Conceptual models visually represent entities and relationships between them in an information system. Effective conceptual models should be simple while communicating sufficient information. This trade-off between model complexity and clarity is crucial to prevent failure of information system development. Past studies have found that more expressive models lead to higher performance on tasks measuring a user s deep understanding of the model and attributed this to lower experience of cognitive workload associated with these models. This study examined this hypothesis by measuring users EEG brain activity while they completed a task with different conceptual models. 30 participants were divided into two groups: One group used a low ontologically expressive model (LOEM), and the other group used a high ontologically expressive model (HOEM). Cognitive workload during the task was quantified using EEG Engagement Index, which is a ratio of brain activity power in beta as opposed to the sum of alpha and theta frequency bands. No significant difference in cognitive workload was found between the LOEM and HOEM groups indicating equal amounts of cognitive processing required for understanding of both models. The main contribution of this study is the introduction of neurophysiological measures as an objective quantification of cognitive workload in the field of conceptual modeling and information systems.
One-Block Transformer (1BT) for EEG-Based Cognitive Workload Assessment
Honda Research Institute Japan Wako City, Japan；Department of Electronic Engineering Hellenic Mediterranean University Chania, Greece；BioSIS (Biosensing \& Intelligent Systems) Lab Centre for Intelligent Computing；Systems University of Canberra Canberra, Australia
Unraveling the Connection: How Cognitive Workload Shapes Intent Recognition in Robot-Assisted Surgery
Cognitive Assistants（认知助手）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland Informatics Campus, Saarbrucken, Germany（萨尔布吕肯德国信息学校区）
Neural Signatures Within and Between Chess Puzzle Solving and Standard Cognitive Tasks for Brain-Computer Interfaces: A Low-Cost Electroencephalography Study
Leveraging fNIRS to Evaluate Workload for Adaptive Training in Virtual Reality
University of Colorado Boulder（科罗拉多大学博尔德分校）；Colorado State University（科罗拉多州立大学）；Montana State University（蒙大拿州立大学）；University of Northern Colorado（北科罗拉多大学）
Assessing Region-Level EEG Contributions to Cognitive Workload Prediction
School of InfoComm Technology, Ngee Ann Polytechnic（信息与通信技术学院，南洋理工学院新加坡分校）；Engineering Product Development Pillar, Singapore University of Technology and Design（工程产品开发支柱，新加坡科技设计大学）；NUS High School of Math and Science（国立大学科学高中）
Augmented Reality Indoor Wayfinding in Hospital Environments An Empirical Study on Navigation Efficiency, User Experience, and Cognitive Load
University of Michigan School of Information（密歇根大学信息学院）；University of Michigan School of Nursing（密歇根大学护理学院）；University of Michigan School for Environment and Sustainability（密歇根大学环境可持续发展学院）；University of Michigan Medical School（密歇根大学医学院）
Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
School of Computer Science, Technological University Dublin, Dublin, Ireland（计算机科学学院，都柏林技术大学，都柏林，爱尔兰）；Artificial Intelligence and Cognitive Load Lab, Applied Intelligence Research Centre, School of Computer Science, Technological University Dublin, Dublin, Ireland（人工智能与认知负荷实验室，应用智能研究中心，计算机科学学院，都柏林技术大学，都柏林，爱尔兰）
