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    Evaluating and modeling the effects of brightness on visual attention using multiple object tracking method
    (Taylor & Francis Ltd, 2023) Yazgan, Mehmet Toyanc; Yagimli, Mustafa; Ozubko, Jason
    This article systematically evaluates and models how brightness affects performance in Multiple Object Tracking (MOT) within screen-based environments. MOT proficiency is essential across various fields, and comprehending the elements that impact MOT performance holds significance for occupational health and safety. While previous studies have scrutinized the influence of brightness on object recognition, its repercussions on MOT performance in screen-based environments remain comparatively less comprehended. This research aims to bridge this gap by delving into the distinct and combined impacts of brightness-related factors on MOT performance. Additionally, it seeks to construct a computational model that can forecast MOT performance across diverse brightness conditions. The outcomes of this study will offer valuable insights into core psychological processes, thereby steering the development of more efficient visual displays to enhance occupational health and safety.Our findings revealed a significant correlation between brightness levels and MOT performance, with optimal tracking observed at medium brightness levels. Additionally, complex object motion patterns were found to exacerbate the challenges of tracking in low brightness settings. These insights have direct implications for screen-based interfaces, suggesting the need for adaptive brightness settings based on the content's complexity and the user's task.