Investigating the effectiveness of mental workload as a predictor of opportune moments for interruption

Abstract

This work investigates the use of workload-aligned task models for predicting opportune moments for interruption. From models for several tasks, we selected boundaries with the lowest (Best) and highest (Worst) mental workload. We compared effects of interrupting primary tasks at these and Random moments on resumption lag, annoyance, and social attribution. Results show that interrupting at the Best moments consistently caused less resumption lag and annoyance, and fostered more social attribution. Results demonstrate that use of workload-aligned models offers a systematic method for predicting opportune moments.