Adaptive estimation schemes for minimizing uncertainty in manual control tasks

Abstract

The present research has sought to expand our understanding of human information processing and control behavior in target tracking tasks. Specifically, it has focused on the problem of quantifying the human's "internal" model that characterizes his perception of short-term target motion, and on the development of con-commitant adaptive schemes for generating estimates of target velocity and acceleration using these models. A combined experimental and analytic program has studied simulated target tracking performance as modified by short periods (~ 1 sec) of target blanking. The blankings occur at pseudo-random times during a flyby. During the blanking period, human operator performance is governed almost entirely by his internal model representation of the target's motion. Ensemble data from blanking experiments has been used to suitably refine the Optimal Control manual tracking model, including the target submodel.