Virtual Environment (VE) user interfaces do not generally support personalized interaction or the ability to adapt to the user. Our research is developing nuance-oriented interfaces that take advantage of subtle clues that users give in their actions. To that end, we have performed five experiments with the goal of recognizing and applying nuances for the task of selection. We found evidence that users adapt their behavior to the feedback given by the system, rather than using a preconceived mental model of the environment. In addition, users' behavior can be modeled by a reward function. Lastly, users interact by trading off exploration with exploitation. We propose a method of modeling this behavior using machine learning as future work. / Master of Science