The Journal of Eye Movement Research has published the paper "Eye-Based Recognition of User Traits and States—A Systematic State-of-the-Art Review". The paper is co-authored by Moritz Langner, Peyman Toreini, and Alexander Maedche. 

In this paper, we systematically reviewing and synthesizing 

the existing literature on the machine-learning-based recognition of user traits and states using eye-tracking data. The data synthesis included a conceptual framework that covered the task, context, technology and data processing, and recognition targets. A total of 90 studies were included that encompassed a variety of tasks (e.g., visual, driving, learning) and contexts (e.g., computer screen, simulator, wild). The recognition targets included cognitive and affective states (e.g., emotions, cognitive workload) and user traits (e.g., personality, working memory. This review identified state-of-the-art approaches and gaps, which highlighted the need for building up best practices, larger-scale datasets, and diversifying tasks and contexts. Future research should focus on improving the ecological validity, multi-modal approaches for robust user modeling, and developing gaze-adaptive systems. 

The paper is Open Access and available here: https://www.mdpi.com/1995-8692/18/2/8