The Big Data and Artificial Intelligence are exponential technologies accelerating innovation across all industries and reshaping our everyday lives. With the pace of new technologies, the sheer amount of data generated from various applications provides valuable information or knowledge for better decision-making and organizational value creation. Research in cognitive science has showed the fundamental role of visual representations (VRs) in the performance of complex cognitive activities. However, there has been little research that makes an effort to understand how information visualization tools or VR systems help users explore, learn or investigate knowledge and how they can support the execution of complex cognitive activities (CCAs), for example, users engaged in sense making, learning, or decision making. In addition, research is also lacking that tackles the issue of how to evaluate the effects of VRs. This research sought to analyze the effects of VRs to help users’ performance of CCAs from the perspectives of human search processes, the changes of intrinsic cognitive load, and learning evolution. Accordingly, we propose a project to investigate the issue of the evolution of human-information interaction models and the evaluation of CCAs based on epistemic action patterns. We refine the framework of epistemology and design of human-information interaction in complex cognitive activities (EDIFICE), develop the related search-action sequence algorithms, and then develop evaluation methods. We aim to have a comprehensive understanding of how VRs support learning and further conduct a scale development for the aims of this research. The contribution of this research are as follows: (1) systematically identify and categorize important theoretical research trends in human-information interaction (HII), design a search pattern, a visual-based search interface, human information behaviors for CCAs; (2) investigate the topic of how cognitive activity support tools (CASTs) support CCAs during the process of HII; (3) design and analyze methods of epistemic action patterns (EAPs) based on the refined framework of EDIFICE-PVR; (4) develop the algorithms of search moves and epistemic action-interaction sequences to categorize EAPs for VRs; (5) develop a clustering approach for grouping users’ search moves and epistemic action-interaction sequences and categorizing search strategies for accomplishing CCAs; (6) conduct empirical studies on electricity visualization system to examine how VR tools support various types of users to execute CCAs and then forming search strategies; (7) conduct empirical studies to examine how VR and search tools support various types of users to execute CCAs and then forming search strategies via the digital archive of National Palace Museum.