Exploring the mental representation of how people interpret visual analytic tasks, based on cognitive foundation

Open Access
Wen, Fangyuan
Area of Honors:
Information Sciences and Technology
Bachelor of Science
Document Type:
Thesis Supervisors:
  • Xiaolong Zhang, Thesis Supervisor
  • David Joseph Fusco, Honors Advisor
  • Visualization
  • Visual analytics
  • cognitive bias
Background: The technology of visualization is developed at a fast pace as it is highly demanded in this big data era. However, current visual analytic tools largely focus on providing visualization tools to target for specific data types and often overlook the processes and activities involved in visual analytics. Visual analytics often involve high-level cognitive activities, and the results of visual analytics can be directly influenced by the cognition of analysts in data analysis. One of important cognitive factors in visual analytics is cognitive bias, which may mislead the analysis in various ways. Research Questions: How do cognitive bias affect the results of visual analytics? Research Method and Result: The research will use a qualitative method to analyze the solutions submitted to the 2017 IEEE VAST Challenge. By comparing some solutions with the ground truth of the challenge, we identify the gaps between the correct answers and the submitted answers and from the perspective of cognitive biases explore the causes of these differences. Our analysis focuses on two common cognitive biases: anchoring bias and availability bias. Conclusion: Our research confirms the existence of cognitive biases in data analysis supported by visual analytics tools. We also provide some guidance to mitigate the impacts of cognitive biases in visual analytics. Our work can help analysts understand the potential biases they may have in their work, and help the designers of visual analytics systems understand what can be done to improve their systems.