Handling Noisy Data in Eye-Tracking Research: Methods and Best Practices

Eye-tracking research plays a crucial role in understanding human behavior, cognitive processes, and user interactions. However, the accuracy and reliability of the findings can be compromised due to the presence of noise and outliers in eye-tracking data. In this study, we delve into various methodologies, such as filtering, outlier identification, and data preparation, to address these challenges. We aim to comprehensively review and analyze different approaches for handling noisy eye-tracking data, including filtering methods, outlier identification algorithms, and data pretreatment strategies. By comparing these methodologies, we aim to shed light on their respective advantages, disadvantages, and trade-offs. Our research will provide researchers with a comprehensive understanding of the available options, empowering them to make informed decisions based on their specific research objectives. Moreover, we will offer valuable guidelines and best practices for effectively processing noisy eye-tracking data. Through this endeavor, we strive to enhance the robustness and reliability of eye-tracking research, thereby advancing our understanding of human behavior and cognitive processes.