Related papers: Toward a Logic of Generalization about Visualizati…

Visualization research often centers on how visual representations generate insight, guide interpretation, or support decision-making. But in many real-world domains, visualizations do not stand out--they recede into the background,…

In decision making a key source of uncertainty is people's perception of information which is influenced by their attitudes toward risk. Both, perception of information and risk attitude, affect the interpretation of information and hence…

Effective altruism is a movement whose goal it to use evidence and reason to figure out how to benefit others as much as possible. This movement is becoming influential, but effective altruists still lack tools to help them understand…

Data visualization should be accessible for all analysts with data, not just the few with technical expertise. Visualization recommender systems aim to lower the barrier to exploring basic visualizations by automatically generating results…

Data visualization is the process by which data of any size or dimensionality is processed to produce an understandable set of data in a lower dimensionality, allowing it to be manipulated and understood more easily by people. The goal of…

Problem-driven visualization work is rooted in deeply understanding the data, actors, processes, and workflows of a target domain. However, an individual's personality traits and cognitive abilities may also influence visualization use.…

Foundation models for vision and language are the basis of AI applications across numerous sectors of society. The success of these models stems from their ability to mimic human capabilities, namely visual perception in vision models, and…

Empirical studies form an integral part of visualization research. Not only can they facilitate the evaluation of various designs, techniques, systems, and practices in visualization, but they can also enable the discovery of the…

Data Visualization has become an important aspect of big data analytics and has grown in sophistication and variety. We specifically identify the need for an analytical framework for data visualization with textual information. Data…

Systematic generalization is a crucial aspect of intelligence, which refers to the ability to generalize to novel tasks by combining known subtasks and concepts. One critical factor that has been shown to influence systematic generalization…

People often use visualizations not only to explore a dataset but also to draw generalizable conclusions about underlying models or phenomena. While previous research has viewed deviations from rational analysis as problematic, we…

Datasets of visualization play a crucial role in automating data-driven visualization pipelines, serving as the foundation for supervised model training and algorithm benchmarking. In this paper, we survey the literature on visualization…

Framing -- how designers define and reinterpret problems, shape narratives, and guide audience understanding -- is central to design practice. Yet in visualization research, framing has been examined mostly through its rhetorical and…

Understanding a visualization is a multi-level process. A reader must extract and extrapolate from numeric facts, understand how those facts apply to both the context of the data and other potential contexts, and draw or evaluate…

Inspiration plays an important role in design, yet its specific impact on data visualization design practice remains underexplored. This study investigates how professional visualization designers perceive and use inspiration in their…

Visualization design is often described as the process of solving a well-defined problem by navigating a design space. While existing visualization design models have provided valuable structure and guidance, they tend to foreground…

Designers often create visualizations to achieve specific high-level analytical or communication goals. These goals require people to naturally extract complex, contextualized, and interconnected patterns in data. While limited prior work…

Professional roles for data visualization designers are growing in popularity, and interest in relationships between the academic research and professional practice communities is gaining traction. However, despite the potential for…

This paper revisits the role of quantitative and qualitative methods in visualization research in the context of advancements in artificial intelligence (AI). The focus is on how we can bridge between the different methods in an integrated…

In each of the last five years, a few dozen empirical studies appeared in visualization journals and conferences. The existing empirical studies have already featured a large number of variables. There are many more variables yet to be…