Detecting Like Patterns In Eye Movement Behaviour On Dynamic Contents Based On Gender Assemblage

Eye tracking has long been the gold standard for understanding user attention. For years, we’ve relied on heatmaps and fixation counts to optimise static content, asking questions like :TRFasazsz “where should the button be placed” ? “Is the headline visible” ? But the digital world is no longer static. It’s filled with TikTok feeds, interactive ads, animated interfaces, and streaming video. As content becomes dynamic, the complexityof measuring attention rises steeply. A new wave of research is tackling this challenge by applying sophisticated pattern recognition to eye movement behaviour (EMB) on moving visuals. Crucially, this research is moving beyond simple biological sex comparisons to investigate Gender Assemblage—revealing profound, predictable differences in how various gender groups strategise their visual attention. The paper investigates how dynamic contents can be used in detecting the similarities between gaze patterns by comparing original gaze patterns and predicted gaze patterns in gender assemblage. The model applied is based on a state space computation, which is compared to other machine learning approaches. The result shows N4SID has the highest performance and the least error with natural patterns on gender assemblage compared to the othermodels. These results aren’t just academic theory proof on advanced dynamics control on behaviour data ; it’s the next frontier for UX designers, marketers, and anyone building future-facing digital experiences..