Variability , Symmetry , and Dynamics in Human

How humans and other animals control rhythmic behaviors, and locomotion in particular, is one of the grand challenges of neuroscience and biomechanics. And yet remarkably few studies address the fundamental control-systems modeling of locomotor control. This thesis attempts to address several pieces of this grand challenge through the development of experimental, theoretical, and computational tools. Specifically, we focus our attention on three key features of human rhythmic motor control, namely variability, symmetry, and dynamics. Variability: Little is known about how haptic sensing of discrete events, such as heel-strike in walking, in rhythmic dynamic tasks enhances behavior and performance. In order to discover the role of discrete haptic cues on rhythmic motor control performance, we study a virtual paddle juggling behavior. We show that haptic sensing of a force impulse to the hand at the moment of ball-paddle collision categorically improves performance over visual feedback alone, not by regulating the rate of convergence to steady state, but rather by reducing cycle-to-cycle variability. Symmetry: Neglecting evident characteristics of a system can certainly be a modii