The Rise of Advanced Computational Techniques for Holistic Analysis of Human Gaze Behavior and Its Significance

The Rise of Advanced Computational Techniques for Holistic Analysis of Human Gaze Behavior and Its Significance Gaze behavior analysis has emerged as a crucial area of study with broad implications for psychology, neuroscience, human-computer interaction, and more. In this research, we introduce an innovative methodology that leverages advanced computational techniques to provide a holistic analysis of human gaze behavior. Our proposed method, GazeNet, combines Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and Gaussian Mixture Models (GMMs) to capture both temporal and spatial aspects of gaze data. GazeNet excels in predictive modeling, enabling real-time applications in diverse fields. In this study, we compare the performance of GazeNet with ten original methods, including Visual Gaze Analysis, Eye-Tracking Analytics, and Gaze Behavior Pattern Recognition. The comparison includes accuracy, precision, recall, and F1 score, revealing that GazeNet consistently outperforms its counterparts. Furthermore, GazeNet provides comprehensive insights into cognitive processes, surpassing traditional methods. Our research signifies the transformative potential of GazeNet in psychology, neuroscience, clinical diagnostics, and real-world applications like user experience design and driver safety. The integration of advanced computational techniques brings a new level of precision and efficiency to gaze analysis, enabling a deeper understanding of human attention allocation.