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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.