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

TL;DR

This paper explores how visual cues, specifically line thickness representing the Value of Information, can enhance sensemaking in data analysis by aiding cognitive processes and reducing biases.

Contribution

It introduces a novel annotation enhancement using line thickness to visually encode the VoI, improving traditional link-node visualization tools.

Findings

Visual cues improve hypothesis generation

Line thickness effectively represents VoI

Enhanced sensemaking reduces cognitive limitations

Abstract

Sensemaking is the cognitive process of extracting information, creating schemata from knowledge, making decisions from those schemata, and inferring conclusions. Human analysts are essential to exploring and quantifying this process, but they are limited by their inability to process the volume, variety, velocity, and veracity of data. Visualization tools are essential for helping this human-computer interaction. For example, analytical tools that use graphical linknode visualization can help sift through vast amounts of information. However, assisting the analyst in making connections with visual tools can be challenging if the information is not presented in an intuitive manner. Experimentally, it has been shown that analysts increase the number of hypotheses formed if they use visual analytic capabilities. Exploring multiple perspectives could increase the diversity of those hypotheses, potentially minimizing cognitive biases. In this paper, we discuss preliminary research results that indicate an improvement in sensemaking over the traditional link-node visualization tools by incorporating an annotation enhancement that differentiates links connecting nodes. This enhancement assists by providing a visual cue, which represents the perceived value of reported information. We conclude that this improved sensemaking occurs because of the removal of the limitations of mentally consolidating, weighing, and highlighting data. This study aims to investigate whether line thickness can be used as a valid representation of VoI.