Leveraging characteristics of task structure to predict the cost of interruption
Open accessAbstract
A challenge in building interruption reasoning systems is to compute an accurate cost of interruption (COI). Prior work has used interface events and other 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 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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cost of multitasking is dependent upon the level of problem representation, task difficulty, and amount of mental information carried over from one task to another.
“Though people with high WMC can do better in the divided attention task in laboratory experiments, the cost of multitasking is dependent upon the level of problem representation, task difficulty, and amount of mental information carried over from one task to another (Iqbal & Bailey, 2006).”
From The indirect relationship of media multitasking self-efficacy on learning performance within the personal learning environment: Implications from the mechanism of perceived attention problems and self-regulation strategies (2017) · cites this paper as Iqbal & Bailey, 2006
The Conclusion (page unavailable) identifies boundary level, carryover, and difficulty of the next subtask as predictors of the cost of interruption. The Identify Candidate Factors section defines carryover as information maintained across a subtask boundary, not information transferred from one task to another. The paper studies interruptions at subtask boundaries, so the claim extends its findings to multitasking more generally. (LLM, conf=0.84)
Experimental — accuracy not yet measuredUsers take less time to resume a task when interrupted at low-workload moments.
“These studies confirmed that users take less time to resume a task when interrupted at low-workload moments [25,26] (see [32] for an additional example).”
From What Makes Interruptions Disruptive?: A Process-Model Account of the Effects of the Problem State Bottleneck on Task Interruption and Resumption (2015) · cites this paper as [26]
It was shown in multiple studies that it is better to be interrupted between tasks [25,26,34] than in the middle of a task.
“It was shown in multiple studies that it is better to be interrupted between tasks [25,26,34] than in the middle of a task.”
From What Makes Interruptions Disruptive?: A Process-Model Account of the Effects of the Problem State Bottleneck on Task Interruption and Resumption (2015) · cites this paper as [26]
The statistical model of [26] showed that Task Level, Difficulty of Next Subtask, and Carry-Over between Subtasks were the most important predictors of interruption disruptiveness.
“which showed that Task Level, Difficulty of Next Subtask, and Carry-Over between Subtasks, were the most important predictors of interruption disruptiveness.”
From What Makes Interruptions Disruptive?: A Process-Model Account of the Effects of the Problem State Bottleneck on Task Interruption and Resumption (2015) · cites this paper as [26]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Carayannis, E. G. (2013) · Springer eBooks · doi:10.1007/978-1-4614-3858-8_100431 · Cited by 4,818 · see citing works
Designing the user interface strategies for effective human-computer interaction
Shneiderman, B. (1998) · SIGB · doi:10.1145/25065.950626 · Cited by 4,680 · see citing works
The Psychology of Human-Computer Interaction
Card, S. K., Moran, T. P., & Newell, A. (2018) · CRC Press eBooks · doi:10.1201/9780203736166 · Cited by 4,103 · see citing works
Cognitive Skills and Their Acquisition
Anderson, J. R. (2013) · Psychology Press eBooks · doi:10.4324/9780203728178 · Cited by 2,926 · see citing works
Multiple resources and performance prediction
Wickens, C. D. (2002) · Theoretical Issues in Ergonomics Science · doi:10.1080/14639220210123806 · Cited by 2,621 · cited 7× · see citing works
“Specifically, systems need to consider subtask boundaries since they are posited to reflect lower workload [28], which typically results in lower COI [37], and since boundaries are present in almost every goal-directed task [5].”
BackgroundMethodInfluentialUser Centered System Design: New Perspectives on Human-Computer Interaction
Anderson, N. S., Norman, D. A., & Draper, S. W. (1988) · The American Journal of Psychology · doi:10.2307/1422802 · Cited by 1,851 · see citing works
On the economy of the human-processing system.
Navon, D., & Gopher, D. (1979) · Psychological Review · doi:10.1037/0033-295x.86.3.214 · Cited by 1,668 · see citing works
(1987) · Andalas University Repository (Andalas University) · Cited by 1,584 · see citing works
Mechanisms of skill acquisition and the law of practice
Newell, A., & Rosenbloom, P. S. (2018) · Figshare · doi:10.1184/r1/6607199.v1 · Cited by 1,365 · see citing works
Executive control of cognitive processes in task switching
Rubinstein, J. S., Meyer, D. E., & Evans, J. E. (2001) · Journal of Experimental Psychology: Human Perception and Performance · doi:10.1037//0096-1523.27.4.763 · Cited by 1,207 · see citing works
The psychology of human-computer interaction
Huesmann, L. R., Card, S. K., Moran, T. P., & Newell, A. (1984) · The American Journal of Psychology · doi:10.2307/1422176 · Cited by 1,177 · see citing works
Executive control of cognitive processes in task switching.
