Learning and reasoning about interruption

ABSTRACT

We present methods for inferring the cost of interrupting users based on multiple streams of events including information generated by interactions with computing devices, visual and acoustical analyses, and data drawn from online calendars. Following a review of prior work on techniques for deliberating about the cost of interruption associated with notifications, we introduce methods for learning models from data that can be used to compute the expected cost of interruption for a user. We describe the Interruption Workbench, a set of event-capture and modeling tools. Finally, we review experiments that characterize the accuracy of the models for predicting interruption cost and discuss research directions.

REFERENCES

• 1: Chickering, D. M., Heckerman, D. and Meek, C. (1997). A Bayesian approach to learning Bayesian networks with local structure. In Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, pp. 80--89.
• 2: Cutrell, E., Czerwinski, M. and Horvitz, E. (2001). Notification, Disruption, and Memory: Effects of Messaging Interruptions on Memory and Performance, Proceedings of Interact 2001, pp. 263--269.
• 3: Gillie, T. and Broadbent, D. (1989). What makes interruptions disruptive? A study of length, similarity and complexity Psychological Research, 50, 243--250.
• 4: Ken Hinckley , Jeff Pierce , Mike Sinclair , Eric Horvitz, Sensing techniques for mobile interaction, Proceedings of the 13th annual ACM symposium on User interface software and technology, p.91-100, November 06-08, 2000, San Diego, California, United States [doi>10.1145/354401.354417]
• 5: Horvitz, E., Jacobs, A., and Hovel, D. (1999). Attention-Sensitive Alerting. In: Proceedings of the Fifteenth Conference on Uncertainty and Artificial Intelligence, pp. 305--313.
• 6: Horvitz, E. Koch, P., Kadie, C. M. Jacobs, A. (2002). Coordinate: Probabilistic Forecasting of Presence and Availability. Proceedings of the Eighteenth Conference on Uncertainty and Artificial Intelligence, pp. 224--233.
• 7: Eric Horvitz , Carl Kadie , Tim Paek , David Hovel, Models of attention in computing and communication: from principles to applications, Communications of the ACM, v.46 n.3, March 2003 [doi>10.1145/636772.636798]
• 8: James M. Hudson , Jim Christensen , Wendy A. Kellogg , Thomas Erickson, "I'd be overwhelmed, but it's just one more thing to do": availability and interruption in research management, Proceedings of the SIGCHI conference on Human factors in computing systems: Changing our world, changing ourselves, April 20-25, 2002, Minneapolis, Minnesota, USA [doi>10.1145/503376.503394]
• 9: Scott Hudson , James Fogarty , Christopher Atkeson , Daniel Avrahami , Jodi Forlizzi , Sara Kiesler , Johnny Lee , Jie Yang, Predicting human interruptibility with sensors: a Wizard of Oz feasibility study, Proceedings of the SIGCHI conference on Human factors in computing systems, April 05-10, 2003, Ft. Lauderdale, Florida, USA [doi>10.1145/642611.642657]
• 10: D. Scott McCrickard , Richard Catrambone , C. M. Chewar , John T. Stasko, Establishing tradeoffs that leverage attention for utility: empirically evaluating information display in notification systems, International Journal of Human-Computer Studies, v.58 n.5, p.547-582, May 2003 [doi>10.1016/S1071-5819(03)00022-3]
• 11: McFarlane, D. (1999). Coordinating the interruption of people in human-computer interaction. Proceedings of Interact '99, pp. 295--303.
• 12: Oliver, N., Horvitz, E., Garg, A. (2002). Layered Representations for Recognizing Office Activity, Proceedings of ICMI 2002, pp. 3--8.
• 13: Ovsiankina, M. (1928). Die wiederaufnahme unterbrochener handlungen. Psychologische Forschung, 11:302--379.
• 14: Toyama, E. and Horvitz, E. (2000). Bayesian Modality Fusion: Probabilistic Integration of Multiple Vision Algorithms for Head Tracking. Proceedings of Fourth Asian Conference on Computer Vision.
• 15: Zeigarnik, B. (1929). Das behalten erledigter und unerledigter handlungen. Psychologische Forschung, 9:1--85.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.10 Vision and Scene Understanding

Additional Classification:
  J. Computer Applications
  J.4 SOCIAL AND BEHAVIORAL SCIENCES
      Subjects: Economics

General Terms:
Economics, Experimentation, Human Factors, Theory

Keywords:
cognitive models, divided attention, interruption, notifications