Modeling Touch-based Menu Selection Performance of Blind Users via Reinforcement Learning

要旨

Although menu selection has been extensively studied in HCI, most existing studies have focused on sighted users, leaving blind users' menu selection under-studied. In this paper, we propose a computational model that can simulate blind users’ menu selection performance and strategies, including the way they use techniques like swiping, gliding, and direct touch. We assume that selection behavior emerges as an adaptation to the user's memory of item positions based on experience and feedback from the screen reader. A key aspect of our model is a model of long-term memory, predicting how a user recalls and forgets item position based on previous menu selections. We compare simulation results predicted by our model against data obtained in an empirical study with ten blind users. The model correctly simulated the effect of the menu length and menu arrangement on selection time, the action composition, and the menu selection strategy of the users.

著者
Zhi Li
Stony Brook University, Stony Brook, New York, United States
Yu-Jung Ko
Stony Brook University, Stony Brook, New York, United States
Aini Putkonen
Aalto University, Helsinki, Finland
Shirin Feiz
Stony Brook University, Stony Brook, New York, United States
Vikas Ashok
Old Dominion University, Norfolk, Virginia, United States
IV Ramakrishnan
Stony Brook University, Stony Brook, New York, United States
Antti Oulasvirta
Aalto University, Helsinki, Finland
Xiaojun Bi
Stony Brook University, Stony Brook, New York, United States
論文URL

https://doi.org/10.1145/3544548.3580640

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: Human AI Collaboration_B

Room Y05+Y06
6 件の発表
2023-04-26 01:35:00
2023-04-26 03:00:00