Rubinstein, J. S., Meyer, D. E., & Evans, J. E. (2001) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/0096-1523.27.4.763 · Cited by 873 · see citing works
A diary study of task switching and interruptions
Czerwinski, M., Horvitz, E., & Wilhite, S. (2004) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/985692.985715 · Cited by 830 · see citing works
Perceiving, remembering, and communicating structure in events.
Zacks, J. M., Tversky, B., & Iyer, G. (2001) · Journal of Experimental Psychology: General · doi:10.1037/0096-3445.130.1.29 · Cited by 640 · see citing works
Bailey, B. P., & Konstan, J. A. (2006) · Computers in Human Behavior · doi:10.1016/j.chb.2005.12.009 · Cited by 600 · see citing works
Self-citationThe Scope and Importance of Human Interruption in Human-Computer Interaction Design
McFarlane, D. C., & Latorella, K. A. (2002) · Human–Computer Interaction · doi:10.1207/s15327051hci1701_1 · Cited by 536 · see citing works
If not now, when?: the effects of interruption at different moments within task execution
Adamczyk, P. D., & Bailey, B. P. (2004) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/985692.985727 · Cited by 531 · see citing works
Self-citationTrafton, J. G., Altmann, E. M., Brock, D. P., & Mintz, F. E. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00023-5 · Cited by 457 · see citing works
Suess, A. P. (1997) · Journal of interactive instruction development · Cited by 443 · see citing works
Temporal factors in mental work: Effects of interrupted activities
Zijlstra, F. R. H., Roe, R. A., Leonora, A. B., & Krediet, I. (1999) · Journal of Occupational and Organizational Psychology · doi:10.1348/096317999166581 · Cited by 429 · see citing works
Attention-Sensitive AlertingPreprint
Horvitz, E., Jacobs, A., & Hovel, D. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.6707 · Cited by 317 · see citing works
Cockpit Checklists: Concepts, Design, and Use
Degani, A., & Wiener, E. L. (1993) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/001872089303500209 · Cited by 317 · see citing works
Predicting human interruptibility with sensors: a Wizard of Oz feasibility study
Hudson, S., Fogarty, J., Atkeson, C., Avrahami, D., Forlizzi, J., Kiesler, S., Lee, J., & Yang, J. (2003) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/642611.642657 · Cited by 295 · see citing works
Predictive human performance modeling made easy
John, B. E., Prevas, K. C., Salvucci, D. D., & Koedinger, K. R. (2004) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/985692.985750 · Cited by 289 · see citing works
Learning and reasoning about interruption
Horvitz, E., & Apacible, J. (2003) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/958432.958440 · Cited by 275 · see citing works
Psychological Issues in Support of Multiple Activities
MIYATA, Y., & NORMAN, D. A. (1986) · User Centered System Design · doi:10.1201/b15703-13 · Cited by 245 · see citing works
TaskTracer: a desktop environment to support multi-tasking knowledge workers
Dragunov, A. N., Dietterich, T. G., Johnsrude, K., McLaughlin, M., Li, L., & Herlocker, J. L. (2005) · Proceedings of the 10th international conference on Intelligent user interfaces · doi:10.1145/1040830.1040855 · Cited by 240 · see citing works
"I'd be overwhelmed, but it's just one more thing to do"
Hudson, J. M., Christensen, J. E., Kellogg, W. A., & Erickson, T. D. (2002) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/503376.503394 · Cited by 236 · see citing works
Instant Messaging and Interruption: Influence of Task Type on Performance
Czerwinski, M. P., Cutrell, E., & Horvitz, E. (2000) · Cited by 227 · see citing works
Iqbal, S. T., & Bailey, B. P. (2005) · CHI '05 Extended Abstracts on Human Factors in Computing Systems · doi:10.1145/1056808.1056948 · Cited by 203 · see citing works
Self-citationTowards an index of opportunity: understanding changes in mental workload during task execution
Iqbal, S. T., Adamczyk, P. D., Zheng, X. S., & Bailey, B. P. (2005) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1054972.1055016 · Cited by 196 · see citing works
Self-citation(2000) · Cited by 192 · see citing works
(1999) · International Conference on Human-Computer Interaction · Cited by 168 · see citing works
Tradeoffs in displaying peripheral information
Maglio, P. P., & Campbell, C. S. (2000) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/332040.332438 · Cited by 168 · see citing works
The cost of email interruption
Jackson, T., Dawson, R. J., & Wilson, D. (2001) · Journal of Systems and Information Technology · doi:10.1108/13287260180000760 · Cited by 161 · see citing works
(2004) · eScholarship (California Digital Library) · Cited by 160 · see citing works
BusyBody: creating and fielding personalized models of the cost of interruption
Horvitz, E., Koch, P., & Apacible, J. (2004) · Proceedings of the 2004 ACM conference on Computer supported cooperative work · doi:10.1145/1031607.1031690 · Cited by 150 · see citing works
