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1. Using Close Reading as a Method for Evaluating Visualizations

   https://doi.org/10.1109/BELIV51497.2020.00011  

   Bares, Annie; Zeller, Stephanie; Jackson, Cullen D.; Keefe, Daniel F.; Samsel, Francesca (October 2020, 2020 IEEE Workshop on Evaluation and Beyond - Methodological Approaches to Visualization (BELIV))

   null (Ed.)

   Visualization research and practice that incorporates the arts make claims to being more effective in connecting with users on a human level. However, these claims are difficult to measure quantitatively. In this paper, we present a follow-on study to use close reading, a humanities method from literary studies, to evaluate visualizations created using artistic processes [Bares 2020]. Close reading is a method in literary studies that we've previously explored as a method for evaluating visualizations. To use close reading as an evaluation method, we guide participants through a series of steps designed to prompt them to interpret the visualization's formal, informational, and contextual features. Here we elaborate on our motivations for using close reading as a method to evaluate visualizations, and enumerate the procedures we used in the study to evaluate a 2D visualization, including modifications made because of the COVID-19 pandemic. Key findings of this study include that close reading is an effective formative method to elicit information related to interpretation and critique; user subject position; and suspicion or skepticism. Information gained through close reading is valuable in the visualization design and iteration processes, both related to designing features and other formal elements more effectively, as well as in considering larger questions of context and framing. 

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2. What Does the Chart Say? Grouping Cues Guide Viewer Comparisons and Conclusions in Bar Charts

   https://doi.org/10.1109/TVCG.2023.3289292  

   Bearfield, Cindy Xiong; Stokes, Chase; Lovett, Andrew; Franconeri, Steven (January 2024, IEEE Transactions on Visualization and Computer Graphics)

   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. 

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3. DIVI: Dynamically Interactive Visualization

   https://doi.org/10.1109/TVCG.2023.3327172  

   Snyder, Luke S.; Heer, Jeffrey (January 2024, IEEE Transactions on Visualization and Computer Graphics)

   Dynamically Interactive Visualization (DIVI) is a novel approach for orchestrating interactions within and across static visualizations. DIVI deconstructs Scalable Vector Graphics charts at runtime to infer content and coordinate user input, decoupling interaction from specification logic. This decoupling allows interactions to extend and compose freely across different tools, chart types, and analysis goals. DIVI exploits positional relations of marks to detect chart components such as axes and legends, reconstruct scales and view encodings, and infer data fields. DIVI then enumerates candidate transformations across inferred data to perform linking between views. To support dynamic interaction without prior specification, we introduce a taxonomy that formalizes the space of standard interactions by chart element, interaction type, and input event. We demonstrate DIVI's usefulness for rapid data exploration and analysis through a usability study with 13 participants and a diverse gallery of dynamically interactive visualizations, including single chart, multi-view, and cross-tool configurations. 

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4. Regression of exchangeable relational arrays

   https://doi.org/10.1093/biomet/asac031  

   Marrs, F W; Fosdick, B K; Mccormick, T H (January 2022, Biometrika)

   Summary Relational arrays represent measures of association between pairs of actors, often in varied contexts or over time. Trade flows between countries, financial transactions between individuals, contact frequencies between school children in classrooms and dynamic protein-protein interactions are all examples of relational arrays. Elements of a relational array are often modelled as a linear function of observable covariates. Uncertainty estimates for regression coefficient estimators, and ideally the coefficient estimators themselves, must account for dependence between elements of the array, e.g., relations involving the same actor. Existing estimators of standard errors that recognize such relational dependence rely on estimating extremely complex, heterogeneous structure across actors. This paper develops a new class of parsimonious coefficient and standard error estimators for regressions of relational arrays. We leverage an exchangeability assumption to derive standard error estimators that pool information across actors, and are substantially more accurate than existing estimators in a variety of settings. This exchangeability assumption is pervasive in network and array models in the statistics literature, but not previously considered when adjusting for dependence in a regression setting with relational data. We demonstrate improvements in inference theoretically, via a simulation study, and by analysis of a dataset involving international trade. 

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5. Cognitive efforts associated with spatial ability under altered spatial conditions: An eye-tracking study

   https://doi.org/10.54941/ahfe1004480  

   Salehi, Faezeh; Pariafsai, Fatemeh; Dixit, Manish (January 2024, Intelligent Human Systems Integration (IHSI 2024), Vol. 119, 2024, 165–175)

   Spatial ability is the ability to generate, store, retrieve, and transform visual information to mentally represent a space and make sense of it. This ability is a critical facet of human cognition that affects knowledge acquisition, productivity, and workplace safety. Although having improved spatial ability is essential for safely navigating and perceiving a space on earth, it is more critical in altered environments of other planets and deep space, which may pose extreme and unfamiliar visuospatial conditions. Such conditions may range from microgravity settings with the misalignment of body and visual axes to a lack of landmark objects that offer spatial cues to perceive size, distance, and speed. These altered visuospatial conditions may pose challenges to human spatial cognitive processing, which assists humans in locating objects in space, perceiving them visually, and comprehending spatial relationships between the objects and surroundings. The main goal of this paper is to examine if eye-tracking data of gaze pattern can indicate whether such altered conditions may demand more mental efforts and attention. The key dimensions of spatial ability (i.e., spatial visualization, spatial relations, and spatial orientation) are examined under the three simulated conditions: (1) aligned body and visual axes (control group); (2) statically misaligned body and visual axes (experiment group I); and dynamically misaligned body and visual axes (experiment group II). The three conditions were simulated in Virtual Reality (VR) using Unity 3D game engine. Participants were recruited from Texas A&M University student population who wore HTC VIVE Head-Mounted Displays (HMDs) equipped with eye-tracking technology to work on three spatial tests to measure spatial visualization, orientation, and relations. The Purdue Spatial Visualization Test: Rotations (PSVT: R), the Mental Cutting Test (MCT), and the Perspective Taking Ability (PTA) test were used to evaluate the spatial visualization, spatial relations, and spatial orientation of 78 participants, respectively. For each test, gaze data was collected through Tobii eye-tracker integrated in the HTC Vive HMDs. Quick eye movements, known as saccades, were identified by analyzing raw eye-tracking data using the rate of change of gaze position over time as a measure of mental effort. The results showed that the mean number of saccades in MCT and PSVT: R tests was statistically larger in experiment group II than in the control group or experiment group I. However, PTA test data did not meet the required assumptions to compare the mean number of saccades in the three groups. The results suggest that spatial relations and visualization may require more mental effort under dynamically misaligned idiotropic and visual axes than aligned or statically misaligned idiotropic and visual axes. However, the data could not reveal whether spatial orientation requires more/less mental effort under aligned, statically misaligned, and dynamically misaligned idiotropic and visual axes. The results of this study are important to understand how altered visuospatial conditions impact spatial cognition and how simulation- or game-based training tools can be developed to train people in adapting to extreme or altered work environments and working more productively and safely. 

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