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Abstract Developments in data visualization research have enabled visualization systems to achieve great general usability and application across a variety of domains. These advancements have improved not only people's understanding of data, but also the general understanding of people themselves, and how they interact with visualization systems. In particular, researchers have gradually come to recognize the deficiency of having one‐size‐fits‐all visualization interfaces, as well as the significance of individual differences in the use of data visualization systems. Unfortunately, the absence of comprehensive surveys of the existing literature impedes the development of this research. In this paper, we review the research perspectives, as well as the personality traits and cognitive abilities, visualizations, tasks, and measures investigated in the existing literature. We aim to provide a detailed summary of existing scholarship, produce evidence‐based reviews, and spur future inquiry. 

Abstract There is fast‐growing literature on provenance‐related research, covering aspects such as its theoretical framework, use cases, and techniques for capturing, visualizing, and analyzing provenance data. As a result, there is an increasing need to identify and taxonomize the existing scholarship. Such an organization of the research landscape will provide a complete picture of the current state of inquiry and identify knowledge gaps or possible avenues for further investigation. In this STAR, we aim to produce a comprehensive survey of work in the data visualization and visual analytics field that focus on the analysis of user interaction and provenance data. We structure our survey around three primary questions: (1) WHY analyze provenance data, (2) WHAT provenance data to encode and how to encode it, and (3) HOW to analyze provenance data. A concluding discussion provides evidence‐based guidelines and highlights concrete opportunities for future development in this emerging area. The survey and papers discussed can be explored online interactively athttps://provenance-survey.caleydo.org. 

Throughout the last decade, researchers have shown that the effectiveness of a visualization tool depends on the experience, personality, and cognitive abilities of the user. This work has also demonstrated that these individual traits can have significant implications for tools that support reasoning and decision-making with data. However, most studies in this area to date have involved only short-duration tasks performed by lay users. This short paper presents a preliminary analysis of a series of exercises with 22 trained intelligence analysts that seeks to deepen our understanding of how individual differences modulate expert behavior in complex analysis tasks. 

Visualization research has made significant progress in demonstrating the value of graphical data representation. Even still, the value added by static visualization is disputed in some areas. When presenting Bayesian reasoning information, for example, some studies suggest that combining text and visualizations could have an interactive effect. In this paper, we use eye tracking to compare how people extract information from text and visualization. Using a Bayesian reasoning problem as a test bed, we provide evidence that visualization makes it easier to identify critical information, but that once identified as critical, information is more easily extracted from the text. These tendencies persist even when text and visualization are presented together, indicating that users do not integrate information well across the two representation types. We discuss these findings and argue that effective representations should consider the ease of both information identification and extraction.