Choi, In KwonChilders, TaylorRaveendranath, Nirmal KumarMishra, SwatiHarris, KyleReda, Khairi2019-01-172019-01-172019In Kwon Choi, Taylor Childers, Nirmal Kumar Raveendranath, Swati Mishra, Kyle Harris, and Khairi Reda. 2019. Concept-Driven Visual Analytics: an Exploratory Study of Model- and Hypothesis- Based Reasoning with Visualizations. In Proceedings of CHI Conference on Human Factors in Computing Systems, Glasgow, Scotland Uk, May 4–9, 2019 (CHI 2019). DOI: https://doi.org/10.1145/3290605.3300298https://hdl.handle.net/1805/18187Visualization tools facilitate exploratory data analysis, but fall short at supporting hypothesis-based reasoning. We conducted an exploratory study to investigate how visualizations might support a concept-driven analysis style, where users can optionally share their hypotheses and conceptual models in natural language, and receive customized plots depicting the fit of their models to the data. We report on how participants leveraged these unique affordances for visual analysis. We found that a majority of participants articulated meaningful models and predictions, utilizing them as entry points to sensemaking. We contribute an abstract typology representing the types of models participants held and externalized as data expectations. Our findings suggest ways for rearchitecting visual analytics tools to better support hypothesis- and model-based reasoning, in addition to their traditional role in exploratory analysis. We discuss the design implications and reflect on the potential benefits and challenges involved.en-USVisual analyticsData visualizationSensemakingMental modelsArticle