Leveraging Characteristics of Task Structure to Predict the Cost of Interruption

A challenge in building interruption reasoning systems is to compute an accurate cost of interruption (COI). Prior work has used non task-specific cues to predict COI, but ignore characteristics related to the /structure/ of a task. This work investigates how well characteristics of task structure can predict COI, as objectively measured by resumption lag. In an experiment, users were interrupted during task execution at various subtask boundaries to collect a large sample of resumption lag values. Statistical methods were employed to create a parsimonious model that uses characteristics of task structure to predict COI. A subsequent experiment with different tasks showed that the model can predict COI with reasonably high accuracy. Our model can be expediently applied to many goal-directed tasks, allowing systems to make more effective decisions about when to interrupt