Predicting Human Control Adaptation from Statistical Variations in Tracking Error and Error Rate

This paper presents the results of an experiment that was performed to verify the 'supervisory control algorithm', a well-known model of human operator adaptation to changes in controlled element dynamics. This model proposes that human adaptive behavior is triggered once the magnitudes of the tracking error or error rate exceed certain decision region limits. In the experiment, a compensatory tracking task with a sudden transition in the controlled element dynamics, as also tested in other recent experiments, was performed by six skilled participants. In addition to performing the control task, participants had to indicate with a button press when they detected a controlled element transition. The results indicate that the published detection limits for the 'supervisory control algorithm' are too conservative for our experiment data, as measured detections could be related to error or error rate occurrences that exceeded 2-6 times their respective pre-transition standard deviations. The effectiveness of new detection limits proportional to these pre-transition standard deviations was tested. The best match to our experiment data was obtained with limits at 3.9σ, for which in only 9.38% and 11.5% of cases a (false positive) too early detection or a (false negative) missed detection occurred, respectively. Overall, these results demonstrate that human operator adaptation can indeed be effectively predicted from statistical variations in tracking error and error rate.