AAYUSH GONDALE,DR.LALIT SACHDEVA,SWAPAN DAS GUPTA
DOI: https://doi.org/The evolution of technologies within Industry 4.0, which includes intelligent automation and cyber-physical systems, calls for a shift in human cognitive flexibility. Pre-existing models centered on automation readiness often focus on a singular, technical form of automation competence. This overlooks critical human-machine dimension collaboration, neurocognitive factors. This study presents a new model of cognitive readiness incorporating real-time neurocognitive evaluation with EEG, fNIRS, HRV, and eye-tracking during industrial simulations. From human factors and workforce neuroscience perspectives, automation readiness is redefined and framed within neurophysiological variables, neurophysiological adaptability, and contextual factors such as age, training, and digital literacy. Employing a mixed-method experimental approach, industry practitioners were assessed in conditions of varying automation levels. Findings indicated that heightened automation complexity increases cognitive load, particularly for older workers and digitally less proficient individuals. From a neurocognitive standpoint, cognitive stressors were EEG alpha desynchronization and fNIRS-driven prefrontal cortical oxygenation increases. Assisted cognitive strategies markedly improved these neurocognitive stressors. The study highlights the need for industrial practices to incorporate cognitive readiness, suggesting adaptive training, assessment, interfaces, and real-time evaluation as applications. Notwithstanding the challenges posed by sample diversity and ecological validity, the study provides a starting point for neuro-industrial engineering, where technology is anthropocentrically calibrated to a human’s cognitive skills for safety, performance, and ecological sustainability in the advancing industries of the future.
