skip to main content
10.1145/1054972.1055016acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
Article

Towards an index of opportunity: understanding changes in mental workload during task execution

Published:02 April 2005Publication History

ABSTRACT

To contribute to systems that reason about human attention, our work empirically demonstrates how a user's mental workload changes during task execution. We conducted a study where users performed interactive, hierarchical tasks while mental workload was measured through the use of pupil size. Results show that (i) different types of subtasks impose different mental workload, (ii) workload decreases at subtask boundaries, (iii) workload decreases more at boundaries higher in a task model and less at boundaries lower in the model, (iv) workload changes among subtask boundaries within the same level of a task model, and (v) effective understanding of why changes in workload occur requires that the measure be tightly coupled to a validated task model. From the results, we show how to map mental workload onto a computational Index of Opportunity that systems can use to better reason about human attention.

References

  1. Adamczyk, P.D. and B.P. Bailey. If Not Now When? The Effects of Interruptions at Various Moments within Task Execution. CHI, 2004, 271--278. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Bailey, B.P., J.A. Konstan and J.V. Carlis. The Effects of Interruptions on Task Performance, Annoyance, and Anxiety in the User Interface. INTERACT, 2001, 593--601.Google ScholarGoogle Scholar
  3. Beatty, J. Task-Evoked Pupillary Responses, Processing Load, and the Structure of Processing Resources. Psychological Bulletin, 91 (2), 276--292, 1982.Google ScholarGoogle ScholarCross RefCross Ref
  4. Bradshaw, J.L. Pupil Size as a Measure of Arousal During Information Processing. Nature, 216, 515--516, 1967.Google ScholarGoogle ScholarCross RefCross Ref
  5. Card, S., T. Moran and A. Newell. The Psychology of Human-Computer Interaction. Lawrence Erlbaum Associates, 1983. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Cutrell, E., M. Czerwinski and E. Horvitz. Notification, Disruption and Memory: Effects of Messaging Interruptions on Memory and Performance. INTERACT, 2001, 263--269.Google ScholarGoogle Scholar
  7. Czerwinski, M., E. Cutrell and E. Horvitz. Instant Messaging and Interruption: Influence of Task Type on Performance. Proc. OZCHI, 2000, 356--361.Google ScholarGoogle Scholar
  8. Czerwinski, M., E. Cutrell and E. Horvitz. Instant Messaging: Effects of Relevance and Timing. In People and Computers XIV: Proceedings of HCI, 2000, 71--76.Google ScholarGoogle Scholar
  9. Gillie, T. and D. Broadbent. What Makes Interruptions Disruptive? A Study of Length, Similarity, and Complexity. Psychological Research, 50, 243--250, 1989.Google ScholarGoogle ScholarCross RefCross Ref
  10. Hess, E.H. and J.M. Polt. Pupil Size in Relation to Mental Activity During Simple Problem Solving. Science, 132, 1190--1192, 1964.Google ScholarGoogle ScholarCross RefCross Ref
  11. Hoecks, B. and W. Levelt. Pupillary Dilation as a Measure of Attention: A Quantitative System Analysis. Behavior Research Methods, Instruments, & Computers, 25, 16--26, 1993.Google ScholarGoogle Scholar
  12. Horvitz, E. and J. Apacible. Learning and Reasoning About Interruption. In Proceedings of the Fifth ACM International Conference on Multimodal Interfaces, 2003, 20--27. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Horvitz, E., J. Breese, D. Heckerman, D. Hovel and K. Rommelse. The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. Proc. Uncertainty in Artificial Intelligence, 1998, 256--265. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Horvitz, E., A. Jacobs and D. Hovel. Attention-Sensitive Alerting. Proc. Uncertainty in Artificial Intelligence, 1999, 305--313. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Hudson, S.E., J. Fogarty, C.G. Atkeson, D. Avrahami, J. Forlizzi, S. Kiesler, J.C. Lee and J. Yang. Predicting Human Interruptibility with Sensors: A Wizard of Oz Feasibility Study. CHI, 2003, 257--264. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Hytintk, J., J. Tommola and A. Alaja. Pupil Dilation as a Measure of Processing Load in Simultaneous Interpretation and Other Language Tasks. The Quarterly Journal of Experimental Psychology, 48A (3), 598--612, 1995.Google ScholarGoogle Scholar
