In robotics, human-robot collaboration works best when robots are responsive to their human partners’ mental states. Human eye gaze has been used as a proxy for one such mental state: attention. While eye gaze can be a useful signal, for example enabling intent prediction, it is also a noisy one. Gaze serves several functions beyond attention, and thus recognizing what people are attending to from their eye gaze is a complex task. In this talk, I will discuss our research on modeling eye gaze to understand human attention in collaborative tasks such as shared manipulation and assisted driving.