Studying how humans interact with abstract, visual representations of massive amounts of data provides knowledge about how cognition works in visual analytics. This knowledge provides guidelines for cognitive-aware design and evaluation of visual analytic tools. Different methods have been used to capture and conceptualize these processes including protocol analysis, experiments, cognitive task analysis, and field studies. In this article, we introduce Pair Analytics: a method for capturing reasoning processes in visual analytics. We claim that Pair Analytics offers two advantages with respect to other methods: (1) a more natural way of making explicit and capturing reasoning processes and (2) an approach to capture social and cognitive processes used to conduct collaborative analysis in real-life settings. We support and illustrate these claims with a pilot study of three phenomena in collaborative visual analytics: coordination of attention, cognitive workload, and navigation of analysis.

The most salient ways in which data visualization and interactive techniques have been understood as the material basis of cognition in the emergent field of visual analytics are discussed. Three main dominant understandings have captured the imagination and theorizations of researchers and technicians in this field: data visualizations and interactive techniques as cognitive amplifiers, cognitive prostheses, and cognitive mediators. The analysis of this treatment of materiality in cognition provides an up-to-date report on whether remarks on the situated character of cognition and the active role of human agents have, in effect, been incorporated in this field or not. We argue that even though visual analytic researchers have incorporated some of the ideas of situated cognition and tempered traditional arguments of information processing from cognitive science, understandings of the role of materiality in cognition are still marked by universalisms and ascriptions of exacerbated agency to visual representations.

These current comparative studies explore the impact of individual differences in personality factors on interface interaction and learning performance behaviors in both an interactive visualization and a menu-driven web table in two studies. Participants were administered three psychometric measures designed to assess Locus of Control, Big Five Extraversion, and Big Five Neuroticism. Participants were then asked to complete procedural learning tasks in each interface. Results demonstrated that all three measures predicted completion times. Additionally, analyses demonstrated that personality factors also predicted the number of insights participants reported while completing the tasks in each interface. Furthermore, we used the psychometric findings in conjunction with a follow-up psychometric survey with a further 50 participants to build initial user profiles based on the cognitive task being undertaken. We discuss how these findings advance our ongoing research in the Personal Equation of Interaction.