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.

This paper describes a qualitative research methodology developed for experimental studies of collaborative visual analysis. In much of this work we build upon Herbert H. Clark's Joint Activity Theory to infer cognitive processes from field experiments testing collaborative decision making over data. As is true of any methodology, it provides the underlying conceptual structure and analytic processes that can be adapted by other researchers to devise their own studies and analyze their results. Our focus is on collaborative use of visual information systems for aircraft safety analysis, however the methods can be/have been extended to other tasks and analysts.

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