Narkar, AnishMichalak, JanPeacock, CandaceDavid-John, Brendan2024-06-042024-06-042024-05-28https://hdl.handle.net/10919/119255The use of ML models to predict a user’s cognitive state from behavioral data has been studied for various applications which includes predicting the intent to perform selections in VR.We developed a novel technique that uses gaze-based intent models to adapt dwell-time thresholds to aid gaze-only selection. A dataset of users performing selection in arithmetic tasks was used to develop intent prediction models (F1 = 0.94).We developed GazeIntent to adapt selection dwell times based on intent model outputs and conducted an end-user study with returning and new users performing additional tasks with varied selection frequencies. Personalized models for returning users effectively accounted for prior experience and were preferred by 63% of users. Our work provides the field with methods to adapt dwell-based selection to users, account for experience over time, and consider tasks that vary by selection frequency.application/pdfenCreative Commons Attribution 4.0 InternationalArticle - Refereed2024-06-01The author(s)https://doi.org/10.1145/3655600