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Notifications can have reduced interruption cost if delivered at moments of lower mental workload during task execution. Cognitive theorists have speculated that these moments occur at subtask boundaries. In this article, we empirically test this speculation by examining how workload changes during execution of goal-directed tasks, focusing on regions between adjacent chunks within the tasks, that is, the subtask boundaries. In a controlled experiment, users performed several interactive tasks while their pupil dilation, a reliable measure of workload, was continuously measured using an eye tracking system. The workload data was extracted from the pupil data, precisely aligned to the corresponding task models, and analyzed. Our principal findings include (i) workload changes throughout the execution of goal-directed tasks; (ii) workload exhibits transient decreases at subtask boundaries relative to the preceding subtasks; (iii) the amount of decrease tends to be greater at boundaries corresponding to the completion of larger chunks of the task; and (iv) different types of subtasks induce different amounts of workload. We situate these findings within resource theories of attention and discuss important implications for interruption management systems.

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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 tasks at predicted Best moments consistently caused less resumption lag and annoyance, and fostered more social attribution. Thus, the use of workload-aligned task models offers a systematic method for predicting opportune moments for interruption.

We present a novel system for notification management and report results from two studies testing its performance and impact. The system uses statistical models to realize defer-to-breakpoint policies for managing notifications. The first study tested how well the models detect three types of breakpoints within novel task sequences. Results show that the models detect breakpoints reasonably well, but struggle to differentiate their type. Our second study explored effects of managing notifications with our system on users and their tasks. Results showed that scheduling notifications at breakpoints reduces frustration and reaction time relative to delivering them immediately. We also found that the relevance of notification content determines the type of breakpoint at which it should be delivered. The core concept of scheduling notifications at breakpoints fits well with how users prefer notifications to be managed. This indicates that users would likely adopt the use of notification management systems in practice.