Interruptions in the middle of a task have considerable costs. The objective of this study was to develop a system that postpones interruptions when they occur in periods of high workload. In Experiment 1, an air traffic control (ATC) simulator was presented with varying working memory demands. Pupil data were used to train a range of machine-learning classifiers to distinguish between high and low workload moments. The Gradient Boosted Tree (GBT) provided the best predictions. In Experiment 2, this classifier was used to develop a real-time interruption management system (IMS). The role of the IMS was to predict high and low workload and to postpone interruptions to the next low workload moment. To examine the IMS’s performance, its interruptions were compared to random interruptions. Results showed that the IMS successfully identified high and low workload moments with 76% accuracy, and postponed interruptions to the next low workload moment.
Get full access to this article
View all access options for this article.
References
1.
AltmannE. M.TraftonJ. G. (2007). Timecourse of recovery from task interruption: Data and a model. Psychonomic Bulletin & Review, 14(6), 1079–1084. https://doi.org/10.3758/BF03193094
2.
AltmannE. M.TraftonJ. G.HambrickD. Z. (2014). Momentary interruptions can derail the train of thought. Journal of Experimental Psychology: General, 143(1), 215–226. https://doi.org/10.1037/a0030986
3.
BeattyJ. (1982). Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychological Bulletin, 91(2), 276–292. https://doi.org/10.1037/0033-2909.91.2.276
4.
CutrellE. B.CzerwinskiM.HorvitzE. (2000). Effects of instant messaging interruptions on computing tasks. In CHI ’00 extended abstracts on Human factors in computing systems - CHI ’00 (p. 99). The Hague, The Netherlands: ACM Press. https://doi.org/10.1145/633292.633351
5.
IqbalS. T.AdamczykP. D.ZhengX. S.BaileyB. P. (2005). Towards an index of opportunity: understanding changes in mental workload during task execution. In Proceedings of the SIGCHI conference on Human factors in computing systems - CHI ’05 (p. 311). Portland, Oregon, USA: ACM Press. https://doi.org/10.1145/1054972.1055016
6.
IqbalS. T.BaileyB. P. (2005). Investigating the effectiveness of mental workload as a predictor of opportune moments for interruption. In CHI ’05 extended abstracts on Human factors in computing systems - CHI ’05 (p. 1489). Portland, OR, USA: ACM Press. https://doi.org/10.1145/1056808.1056948
7.
IqbalS. T.BaileyB. P. (2007). Understanding and developing models for detecting and differentiating breakpoints during interactive tasks. In Proceedings of the SIGCHI conference on Human factors in computing systems - CHI ’07 (p. 697). San Jose, California, USA: ACM Press. https://doi.org/10.1145/1240624.1240732
KaratekinC. (2004). Development of attentional allocation in the dual task paradigm. International Journal of Psychophysiology, 52(1), 7–21. https://doi.org/10.1016/j.ijpsycho.2003.12.002
10.
KatidiotiI.BorstJ. P.Bierens de HaanDouwe J.PeppingTamaravan VugtMarieke K.TaatgenNiels A. (2016). Interrupted by Your Pupil: An Interruption Management System Based on Pupil Dilation. INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION, 32(10), 791–801. https://doi.org/10.1080/10447318.2016.1198525
11.
MarkG.GonzalezV. M.HarrisJ. (2005). No task left behind?: examining the nature of fragmented work. In Proceedings of the SIGCHI conference on Human factors in computing systems - CHI ’05 (p. 321). Portland, Oregon, USA: ACM Press. https://doi.org/10.1145/1054972.1055017
12.
MonkC. A.Boehm-DavisD. A.MasonG.TraftonJ. G. (2004). Recovering From Interruptions: Implications for Driver Distraction Research. Human Factors: The Journal of the Human Factors and Ergonomics Society, 46(4), 650–663. https://doi.org/10.1518/hfes.46.4.650.56816
13.
RafiqiS.WangwiwattanaC.FernandezE.NairS.LarsonE. (2015). Work-in-Progress, PupilWare-M: Cognitive Load Estimation Using Unmodified Smartphone Cameras. In 2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems (pp. 645–650). Dallas, TX, USA: IEEE. https://doi.org/10.1109/MASS.2015.31
14.
SalvucciD. D.BogunovichP. (2010). Multitasking and monotasking: the effects of mental workload on deferred task interruptions. In Proceedings of the 28th international conference on Human factors in computing systems - CHI ’10 (p. 85). Atlanta, Georgia, USA: ACM Press. https://doi.org/10.1145/1753326.1753340
15.
ShaposhnikH.BorstJ. P.TaatgenN. A. (2018). Predicting the Optimal Time for Interruption using Pupillary Data and Classification (pp. 2479–2484). Presented at the the 40th Annual Meeting of the Cognitive Science Society, Madison.
16.
ZemblysR.NiehorsterD. C.KomogortsevO.HolmqvistK. (2018). Using machine learning to detect events in eye-tracking data. Behavior Research Methods, 50(1), 160–181. https://doi.org/10.3758/s13428-017-0860-3
17.
ZügerM.FritzT. (2015). Interruptibility of Software Developers and its Prediction Using Psycho-Physiological Sensors. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI ’15 (pp. 2981–2990). Seoul, Republic of Korea: ACM Press. https://doi.org/10.1145/2702123.2702593
18.
ZügerM.MüllerS. C.MeyerA. N.FritzT. (2018). Sensing Interruptibility in the Office: A Field Study on the Use of Biometric and Computer Interaction Sensors. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI ’18 (pp. 1–14). Montreal QC, Canada: ACM Press. https://doi.org/10.1145/3173574.3174165
