Computational Adaptation of XR Interfaces Through Interaction Simulation

TL;DR

This paper proposes a computational model for adapting XR interfaces by simulating user interactions considering cognitive and motor costs, aiming to enhance user experience and performance.

Contribution

It introduces a novel simulation-based model that holistically evaluates costs and benefits for adaptive XR interface recommendations.

Findings

Model improves adaptation decisions by considering interaction costs.

Simulation results show enhanced user experience in menu selection tasks.

Holistic cost-benefit analysis outperforms greedy algorithms.

Abstract

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.