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1. The Role of Text in Visualizations: How Annotations Shape Perceptions of Bias and Influence Predictions

   https://doi.org/10.1109/TVCG.2023.3338451  

   Stokes, Chase; Bearfield, Cindy Xiong; Hearst, Marti A (December 2023, IEEE Transactions on Visualization and Computer Graphics)

   This paper investigates the role of text in visualizations, specifically the impact of text position, semantic content, and biased wording. Two empirical studies were conducted based on two tasks (predicting data trends and appraising bias) using two visualization types (bar and line charts). While the addition of text had a minimal effect on how people perceive data trends, there was a significant impact on how biased they perceive the authors to be. This finding revealed a relationship between the degree of bias in textual information and the perception of the authors' bias. Exploratory analyses support an interaction between a person's prediction and the degree of bias they perceived. This paper also develops a crowdsourced method for creating chart annotations that range from neutral to highly biased. This research highlights the need for designers to mitigate potential polarization of readers' opinions based on how authors' ideas are expressed. 

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2. Perceptual and Cognitive Foundations of Information Visualization

   https://doi.org/10.1146/annurev-vision-110323-110009  

   Schloss, Karen B (September 2025, Annual Review of Vision Science)

   Information visualization is central to how humans communicate. Designers produce visualizations to represent information about the world, and observers construct interpretations based on the visual input as well as their heuristics, biases, prior knowledge, and beliefs. Several layers of processing go into the design and interpretation of visualizations. This review focuses on processes that observers use for interpretation: perceiving visual features and their interrelations, mapping those visual features onto the concepts they represent, and comprehending information about the world based on observations from visualizations. Observers are more effective at interpreting visualizations when the design is well-aligned with the way their perceptual and cognitive systems naturally construct interpretations. By understanding how these systems work, it is possible to design visualizations that play to their strengths and thereby facilitate visual communication. 

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3. Comparative Visualizations through Parameterization and Variability

   https://doi.org/10.1109/VLHCC.2018.8506578  

   Smeltzer, Karl; Erwig, Martin (October 2018, 2018 IEEE Symposium on Visual Languages and Human-Centric Computing)

   Comparative visualizations and the comparison tasks they support constitute a crucial part of visual data analysis on complex data sets. Existing approaches are ad hoc and often require significant effort to produce comparative visualizations, which is impractical especially in cases where visualizations have to be amended in response to changes in the underlying data. We show that the combination of parameterized visualizations and variations yields an effective model for comparative visualizations. Our approach supports data exploration and automatic visualization updates when the underlying data changes. We provide a prototype implementation and demonstrate that our approach covers most of existing comparative visualizations. 

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4. An empirical study of counterfactual visualization to support visual causal inference

   https://doi.org/10.1177/14738716241229437  

   Wang, Arran Zeyu; Borland, David; Gotz, David (April 2024, Information Visualization)

   Counterfactuals – expressing what might have been true under different circumstances – have been widely applied in statistics and machine learning to help understand causal relationships. More recently, counterfactuals have begun to emerge as a technique being applied within visualization research. However, it remains unclear to what extent counterfactuals can aid with visual data communication. In this paper, we primarily focus on assessing the quality of users’ understanding of data when provided with counterfactual visualizations. We propose a preliminary model of causality comprehension by connecting theories from causal inference and visual data communication. Leveraging this model, we conducted an empirical study to explore how counterfactuals can improve users’ understanding of data in static visualizations. Our results indicate that visualizing counterfactuals had a positive impact on participants’ interpretations of causal relations within datasets. These results motivate a discussion of how to more effectively incorporate counterfactuals into data visualizations. 

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5. The Science of Visual Data Communication: What Works

   https://doi.org/10.1177/15291006211051956  

   Franconeri, Steven L.; Padilla, Lace M.; Shah, Priti; Zacks, Jeffrey M.; Hullman, Jessica (December 2021, Psychological Science in the Public Interest)

   Effectively designed data visualizations allow viewers to use their powerful visual systems to understand patterns in data across science, education, health, and public policy. But ineffectively designed visualizations can cause confusion, misunderstanding, or even distrust—especially among viewers with low graphical literacy. We review research-backed guidelines for creating effective and intuitive visualizations oriented toward communicating data to students, coworkers, and the general public. We describe how the visual system can quickly extract broad statistics from a display, whereas poorly designed displays can lead to misperceptions and illusions. Extracting global statistics is fast, but comparing between subsets of values is slow. Effective graphics avoid taxing working memory, guide attention, and respect familiar conventions. Data visualizations can play a critical role in teaching and communication, provided that designers tailor those visualizations to their audience. 

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