Heads-Up Multitasker: Simulating Attention Switching On Optical Head-Mounted Displays

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openAccess

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A4 Artikkeli konferenssijulkaisussa

Date

2024-05-11

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en

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18

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Abstract

Optical Head-Mounted Displays (OHMDs) allow users to read digital content while walking. A better understanding of how users allocate attention between these two tasks is crucial for improving OHMD interfaces. This paper introduces a computational model for simulating users’ attention switches between reading and walking. We model users’ decision to deploy visual attention as a hierarchical reinforcement learning problem, wherein a supervisory controller optimizes attention allocation while considering both reading activity and walking safety. Our model simulates the control of eye movements and locomotion as an adaptation to the given task priority, design of digital content, and walking speed. The model replicates key multitasking behaviors during OHMD reading while walking, including attention switches, changes in reading and walking speeds, and reading resumptions.

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Keywords

computational rationality, multitasking, bounded optimal control, heads-up computing, deep reinforcement learning

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Citation

Bai, Y, Ikkala, A, Oulasvirta, A, Zhao, S, Wang, L J, Yang, P & Xu, P 2024, Heads-Up Multitasker: Simulating Attention Switching On Optical Head-Mounted Displays . in F F Mueller, P Kyburz, J R Williamson, C Sas, M L Wilson, P Toups Dugas & I Shklovski (eds), CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems ., 79, ACM, ACM SIGCHI Annual Conference on Human Factors in Computing Systems, Honolulu, Hawaii, United States, 11/05/2024 . https://doi.org/10.1145/3613904.3642540