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People routinely rely on data to make decisions, but the process can be riddled with biases. We show that patterns in data might be noticed first or more strongly, depending on how the data is visually represented or what the viewer finds salient. We also demonstrate that viewer interpretation of data is similar to that of ‘ambiguous figures’ such that two people looking at the same data can come to different decisions. In our studies, participants read visualizations depicting competitions between two entities, where one has a historical lead (A) but the other has been gaining momentum (B) and predicted a winner, across two chart types and three annotation approaches. They either saw the historical lead as salient and predicted that A would win, or saw the increasing momentum as salient and predicted B to win. These results suggest that decisions can be influenced by both how data are presented and what patterns people find visually salien 

Dynamic data visualizations can convey large amounts of information over time, such as using motion to depict changes in data values for multiple entities. Such dynamic displays put a demand on our visual processing capacities, yet our perception of motion is limited. Several techniques have been shown to improve the processing of dynamic displays. Staging the animation to sequentially show steps in a transition and tracing object movement by displaying trajectory histories can improve processing by reducing the cognitive load. In this paper, We examine the effectiveness of staging and tracing in dynamic displays. We showed participants animated line charts depicting the movements of lines and asked them to identify the line with the highest mean and variance. We manipulated the animation to display the lines with or without staging, tracing and history, and compared the results to a static chart as a control. Results showed that tracing and staging are preferred by participants, and improve their performance in mean and variance tasks respectively. They also preferred display time 3 times shorter when staging is used. Also, encoding animation speed with mean and variance in congruent tasks is associated with higher accuracy. These findings help inform real-world best practices for building dynamic displays. 

Reading a visualization is like reading a paragraph. Each sentence is a comparison: the mean of these is higher than those; this difference is smaller than that. What determines which comparisons are made first? The viewer's goals and expertise matter, but the way that values are visually grouped together within the chart also impacts those comparisons. Research from psychology suggests that comparisons involve multiple steps. First, the viewer divides the visualization into a set of units. This might include a single bar or a grouped set of bars. Then the viewer selects and compares two of these units, perhaps noting that one pair of bars is longer than another. Viewers might take an additional third step and perform a second-order comparison, perhaps determining that the difference between one pair of bars is greater than the difference between another pair. We create a visual comparison taxonomy that allows us to develop and test a sequence of hypotheses about which comparisons people are more likely to make when reading a visualization. We find that people tend to compare two groups before comparing two individual bars and that second-order comparisons are rare. Visual cues like spatial proximity and color can influence which elements are grouped together and selected for comparison, with spatial proximity being a stronger grouping cue. Interestingly, once the viewer grouped together and compared a set of bars, regardless of whether the group is formed by spatial proximity or color similarity, they no longer consider other possible groupings in their comparisons. 

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. 

Trust is an essential aspect of data visualization, as it plays a crucial role in the interpretation and decision-making processes of users. While research in social sciences outlines the multi-dimensional factors that can play a role in trust formation, most data visualization trust researchers employ a single-item scale to measure trust. We address this gap by proposing a comprehensive, multidimensional conceptualization and operationalization of trust in visualization. We do this by applying general theories of trust from social sciences, as well as synthesizing and extending earlier work and factors identified by studies in the visualization field. We apply a two-dimensional approach to trust in visualization, to distinguish between cognitive and affective elements, as well as between visualization and data-specific trust antecedents. We use our framework to design and run a large crowd-sourced study to quantify the role of visual complexity in establishing trust in science visualizations. Our study provides empirical evidence for several aspects of our proposed theoretical framework, most notably the impact of cognition, affective responses, and individual differences when establishing trust in visualizations.