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Sensemaking is the cognitive process of extracting information, creatingschemata from knowledge, making decisions from those schemata, and inferringconclusions. Human analysts are essential to exploring and quantifying thisprocess, but they are limited by their inability to process the volume,variety, velocity, and veracity of data. Visualization tools are essential forhelping this human-computer interaction. For example, analytical tools that usegraphical linknode visualization can help sift through vast amounts ofinformation. However, assisting the analyst in making connections with visualtools can be challenging if the information is not presented in an intuitivemanner.  Experimentally, it has been shown that analysts increase the number ofhypotheses formed if they use visual analytic capabilities. Exploring multipleperspectives could increase the diversity of those hypotheses, potentiallyminimizing cognitive biases. In this paper, we discuss preliminary researchresults that indicate an improvement in sensemaking over the traditionallink-node visualization tools by incorporating an annotation enhancement thatdifferentiates links connecting nodes. This enhancement assists by providing avisual cue, which represents the perceived value of reported information. Weconclude that this improved sensemaking occurs because of the removal of thelimitations of mentally consolidating, weighing, and highlighting data. Thisstudy aims to investigate whether line thickness can be used as a validrepresentation of VoI.

Using the Value of Information (VoI) Metric to Improve Sensemaking