The field of eye movement analysis is rapidly advancing, with a strong focus on developing innovative methods for quantifying user attention and decoding complex cognitive processes. Recent research has highlighted the importance of leveraging machine learning techniques to analyze eye movements and extract valuable insights into user behavior. The use of eye-tracking data has shown great promise in enhancing lie detectors, predicting user attention in multi-slot environments, and decoding open-ended information seeking goals. Furthermore, researchers are exploring the potential of eye movements to characterize topic familiarity and query specificity, with applications in search and human-computer interaction. Notable papers in this area include:

• AdSight, which introduces a scalable and accurate method for quantifying user attention in multi-slot sponsored search.
• Decoding Open-Ended Information Seeking Goals from Eye Movements in Reading, which demonstrates the feasibility of automatically decoding reading goals from eye movements.
• Eye Movements as Indicators of Deception, which evaluates the efficacy of AI models for detecting deception using gaze data.