Cognitive Load and Worker Performance in Digitally Augmented Manufacturing Environments

Abstract

The growing pace of digitally augmented technology adoption such as augmented reality (AR), virtual reality (VR), digital twins, and collaborative robotics have transformed the contemporary manufacturing ecosystem to form a setting where human cognition and digital intelligence continuously interact. This review explores the connection between cognitive load and worker performance in digitally augmented manufacturing environments (DAMEs), summarizing findings on the topic of cognitive psychology, neuroergonomics, and human-machine interaction studies. The results prove that digital augmentation, designed with ergonomic accuracy and adaptive intelligence, could decrease the amount of extraneous mental activity by as much as 30 per cent, producing quantifiable effectiveness in task accuracy, throughput time, safety and situational awareness. Mental workload on the other hand can be increased by poor interface design, information overload, and latency in system feedback leading to fatigue, stress and deterioration of the quality of decision. The review combines both the empirical evidence and theoretical models that emphasize the mediating effect of cognitive load between the design of digital systems and their operational performance and well-being. It determines significant gaps in adaptive workload and multimodal measurement integration and ethical data management throughout augmented manufacturing systems. The paper concludes that human-centred, cognitively sustainable design relying on the assistance of artificial intelligence and biosensing technologies that can maintain workloads in real-time is the way to move forward. The alignment will help bring the Industry 5.0 vision of intelligent, empathic, and human-aligned manufacturing environments that are productive and cognitively healthy.