  17. Iqbal, S.T., X.S. Zheng and B.P. Bailey. Task-Evoked Pupillary Response to Mental Workload in Human-Computer Interaction. CHI, 2004, 1477--1480. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Jackson, T.W., R.J. Dawson and D. Wilson. The Cost of Email Interruption. Journal of Systems and Information Technology, 5 (1), 81--92, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  19. John, B., A. Vera, M. Matessa, M. Freed and R. Remington. Automating CPM-Goms. CHI, 2002, 147--154. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Juris, M. and M. Velden. The Pupillary Response to Mental Overload. Physiological Psychology, 5 (4), 421--424, 1977.Google ScholarGoogle ScholarCross RefCross Ref
  21. Kahneman, D. Pupillary Responses in a Pitch-Discrimination Task. Perception & Psychophysics, 2, 101--105, 1967.Google ScholarGoogle ScholarCross RefCross Ref
  22. Kramer, A.F. Physiological Metrics of Mental Workload: A Review of Recent Progress. In Damos, D.L. ed. Multiple-Task Performance, Taylor and Francis, London, 1991, 279--328.Google ScholarGoogle Scholar
  23. Kreifeldt, J.G. and M.E. McCarthy. Interruption as a Test of the User-Computer Interface. In Proceedings of the 17th Annual Conference on Manual Control, Jet Propulsion Laboratory, California Institute of Technology, JPL Publication 81-95, 1981, 655--667.Google ScholarGoogle Scholar
  24. Maes, P. Agents That Reduce Work and Information Overload. Communications of the ACM, 37 (7), 30--40, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Marshall, S.P. New Techniques for Evaluating Innovative Interfaces with Eye Tracking. UIST, 2003, Keynote Talk. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. McFarlane, D.C. Coordinating the Interruption of People in Human-Computer Interaction. INTERACT, 1999, 295--303.Google ScholarGoogle Scholar
  27. Miyata, Y. and D.A. Norman. The Control of Multiple Activities. In Norman, D.A. and Draper, S.W. (eds.) User Centered System Design: New Perspectives on Human-Computer Interaction, Lawrence Erlbaum Associates, Hillsdale, NJ, 1986.Google ScholarGoogle Scholar
  28. Nakayama, M. and K. Takahashi. The Act of Task Difficulty and Eye-Movement Frequency for the Ocul-Motor Indices. In Proceedings of Eye Tracking Research and Application, 2002, 37--42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Picard, R.W. Affective Computing. MIT Press, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Rowe, D.W., J. Sibert and D. Irwin. Heart Rate Variability: Indicator of User State as an Aid to Human-Computer Interaction. CHI, 1998, 480--487. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Shell, J.S., T. Selker and R. Vertegaal. Interacting with Groups of Computers. CACM, 46 (3), 40--46, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Speier, C., J.S. Valacich and I. Vessey. The Influence of Task Interruption on Individual Decision Making: An Information Overload Perspective. Decision Sciences, 30 (2), 337--360, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  33. Takahashi, K., M. Nakayama and Y. Shimizu. The Response of Eye-Movement and Pupil Size to Audio Instruction While Viewing a Moving Target. Proc. Eye Tracking Research & Applications, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Tobii-Systems. http://www.tobii.se/Google ScholarGoogle Scholar
  35. Wilson, G.M. and M.A. Sasse. The Head or the Heart?: Measuring the Impact of Media Quality. CHI, 2000, 117--118. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Zijlstra, F.R.H., R.A. Roe, A.B. Leonora and I. Krediet. Temporal Factors in Mental Work: Effects of Interrupted Activities. Journal of Occupational and Organizational Psychology, 72, 163--185, 1999.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Towards an index of opportunity: understanding changes in mental workload during task execution

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        CHI '05: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
        April 2005
        928 pages
        ISBN:1581139985
        DOI:10.1145/1054972

        Copyright © 2005 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 2 April 2005

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • Article

        Acceptance Rates

        CHI '05 Paper Acceptance Rate93of372submissions,25%Overall Acceptance Rate6,199of26,314submissions,24%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader