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

翻译：利用机器学习模型从行为数据预测用户认知状态的方法已被应用于多种场景，包括预测用户在虚拟现实中执行选择操作的意图。我们提出了一种新技术，通过基于注视的意图模型动态调整驻留时间阈值，以辅助纯注视选择。我们构建了用户在算术任务中进行选择的数据集，并开发了意图预测模型（F1=0.94）。进一步设计了GazeIntent系统，根据意图模型输出自适应调整选择驻留时间，并面向新用户与回访用户开展了一项终端用户研究，使其完成不同选择频率的附加任务。为回访用户建立的个性化模型有效考虑了其先前经验，并获得63%用户的偏好。本研究为领域提供了三种方法：根据用户自适应调整驻留选择、随时间推移考虑用户经验积累、以及适应不同选择频率的任务需求。