  The 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
