Rüdiger Christoph Pryss ^1,2,3* Jan vom Brocke ^4,5,6 Manfred Reichert ⁷ Enrico Rukzio ⁸ Winfried Schlee ⁹ Barbara Weber ¹⁰

• ¹ Julius Maximilian University of Würzburg, Würzburg, Germany
• ² Institute of Clinical Epidemiology and Biometry, Faculty of Medicine, University of Würzburg, Würzburg, Bavaria, Germany
• ³ Institute of Medical Data Science, University Hospital of Würzburg, Würzburg, Bavaria, Germany
• ⁴ Department of Information Systems, University of Liechtenstein, Vaduz, Liechtenstein, Vaduz, Liechtenstein
• ⁵ University of Münster, Münster, North Rhine-Westphalia, Germany
• ⁶ European Research Center for Information Systems, Münster, Germany, Münster, Germany
• ⁷ Institute for Databases and Information Systems, Faculty of Engineering, Computer Science and Psychology, University of Ulm, Ulm, Germany
• ⁸ Institute for Media Research and Media Development, Ulm University, Ulm, Germany, Ulm, Germany
• ⁹ Eastern Switzerland University of Applied Sciences, St. Gallen, St. Gallen, Switzerland
• ¹⁰ Institute of Computer Science, University of St. Gallen, 9000 St. Gallen, Switzerland, St. Gallen, Switzerland

The integration of methodologies from cognitive psychology and neuroscience into the study of IT systems has seen a notable uptick in recent years. This surge is spurred by the escalating complexity of IT systems, which increasingly tax both novice and seasoned users alike Ma et al. (2023); Leger et al. (2014).Consequently, insights into constructing IT systems with a reduced cognitive burden are gaining paramount importance.A survey of the current literature reveals several discernible trends. Researchers are delving into the cognitive processes involved in programming Peitek et al. (2021) 2022), which establish a direct connection between the brain and input/output devices.With the proliferation of digital devices such as smartwatches and wristbands, as well as advanced facial recognition services, neuroscience is increasingly finding its way into everyday working and private life.Many use cases have been identified, including using emotion-sensing devices to significantly reduce stress and supporting flow in people's work processes Whelan et al. (2019). Research has also begun to investigate the design of so-called neuro-adaptive processes, which capture body data during process execution and adapt the process flow and human-computer interaction accordingly in real time vom Brocke (2022).Against this backdrop, this topic aims to foster a neuroscientific perspective on the subject matter. A total of 76 authors were actively solicited to contribute, with 29 ultimately participating. Out of the 22 targeted submissions, 17 papers were received. Following a rigorous review process, 12 papers were regrettably declined, while 5 were accepted, yielding an acceptance rate of 29%. Moreover, the topic has amassed 12,200 visits to date, underscoring the continued relevance of its content.Spanning a duration of 11 months in 2022, this topic enjoyed the collaborative support of both computer scientists and neuroscientists on the editorial board. In the subsequent chapters, we provide a comprehensive overview of the 5 accepted papers, culminating in a summary and future outlook in this editorial. In this topic, five papers were accepted. These papers bear the following titles:• On the accuracy of code complexity metrics: A neuroscience-based guideline for improvement Hao et al. (2023)