Abstract: Users’ cognitive behaviors are dramatically influenced by the specific tasks assigned to them. Information on users’ tasks can be applied to many areas, such as human behavior analysis and intelligent human-computer interfaces. It can be used as the input of intelligent systems and enable the systems to automatically adjust their functions according to different tasks. User task prediction refers to the prediction of users’ tasks at hand based on the characteristics of his or her eye movements, the characteristics of scene content, and other related information. User task prediction is a popular research topic in vision research, and researchers have proposed many successful task prediction algorithms. However, the algorithms proposed in prior works mainly focus on a particular scene, and comparison and analysis are absent for these algorithms. This paper presented a review of prior works on task prediction in scenes of images, videos, and real world, and detailed existing task prediction algorithms. Based on a real-world task dataset, this paper evaluated the performances of existing algorithms and conducted the corresponding analysis and discussion. As such, this work can provide meaningful insights for future works on this important topic. 

Key words: user task prediction, cognitive state prediction, task classification, scanpath classification, machine learning