A Design Space of Vision Science Methods for Visualization Research
Madison Elliott, Christine Nothelfer, Cindy Xiong, Danielle Szafir
View presentation: 2020-10-30T17:00:00Z GMT-0600 Change your timezone on the schedule page
Direct link to video on YouTube: https://youtu.be/v6bJwsHxRLY
Data Type Agnostic, Guidelines, Methodologies, Taxonomy, Models, Frameworks, Theory, Human-Subjects Quantitative Studies, Domain Agnostic, Collaboration, Data Analysis, Reasoning, Problem Solving, and Decision Making, Perception & Cognition
A growing number of efforts aim to understand what people see when using a visualization. These efforts provide scientific grounding to complement design intuitions, leading to more effective visualization practice. However, published visualization research currently reflects a limited set of available methods for understanding how people process visualized data. Alternative methods from vision science offer a rich suite of tools for understanding visualizations, but no curated collection of these methods exists in either perception or visualization research. We introduce a design space of experimental methods for empirically investigating the perceptual processes involved with viewing data visualizations to ultimately inform visualization design guidelines. This paper provides a shared lexicon for facilitating experimental visualization research. We discuss popular experimental paradigms, adjustment types, response types, and dependent measures used in vision science research, rooting each in visualization examples. We then discuss the advantages and limitations of each technique. Researchers can use this design space to create innovative studies and progress scientific understanding of design choices and evaluations in visualization. We highlight a history of collaborative success between visualization and vision science research and advocate for a deeper relationship between the two fields that can elaborate on and extend the methodological design space for understanding visualization and vision.