Simplifying the complexities of maintaining balance

Insights are provided by simple closed-loop models of human postural control. In developing a quantitative model to help us understand the postural control system, one might be tempted to capture as much of the complexity as is known about each of the subsystems. However, this article will follow the approach of Occam´s Razor. That is, we begin with the simplest possible representation of each of the subsystems and only add complexity as necessary to be consistent with experimental data. For example, a control model with PD control and a positive force feedback loop provides a better explanation of the low-frequency dynamic behavior than the PID control model. Since both models have the same number of parameters, Occam´s Razor favors the positive force feedback model over the PID model or any variation on the PID model that includes additional parameters. While there is some experimental evidence that positive force feedback plays a role in some aspects of motor control its contribution to postural control is unknown. Our model that includes positive force feedback represents a quantitative hypothesis that motivates additional experiments to confirm, or refute the contribution of positive force feedback to human postural control and to investigate the dynamic properties of this feedback loop. An important feature clearly revealed by the model-based interpretation of experimental data is the ability of the human postural control system to alter its source of sensory orientation cues as environmental conditions change. Our relatively simple models allowed us to apply systems identification methods in order to estimate the relative contributions (sensory weights) of various sensory orientation cues in different environmental conditions However, our simple models do not predict how the sensory weights should change as a function of environmental conditions or provide insight into the neural mechanisms that cause these changes.