Investigation into Crossover Regression in Compensatory Manual Tracking Tasks

Abstract

This paper investigates the crossover-regression phenomenon in compensatory manual-control tasks. The adjustment, between-subject variation, and accuracy of linear human-operator models are analyzed in detail. A theoretical investigation into closed-loop error minimization will be presented. Our main hypothesis was that crossover regression is caused by an operator's inability to sufficiently decrease the time delays required to limit forcing-function resonance. To test the hypothesis and explore the use of linear-operator models in regressed conditions, an experiment very similar to McRuer's landmark 1965 experiment was conducted. A comparison between regressive and nonregressive conditions revealed that crossover regression is indeed a strategy to reduce forcing-function resonance. The bandwidth of the forcing-function signal at which participants regressed their crossover frequency was found to vary considerably between participants. In regressed conditions, the between-subject variability in frequency-domain performance increased. Additionally, the operator control behavior became increasingly nonlinear, resulting in larger uncertainties and a higher between-subject variability in the linear-model parameter estimates.