Exploring The Mental Representation Of How People Interpret Visual Analytic Tasks Based On Cognitive Foundation

Background: The technology of visualization is developed at a fast pace as it is highlydemanded in this big data era. However, current visual analytic tools largely focus on providingvisualization tools to target for specific data types and often overlook the processes and activitiesinvolved in visual analytics. Visual analytics often involve high-level cognitive activities, and theresults 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 theanalysis 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 thesolutions submitted to the 2017 IEEE VAST Challenge. By comparing some solutions with theground truth of the challenge, we identify the gaps between the correct answers and thesubmitted answers and from the perspective of cognitive biases explore the causes of thesedifferences. Our analysis focuses on two common cognitive biases: anchoring bias andavailability bias.Conclusion: Our research confirms the existence of cognitive biases in data analysissupported by visual analytics tools. We also provide some guidance to mitigate the impacts ofcognitive biases in visual analytics. Our work can help analysts understand the potential biasesthey may have in their work, and help the designers of visual analytics systems understand whatcan be done to improve their systems.