Metrics of Motor Learning for Analyzing Movement Mapping in Virtual Reality

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Virtual reality (VR) techniques can modify how physical body movements are mapped to the virtual body. However, it is unclear how users learn such mappings and, therefore, how the learning process may impede interaction. To understand and quantify the learning of the techniques, we design new metrics explicitly for VR interactions based on the motor learning literature. We evaluate the metrics in three object selection and manipulation tasks, employing linear-translational and nonlinear-rotational gains and finger-to-arm mapping. The study shows that the metrics demonstrate known characteristics of motor learning similar to task completion time, typically with faster initial learning followed by more gradual improvements over time. More importantly, the metrics capture learning behaviors that task completion time does not. We discuss how the metrics can provide new insights into how users adapt to movement mappings and how they can help analyze and improve such techniques.
Original languageEnglish
Title of host publicationCHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems
Number of pages18
PublisherAssociation for Computing Machinery
Publication date2024
Article number724
ISBN (Electronic)979-8-4007-0330-0/24/05
Publication statusPublished - 2024
EventCHI '24: CHI Conference on Human Factors in Computing Systems - Honolulo HL, United States
Duration: 11 May 202416 May 2024


ConferenceCHI '24: CHI Conference on Human Factors in Computing Systems
LandUnited States
ByHonolulo HL

ID: 394385455