The sensorimotor system prefers sway velocity information when maintaining upright posture. Sway velocity has a unique characteristic of being persistent on a short time-scale and anti-persistent on a longer time-scale. The time where the transition from persistence to anti-persistence occurs provides information about how sway velocity is controlled. It is, however, not clear what factors affect shifts in this transition point. This research investigated postural responses to support surface movements of different temporal correlations and movement velocities. Participants stood on a force platform that was translated according to three different levels of temporal correlation. White noise had no correlation, pink noise had moderate correlation, and sine wave movements had very strong correlation. Each correlation structure was analyzed at five different average movement velocities (0.5, 1.0, 2.0, 3.0, and 4.0 cm·s-1), as well as one trial of quiet stance. Center of pressure velocity was analyzed using fractal analysis to determine the transition from persistent to anti-persistent behavior, as well as the strength of persistence. As movement velocity increased, the time to transition became longer for the sine wave and shorter for the white and pink noise movements. Likewise, during the persistent time-scale, the sine wave resulted in the strongest correlation, while white and pink noise had weaker correlations. At the highest three movement velocities, the strength of persistence was lower for the white noise compared to pink noise movements. These results demonstrate that the predictability and velocity of support surface oscillations affect the time-scale threshold between persistent and anti-persistent postural responses. Consequently, whether a feedforward or feedback control is utilized for appropriate postural responses may also be determined by the predictability and velocity of environmental stimuli. The study provides new insight into flexibility and adaptability in postural control. This information has implications for the design of rehabilitative protocols in neuromuscular control.
Keywords: Crossover; Detrended fluctuation analysis; Feedback; Feedforward; Fractal; Temporal correlation.