Effects of instant messaging interruptions on computing tasks
Cutrell, E. B., Czerwinski, M. P., & Horvitz, E. (2000) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/633292.633351 · Cited by 143 · see citing works
Examining the robustness of sensor-based statistical models of human interruptibility
Fogarty, J. A., Hudson, S. E., & Lai, J. C. (2004) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/985692.985719 · Cited by 134 · see citing works
McCrickard, D. S., Catrambone, R., Chewar, C. M., & Stasko, J. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00022-3 · Cited by 123 · see citing works
The Attentional Costs of Interrupting Task Performance at Various Stages
Monk, C. A., Boehm-Davis, D. A., & Trafton, J. G. (2002) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/154193120204602210 · Cited by 114 · see citing works
Examining task engagement in sensor-based statistical models of human interruptibility
Fogarty, J., Ko, A. J., Aung, H. H., Golden, E., Tang, K. P., & Hudson, S. E. (2005) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1054972.1055018 · Cited by 111 · see citing works
Supporting cognitive models as users
Ritter, F. E., Baxter, G. D., Jones, G., & Young, R. M. (2000) · ACM Transactions on Computer-Human Interaction · doi:10.1145/353485.353486 · Cited by 95 · see citing works
A framework for specifying and monitoring user tasks
Bailey, B. P., Adamczyk, P. D., Chang, T. Y., & Chilson, N. A. (2006) · Computers in Human Behavior · doi:10.1016/j.chb.2005.12.011 · Cited by 73 · see citing works
Self-citationProceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
Costabile, M. F., & Paternò, F. (2005) · Cited by 41 · see citing works
Supporting efficient development of cognitive models at multiple skill levels
Tollinger, I., Lewis, R. L., McCurdy, M., Tollinger, P., Vera, A. H., Howes, A., & Pelton, L. (2005) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1054972.1055029 · Cited by 25 · see citing works
Visual Cues to Reduce Errors in a Routine Procedural Task
Chung, P., & Byrne, M. D. (2004) · eScholarship (California Digital Library) · Cited by 18 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Bailey, B. P., & Iqbal, S. T. (2008) · ACM Transactions on Computer-Human Interaction · doi:10.1145/1314683.1314689 · Cited by 325 · see citing works
Self-citationDisruption and recovery of computing tasks: field study, analysis, and directions
Iqbal, S. T., & Horvitz, E. (2007) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1240624.1240730 · Cited by 300 · see citing works
Self-citationSalvucci, D. D., Taatgen, N. A., & Borst, J. P. (2009) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1518981 · Cited by 251 · see citing works
Effects of intelligent notification management on users and their tasks
Iqbal, S. T., & Bailey, B. P. (2008) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357070 · Cited by 197 · see citing works
Self-citationBorst, J. P., Taatgen, N. A., & van Rijn, H. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702156 · Cited by 156 · see citing works
Wu, J.-Y. (2017) · Computers & Education · doi:10.1016/j.compedu.2016.10.010 · Cited by 104 · see citing works
Interruptibility of Software Developers and its Prediction Using Psycho-Physiological Sensors
Züger, M., & Fritz, T. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702593 · Cited by 96 · see citing works
Goyal, N., & Fussell, S. R. (2017) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3130917 · Cited by 46 · see citing works
Study of user interruptibility estimation based on focused application switching
Tanaka, T., & Fujita, K. (2011) · Proceedings of the ACM 2011 conference on Computer supported cooperative work · doi:10.1145/1958824.1958954 · Cited by 32 · see citing works
Context-dependent awareness support in open collaboration environments
Ardissono, L., & Bosio, G. (2012) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-011-9100-1 · Cited by 20 · see citing works
BUILDING USER TASK SPACE BY MINING TEMPORALLY PROXIMATE DESKTOP ACTIONS
Mirza, H. T., Chen, L., Majid, A., & Chen, G. (2011) · Cybernetics and Systems · doi:10.1080/01969722.2011.634675 · Cited by 2 · see citing works
- Authors
- Iqbal, S. T., & Bailey, B. P.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2006-04-22
- Pages
- 741-750
- OpenAlex
- W1980358571
- Cited by
- 138
- References
- 47
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 6 · 2023: 4 · 2022: 6 · 2021: 5 · 2020: 5 · 2019: 8 · 2018: 7 · 2017: 16 · 2016: 10 · 2015: 13 · 2014: 8 · 2013: 7 · 2012: 5
- FWCI
- 5.95
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) (submittedVersion)
- Funders
- National Science Foundation
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Personal Information Management and User Behavior
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- interruption cost, task structure, task boundaries, goal-directed tasks, human-computer interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
