Emotional Intelligence as a Neuroergonomic Buffer in High-Pressure Professional Environments

Abstract: Technical and business professionals increasingly operate in cognitively demanding, high-pressure environments that require sustained attention, adaptive regulation, and effective collaboration. Professionals such as software engineers, product managers, and business leaders routinely face complex incidents, accelerated release cycles, and human–AI collaboration scenarios that place heavy demands on cognitive and emotional regulation. This paper presents a theoretical model integrating Emotional Intelligence (EI) and neuroergonomics to explain how emotional regulation influences neural efficiency and cognitive stability under stress. The framework positions EI as a neuroergonomic buffer, a moderating mechanism that mitigates the impact of stress and workload, measured through electroencephalography (EEG) workload indices. Drawing on human factors and organizational psychology, the model proposes that EI competencies, self-awareness, self-regulation, empathy, and relationship management, enable professionals to maintain neural stability and cognitive efficiency in high-pressure situations. The framework explains how Emotional Intelligence influences EEG based workload responses by enhancing emotional and stress regulation, promoting neural efficiency, and facilitating faster recovery after demanding tasks. The paper concludes by outlining implications for leadership development, human technology systems design, and future neuroergonomic research on emotional regulation and performance in complex professional environments. By bridging Emotional Intelligence and neuroergonomics, this framework provides a conceptual foundation for empirical studies and organizational applications that integrate emotional competence development with neurophysiological feedback to enhance human performance, resilience, and well-being in cognitively demanding work environments.

Keywords: Emotional Intelligence, Neuroergonomics, Cognitive Workload, Stress Regulation, EEG, Human–AI Teaming, Future of Work, Leadership, Cognitive Neuroscience

DOI: 10.54941/ahfe1006827

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