A4 Article in conference proceedings
Supporting Task Switching with Reinforcement Learning (2024)

Lingler, A., Talypova, D., Jokinen, J. P.P., Oulasvirta, A., & Wintersberger, P. (2024). Supporting Task Switching with Reinforcement Learning. In F. F. Mueller, P. Kyburz, J. R. Williamson, C. Sas, M. L. Wilson, P. T. Dugas, & I. Shklovski (Eds.), CHI '24 : Proceedings of the CHI Conference on Human Factors in Computing Systems (Article 82). ACM. https://doi.org/10.1145/3613904.3642063

JYU authors or editors

Publication details

All authors or editors: Lingler, Alexander; Talypova, Dinara; Jokinen, Jussi P. P.; Oulasvirta, Antti; Wintersberger, Philipp

Parent publication: CHI '24 : Proceedings of the CHI Conference on Human Factors in Computing Systems

Parent publication editors: Mueller, Florian Floyd; Kyburz, Penny; Williamson, Julie R.; Sas, Corina; Wilson, Max L.; Dugas, Phoebe Toups; Shklovski, Irina

Conference:

Place and date of conference: Honolulu, USA, 11.-16.5.2024

eISBN: 979-8-4007-0330-0

Publication year: 2024

Publication date: 11/05/2024

Article number: 82

Publisher: ACM

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1145/3613904.3642063

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/94909

Abstract

Attention management systems aim to mitigate the negative effects of multitasking. However, sophisticated real-time attention management is yet to be developed. We present a novel concept for attention management with reinforcement learning that automatically switches tasks. The system was trained with a user model based on principles of computational rationality. Due to this user model, the system derives a policy that schedules task switches by considering human constraints such as visual limitations and reaction times. We evaluated its capabilities in a challenging dual-task balancing game. Our results confirm our main hypothesis that an attention management system based on reinforcement learning can significantly improve human performance, compared to humans’ self-determined interruption strategy. The system raised the frequency and difficulty of task switches compared to the users while still yielding a lower subjective workload. We conclude by arguing that the concept can be applied to a great variety of multitasking settings.

Keywords: machine learning; reinforcement learning; human-computer interaction; attention; tasks; stoppage; laboratory research; quantitative research

Free keywords: interruption; notification; task switching; machine learning; artifact or system; lab study; quantitative methods

Contributing organizations

Ministry reporting: Yes

Preliminary JUFO rating: 3