Error processing is a fundamental cognitive function that shapes decision-making, learning, and performance in knowledge workers. While its neural mechanisms are well studied, the influence of hormonal variations—particularly across the menstrual cycle—remains underexplored. This study investigates how fluctuations in estradiol and progesterone affect cognitive markers (error-related potentials) and behavioral markers such as reaction time and error rate. We propose a three-month longitudinal within-subject study with 20 female participants, using electroencephalography (EEG) and a Go/No-Go task to assess cognitive control across menstrual phases. Situated within the NeuroIS domain, this research integrates neuroscientific methods into information systems (IS) design to better understand and support cognitive performance in digital work environments. By combining neurophysiological and hormonal data, the study explores how endogenous fluctuations shape error monitoring and post-error adjustments. Findings are expected to offer insights into cycle-dependent cognitive variability, informing task management and adaptive, user-centered IS that promote personalized workplace strategies.

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Cycle-Sensitive Error Processing: Neurophysiological Insights for Enhancing Cognitive Performance in Digital Work Environments

  • Rabea Liebram,
  • Alina Bockshecker,
  • Sven Hoffmann,
  • Roman Liepelt,
  • Stefan Smolnik

摘要

Error processing is a fundamental cognitive function that shapes decision-making, learning, and performance in knowledge workers. While its neural mechanisms are well studied, the influence of hormonal variations—particularly across the menstrual cycle—remains underexplored. This study investigates how fluctuations in estradiol and progesterone affect cognitive markers (error-related potentials) and behavioral markers such as reaction time and error rate. We propose a three-month longitudinal within-subject study with 20 female participants, using electroencephalography (EEG) and a Go/No-Go task to assess cognitive control across menstrual phases. Situated within the NeuroIS domain, this research integrates neuroscientific methods into information systems (IS) design to better understand and support cognitive performance in digital work environments. By combining neurophysiological and hormonal data, the study explores how endogenous fluctuations shape error monitoring and post-error adjustments. Findings are expected to offer insights into cycle-dependent cognitive variability, informing task management and adaptive, user-centered IS that promote personalized workplace strategies.