i>out</i>. (b) The feedback gain <i>G</i> (Eq (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140836#pone.0140836.e019" target="_blank">11</a>)). The blue, green and red bars indicate the pre-test, the learning phases, and the post-test, respectively. The asterisk indicates a significant difference (<i>P</i> < 0.05). Specific values are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140836#pone.0140836.s004" target="_blank">S3 Table</a>.</p