An Algorithm to Ascertain Critical Regions of Human Tracking Ability

A statistical algorithm is developed to study human tracking behavior in a precognitive tracking task. The algorithm presented here determines the point in time when a tracking task becomes too difficult for the human to follow. Consequently, different behavior responses are observed to occur. A decision rule based on a statistical test of normality is used to delineate the two regions of tracking behavior. The proof of convergence of this algorithm to a unique solution is given. Data from a good and poor tracker are analyzed using this algorithm to illustrate how to utilize the approach presented here.