21 Understanding Changes in Mental Workload during Execution of Goal-Directed Tasks and Its Application for Interruption Management BRIAN P. BAILEY and SHAMSI T. IQBAL University of Illinois at Urbana-Champaign 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 inter- active 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) dif- ferent 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. Categories and Subject Descriptors: H.1.2 [Models and Principles]: User/Machine Systems— Human information processing; H.5.2 [Information Interfaces and Presentation]: User Inter- faces—Evaluation/methodology, user-centered design General Terms: Design, Experimentation, Human Factors, Measurement Additional Key Words and Phrases: Attention, interruption, workload, pupil size, task models, user studies ACM Reference Format: Bailey, B. P. and Iqbal, S. T. 2008. Understanding changes in mental workload during execution of goal-directed tasks and its application for interruption management. ACM Trans. Comput.- Hum. Interact. 14, 4, Article 21 (January 2008), 28 pages. DOI = 10.1145/1314683.1314689 http://doi.acm.org/10.1145/1314683.1314689 This research was supported in part by the National Science Foundation (NSF) under award no. IIS-0534462. Authors’ address: Department of Computer Science, University of Illinois, Urbana, IL 61801; email: {bpbailey;siqbal}@uiuc.edu. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or permission@acm.org. C  2008 ACM 1073-0616/2008/01-ART21 $5.00 DOI 10.1145/1314683.1314689 http://doi.acm.org/ 10.1145/1314683.1314689 ACM Transactions on Computer-Human Interaction, Vol. 14, No. 4, Article 21, Publication date: January 2008.