Estimating Immersed User States from Eye Movements: A Survey

The cognitive state of a person affects their task performance and learning success. This holds particularly for immersed states like flow and immersion. Hence, estimating immersed user states appears useful for adapting computer systems in order to maintain or achieve user flow or immersion. For practical use, the estimation method must provide continuously quantitative measurements in real-time in order to allow capturing short-term user state changes. In addition, the method should work as unobtrusive as possible in order to impose as less additional load on the user as possible. Tracking eye movement behavior using a remote eye-tracking device meets both requirements. Eye movement parameters are related to various cognitive processes and might therefore be useful for the estimation of immersed user states. This contribution gives an overview on the potential of the eye movement parameters fixation duration, pupil dilation, and spontaneous eye blink rate. As immersed states are complex cognitive states and as the three parameters provide complementary information it appears appropriate to capture all three parameters for the estimation. However, all three parameters are affected by multiple other factors besides the characteristics of the task. Hence, even the estimation using the combination appears to be a challenging issue.