A Simplified Model of Motor Control

In general, control of movement is considered to be either cortical, spinal, or purely biomechanical and is studied separately at these levels. To achieve this separation when studying a particular level, variations that may be introduced by the other levels are generally either ignored or restricted. This restriction misrepresents the way movements occur in realistic scenarios and limits the ability to model movements in a biologically inspired manner. In this work, we propose a heuristic model for motor control that conceptually and mathematically accounts for the entire motor process, from target to endpoint. It simulates human arm motion and is able to represent functionally different motion properties by flexibly choosing more or less complex motion paths without built-in optimization or joint constraints. With a novel implementation of hierarchical control, this model successfully overcomes the problem of degrees of freedom in robotics. It can serve as a template for neurocomputational work that currently uses control architectures that do not mirror the human motor control process. The model itself also suggests a maximum threshold for delays in position feedback for effective movement, and that the primary role of position feedback in movement is to overcome the effects of environmental perturbations at the joint level. These findings can inform future efforts to develop biologically inspired motor control techniques for prosthetic devices.