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 studied the organization of leg and trunk muscles into groups (M-modes) and co-variation of M-mode involvement (M-mode synergies) during whole-body tasks associated with large variations of the moment of force about the vertical body axis. Our major questions were: (1) Can muscle activation patterns during such tasks be described with a few M-modes common across tasks and subjects? (2) Do these modes form the basis for synergies stabilizing MZ time pattern? (3) Will this organization differ between an explicit body rotation task and a task associated with locomotor-like alternating arm movements? Healthy subjects stood barefoot on the force platform and performed two motor tasks while paced by the metronome at 0.7, 1.0, and 1.4 Hz: Cyclic rotation of the upper body about the vertical body axis (body rotation task), and alternating rhythmic arm movements imitating those during running or quick walking (arm movement task). Principal component analysis was used to identify three M-modes within the space of integrated indices of muscle activity. The M-mode vectors showed clustering neither across subjects nor across frequencies. Variance in the M-mode space across sway cycles was partitioned into two components, one that did not affect the average value of MZ shift ("good variance") and the other that did. An index was computed reflecting the relative amount of the "good variance"; positive values of this index have been interpreted as reflecting a multi-M-mode synergy stabilizing the MZ trajectory. On average, the index was positive for both tasks and across all frequencies studied. However, the magnitude of the index was smaller for the intermediate frequency (1 Hz). The results show that the organization of muscles into groups during relatively complex whole-body tasks can differ significantly across both task variations and subjects. Nevertheless, the central nervous system seems to be able to build MZ stabilizing synergies based on different sets of M-modes, within the approach accepted in this study. The drop in the synergy index at the frequency of 1 Hz, which was close to the preferred movement frequency, may be interpreted as corroborating the neural origin of the M-mode co-variation.