Computational Adaptation of XR Interfaces Through Interaction Simulation
This is an incremental contribution to XR interface design, addressing uncertainty in predictions for adaptive systems.
The paper tackles the problem of adaptive XR interfaces by proposing a computational model that simulates user interactions considering cognitive and motor costs, aiming to improve user experience and performance in menu selection tasks.
Adaptive and intelligent user interfaces have been proposed as a critical component of a successful extended reality (XR) system. In particular, a predictive system can make inferences about a user and provide them with task-relevant recommendations or adaptations. However, we believe such adaptive interfaces should carefully consider the overall \emph{cost} of interactions to better address uncertainty of predictions. In this position paper, we discuss a computational approach to adapt XR interfaces, with the goal of improving user experience and performance. Our novel model, applied to menu selection tasks, simulates user interactions by considering both cognitive and motor costs. In contrast to greedy algorithms that adapt based on predictions alone, our model holistically accounts for costs and benefits of adaptations towards adapting the interface and providing optimal recommendations to the user.