Experimental and educational platforms for studying architecture and tradeoffs in human sensorimotor control

This paper describes several surprisingly rich but simple demos and a new experimental platform for human sensorimotor control research and also controls education. The platform safely simulates a canonical sensorimotor task of riding a mountain bike down a steep, twisting, bumpy trail using a standard display and inexpensive off-the-shelf gaming steering wheel with a force feedback motor. We use the platform to verify our theory, presented in a companion paper. The theory tells how component hardware speed-accuracy tradeoffs (SATs) in control loops impose corresponding SATs at the system level and how effective architectures mitigate the deleterious impact of hardware SATs through layering and “diversity sweet spots” (DSSs). Specifically, we measure the impacts on system performance of delays, quantization, and uncertainties in sensorimotor control loops, both within the subject's nervous system and added externally via software in the platform. This provides a remarkably rich test of the theory, which is consistent with all preliminary data. Moreover, as the theory predicted, subjects effectively multiplex specific higher layer planning/tracking of the trail using vision with lower layer rejection of unseen bump disturbances using reflexes. In contrast, humans multitask badly on tasks that do not naturally distribute across layers (e.g. texting and driving). The platform is cheap to build and easy to program for both research and education purposes, yet verifies our theory, which is aimed at closing a crucial gap between neurophysiology and sensorimotor control. The platform can be downloaded at https://github.com/Doyle-Lab/WheelCon

Connecting the Speed-Accuracy Trade-Offs in Sensorimotor Control and Neurophysiology Reveals Diversity Sweet Spots in Layered Control Architectures

Nervous systems sense, communicate, compute, and actuate movement using distributed components with trade-offs in speed, accuracy, sparsity, noise, and saturation. Nevertheless, the resulting control can achieve remarkably fast, accurate, and robust performance due to a highly effective layered control architecture. However, this architecture has received little attention from the existing research. This is in part because of the lack of theory that connects speed-accuracy trade-offs (SATs) in the components neurophysiology with system-level sensorimotor control and characterizes the overall system performance when different layers (planning vs. reflex layer) act work jointly. In thesis, we present a theoretical framework that provides a synthetic perspective of both levels and layers. We then use this framework to clarify the properties of effective layered architectures and explain why there exists extreme diversity across layers (planning vs. reflex layers) and within levels (sensorimotor versus neural/muscle hardware levels). The framework characterizes how the sensorimotor SATs are constrained by the component SATs of neurons communicating with spikes and their sensory and muscle endpoints, in both stochastic and deterministic models. The theoretical predictions are also verified using driving experiments. Our results lead to a novel concept, termed ``diversity sweet spots (DSSs)'': the appropriate diversity in the properties of neurons and muscles across layers and within levels help create systems that are both fast and accurate despite being built from components that are individually slow or inaccurate. At the component level, this concept explains why there are extreme heterogeneities in the neural or muscle composition. At the system level, DSSs explain the benefits of layering to allow extreme heterogeneities in speed and accuracy in different sensorimotor loops. Similar issues and properties also extend down to the cellular level in biology and outward to our most advanced network technologies from smart grid to the Internet of Things. We present our initial step in expanding our framework to that area and widely-open area of research for future direction