Rambling and trembling in response to body loading.

Various studies have suggested that postural sway is controlled by at least two subsystems. Rambling-Trembling analysis is a widely accepted methodology to dissociate the signals generated by these two hypothetical subsystems. The core assumption of this method is based on the equilibrium point hypothesis which suggests that the central nervous system preserves upright standing by transiently shifting the center of pressure (COP) from one equilibrium point to another. The trajectory generated by this shifting is referred to as rambling and its difference from the original COP signal is referred to as trembling. In this study we showed that these two components of COP are differentially affected when standing with external loads. Using Detrended Fluctuation analysis, we compared the pattern of these two signals in different configurations of body loading. Our findings suggest that by applying an external load, the dynamics of the trembling component is altered independently of the area of postural sway and also independently of the rambling component. The dynamics of rambling changed only during the backloading condition in which the postural sway area also substantially increased. It can be suggested that during loaded standing, the trembling mechanism (which is suggested to be activated by peripheral mechanisms and reflexes) is altered without affecting the central influence on the shifts of the equilibrium point.

Snoddy (1926) revisited: time scales of motor learning.

The authors investigated the time scales of the learning of a mirror-tracing task to reexamine G. S. Snoddy's (1926) original claim and the received theoretical view (A. Newell & P. S. Rosenbloom, 1981) that motor learning follows a power law. Adult participants (N = 16) learned the tracing task in either a normal or a reversed visual-image condition over 5 consecutive days of practice and then performed 1 day of practice 1 week later and again 1 month later. The reversed-image group's performance was poorer than that of the normal-image group throughout the practice. An exponential was the best fitting function on individual data, but the power-law function was the best fit on the group-averaged data. The findings provided preliminary evidence that 2 characteristic time scales, (a) fast, dominated by warm-up, and (b) slow, dominated by persistent change, capture individuals' performance in the learning of the mirror-tracing task.