Human Decisions from Visualized Data
Project 2022-23
Project Title: Human Decisions from Visualized Data.
Lab/Research Group: HCI-VIS Group
We conduct interdisciplinary research across human visual perception, cognition, and data visualization communities. By investigating how humans perceive, interpret, and make decisions from visualized data, we answer questions such as "what are the underlying perceptual and cognitive processes when people make sense of data visualizations?", and "how can we design an effective visualization or tell a good story with data?". We leverage the empirical knowledge we obtained from user experiments to develop novel visualization techniques to facilitate data exploration, analysis, and communication.
Project Description
Confirmation bias describes the tendency to seek and interpret information preferentially to support one's pre-existing beliefs. This bias can be especially nefarious in data analytics, because data visualizations are often thought to present "what is" or objective truths to a viewer. Data analysts can fall victim to confirmation bias by seeing only what they want to see in data, drawing inaccurate or sub-optimal conclusions. In this project, we will study how confirmation bias manifests in real-world visual data analysis tasks, and develop bias mitigation interventions.
Learning Objectives:
By participating in this year-long project, the students will learn how to design user experiments, build online experiments, work with human participants, and analyze human behavior data. The students will also learn about biases in human decision making, principals of data visualization, and how to present their work to a general audience.
