When a standing person performs a movement such that the center of gravity shifts, the activity of postural muscles adjusts to keep the balance. We assume that such adjustments are controlled using a small set of central variables, while each variable induces changes in the activity of a subgroup of postural muscles. The purpose of this study has been to identify such muscle groups (muscle modes or M-modes) and compare them across tasks and subjects. Four tasks required the subjects to release a load from extended arms leading to a center of pressure (COP) shift prior to the load release. The fifth task required an explicit COP shift by voluntary sway. Electromyographic activity of 11 postural muscles on one side of the body was integrated over a 100-ms interval corresponding to the early stage of the COP shift, and this integrated EMG activity was subjected to a principal component (PC) analysis across multiple repetitions of each task. Three PCs were identified and associated with a "push-back M-mode," a "push-forward M-mode," and a "mixed M-mode." Cluster analysis of the PC vectors across tasks and across subjects confirmed the existence of distinctive push-forward and push-back muscle groups. PC vectors were also compared across tasks and across subjects using cosines as a measure of colinearity between pairs of vectors. In general, M-modes were similar across both tasks and subjects. We conclude that shifts of the COP, whether implicit or explicit, are controlled using a small set of central variables associated with changes in the activity of robust subsets of postural muscles. These results can be used for future analysis of muscle synergies associated with postural tasks.

The purpose of the study was to develop a model of force variability for a fast action performed by a multi-effector system and to verify it for multi-finger quick force production. The experiments involved quick isometric contractions to different target force levels using different finger combinations. Force variance calculated over sets of trials for a multi-finger force production task showed non-monotonic single-peak profiles of force variance with a peak at a time between the times of the maxima of the force rate and of the total force. When analyzed in the four-dimensional space of finger forces, the variance peak was mostly expressed in the direction of the force rate, and was absent in the directions orthogonal to it. The non-monotonic time profile of the force variance could be reproduced by a model of force production, which assumes that each finger force profile is based on a template function scaled in duration and magnitude with two parameters assigned prior to each trial with some variability. The model allows decomposition of the force variance into two fractions related to variability in setting the magnitude and duration scaling parameters. The former fraction changes monotonically with time, while the latter shows a transient peak in the middle of the action. The model was able to reproduce experimental variance time profiles across conditions with the total error of under 8%. The results demonstrate, in particular, that fast multi-finger actions may show transient changes in motor variability in certain directions of the finger force space, particularly in the direction of the first force derivative, without any task-specific coordinating action by the controller. These findings require a reconsideration of some of the conclusions drawn in recent studies on the structure of motor variability in redundant multi-effector systems.

We tested a hypothesis that force production by multi-finger groups leads to lower indices of force variability as compared to similar single-finger tasks. Three experiments were performed with quick force production, steady-state force production under visual feedback, and steady-state force production without visual feedback. In all experiments, a range of force levels was used computed as percentages of the maximal voluntary contraction force for each involved finger combination. Force standard deviation increased linearly with force magnitude across all three experiments and all finger combinations. There were modest differences between multi-finger and single-finger tasks in the indices of force variability, significant only in the tasks with steady-state force production under visual feedback. When fingers acted in groups, each finger showed significantly higher force variability as compared to its single-finger task and as compared to the multi-finger group as a whole. Fingers that were not instructed to produce force also showed close to linear relations between force standard deviation and force magnitude. For these fingers, indices of force variability were much higher as compared to those computed for the forces produced by instructed fingers. We interpret the findings within a feed-forward scheme of multi-finger control with two inputs only one of which is related to the explicit task. The total force variability reflects variability in only the task-related component, while variability of the finger forces is also due to variability of the component that is not related to the task. The findings tentatively suggest that total force variability originates at an upper level of the control hierarchy in accordance to the Weber-Fechner law rather than reflects a "neural noise" at the segmental level.

We describe a model of feed-forward control of a redundant motor system and validate it using, as examples, tasks of multi-finger force production. The model assumes the existence of two input signals at an upper level of the control hierarchy, related and unrelated to a task variable. Knowledge of the Jacobian of the system is assumed at the level of generation of elemental variables (variables at the level of effectors). Variance at the level of elemental variables is considered as the sum of two components, related and unrelated to variability in the task variable. An index of stabilization of the task variable is similarly introduced as to how it was done in several studies using the framework of the uncontrolled manifold hypothesis. Several phenomena have been simulated including data point distributions corresponding to presence and absence of force-stabilizing synergies in two-finger tasks, changes in synergies with practice, and changes in synergy indices in preparation to a fast action. The model is discussed in comparison to other models of control of multi-element systems based on feedback processes. It shows that patterns of structured variability in the space of elemental variables can result from feed-forward processes. Relations of the model to the equilibrium-point hypothesis are also discussed.

The authors examined the kinematic qualities of the aiming trajectory as related to expertise. In all, 2 phases of the trajectory were discriminated. The first phase was regular approximation to the target accompanied by substantial fluctuations obeying the Weber-Fechner law. During the first phase, shooters did not initiate the triggering despite any random closeness of the aiming point (AP) to the target. In the second phase, beginning at 0.6-0.8 s before the trigger pull, shooters applied a different control strategy: They waited until the following random fluctuation brought the AP closer to the target and then initiated triggering. This strategy is tenable when sensitivity of perception is greater than precision of the motor action, and could be considered a case of stochastic resonance. The strategies that novices and experts used distinguished only in the values of parameters. The authors present an analytical model explaining the main properties of shooting.