Calibrating Trust in Visualization through the Manipulation of Visual Complexity
Content
Speaker
Abstract
Data visualizations are widely used to understand, communicate, and inform decisions in various fields including healthcare, environmental science, and education. The design choices made when creating visualizations can significantly shape people’s interpretation of data and, hence, their decisions. This raises critical issues surrounding trust in data visualizations, particularly ensuring viewers can accurately gauge the reliability of the information presented. Critical information can be discounted or dismissed without mutual trust between the viewer and the visualization. Therefore, establishing trust is a critical first step in visual data communication. However, existing visualization research has yet to reconcile a model of trust in visual data communication. My dissertation will explore the relationship between visualization design and trust to build a model that quantifies the impact of perceptual factors on trust in visual data communication.
To this end, I comprehensively surveyed literature across social science and computer science and established a multidimensional framework for operationalizing trust in visualization. This framework proposes that trust results from factors that are cognitive, based on logical reasoning, or affective, driven by emotions and beliefs. The framework further divides trust in visual data communication into two aspects: trust in the quality of the underlying data and trust in the design of the visualization, including its clarity and potential to mislead. Building on this framework, I conducted a series of experiments that identified visual complexity as a key factor influencing both trust in the underlying data and trust in the visualization design. For my dissertation, I will operationalize visual complexity in the context of visualization design and systematically examine its effect on trusting behaviors.
Psychologists have identified processing fluency, the speed and accuracy with which we perceive and interpret a stimulus, as a driving factor of visual complexity. Many perceptual elements can impact the processing fluency of a visualization, including the choice of color, the size of visual marks, the scale of the visualization, and the amount of information displayed. I propose to 1) establish the design space of factors that impact the processing fluency of visualizations, 2) generate a database of
visualizations with varying levels of processing fluency through formative studies, and 3) test the effect of fluency on trusting behaviors. These efforts will contribute to a comprehensive set of guidelines for creating perceptually fluent visualizations, with a consideration of how individual design choices can change perceived visualization complexity and impact trust in visual data communication.
Advisor
Cindy Xiong Bearfield