Susceptibility of domain experts to color manipulation indicate a need for design principles in data visualization (original) (raw)
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IEEE Transactions on Visualization and Computer Graphics, 2018
Geographical maps encoded with rainbow color scales are widely used by climate scientists. Despite a plethora of evidence from the visualization and vision sciences literature about the shortcomings of the rainbow color scale, they continue to be preferred over perceptually optimal alternatives. To study and analyze this mismatch between theory and practice, we present a web-based user study that compares the effect of color scales on performance accuracy for climate-modeling tasks. In this study, we used pairs of continuous geographical maps generated using climatological metrics for quantifying pairwise magnitude difference and spatial similarity. For each pair of maps, 39 scientist-observers judged: i) the magnitude of their difference, ii) their degree of spatial similarity, and iii) the region of greatest dissimilarity between them. Besides the rainbow color scale, two other continuous color scales were chosen such that all three of them covaried two dimensions (luminance monotonicity and hue banding), hypothesized to have an impact on task performance. We also analyzed subjective performance measures, such as user confidence, perceived accuracy, preference, and familiarity in using the different color scales. We found that monotonic luminance scales produced significantly more accurate judgments of magnitude difference but were not superior in spatial comparison tasks, and that hue banding had differential effects based on the task and conditions. Scientists expressed the highest preference and perceived confidence and accuracy with the rainbow, despite its poor performance on the magnitude comparison tasks. We also report on interesting interactions among stimulus conditions, tasks, and color scales, that lead to open research questions.
Cognitive Models of the Influence of Color Scale on Data Visualization Tasks
Human Factors: The Journal of the Human Factors and Ergonomics Society, 2009
Objective: Computational models of identification and relative comparison tasks performed on color-coded data visualizations were presented and evaluated against two experiments. In this context, the possibility of a dual-use color scale, useful for both tasks, was explored, and the use of the legend was a major focus. Background: Multicolored scales are superior to ordered brightness scales for identification tasks, such as determining the absolute numeric value of a represented item, whereas ordered brightness scales are superior for relative comparison tasks, such as determining which of two represented items has a greater value. Method: Computational models were constructed for these tasks, and their predictions were compared with the results of two experiments. Results: The models fit the experimental results well. A multicolored, brightness-ordered dual-use scale supported high accuracy on both tasks and fast responses on a comparison task but relatively slower responses on th...
Decision making with visualizations: a cognitive framework across disciplines
Visualizations—visual representations of information, depicted in graphics—are studied by researchers in numerous ways, ranging from the study of the basic principles of creating visualizations, to the cognitive processes underlying their use, as well as how visualizations communicate complex information (such as in medical risk or spatial patterns). However, findings from different domains are rarely shared across domains though there may be domain-general principles underlying visualizations and their use. The limited cross-domain communication may be due to a lack of a unifying cognitive framework. This review aims to address this gap by proposing an integrative model that is grounded in models of visualization comprehension and a dual-process account of decision making. We review empirical studies of decision making with static two-dimensional visualizations motivated by a wide range of research goals and find significant direct and indirect support for a dual-process account of decision making with visualizations. Consistent with a dual-process model, the first type of visualization decision mechanism produces fast, easy, and computationally light decisions with visualizations. The second facilitates slower, more contemplative, and effortful decisions with visualizations. We illustrate the utility of a dual-process account of decision making with visualizations using four cross-domain findings that may constitute universal visualization principles. Further, we offer guidance for future research, including novel areas of exploration and practical recommendations for visualization designers based on cognitive theory and empirical findings.
Understanding Expert and Novice Meaning-Making from Global Data Visualizations
Scientists often create visualizations with cultural conventions such that novices, who lack the extensive training of professionals, cannot make meaning from them in the same way as experts. This research addresses the question of how scientists and novices analyze global data visualizations and how they use scaffolding, that is, supporting details or labels added to the images to clarify the meaning of the data. The project uses multiple methodologies from education and neuroscience to address questions of how people make meaning.
A Task-based Taxonomy of Cognitive Biases for Information Visualization
IEEE Transactions on Visualization and Computer Graphics (TVCG/VIS '19), 2019
Information visualization designers strive to design data displays that allow for efficient exploration, analysis, and communication of patterns in data, leading to informed decisions. Unfortunately, human judgment and decision making are imperfect and often plagued by cognitive biases. There is limited empirical research documenting how these biases affect visual data analysis activities. Existing taxonomies are organized by cognitive theories that are hard to associate with visualization tasks. Based on a survey of the literature we propose a task-based taxonomy of 154 cognitive biases organized in 7 main categories. We hope the taxonomy will help visualization researchers relate their design to the corresponding possible biases, and lead to new research that detects and addresses biased judgment and decision making in data visualization.
Using color in visualization: A survey
Computers & Graphics, 2011
Color mapping is an important technique used in visualization to build visual representations of data and information. With output devices such as computer displays providing a large number of colors, developers sometimes tend to build their visualization to be visually appealing, while forgetting the main goal of clear depiction of the underlying data.
What Geoscience Experts And Novices Look At, And What They See, When Viewing Data Visualizations
Journal of Astronomy & Earth Sciences Education (JAESE), 2016
This study examines how geoscience experts and novices make meaning from an iconic type of data visualization: shaded relief images of bathymetry and topography. Participants examined, described, and interpreted a global image, two high-resolution seafloor images, and 2 high-resolution continental images, while having their gaze direction eye-tracked and their utterances and gestures videoed. In addition, experts were asked about how they would coach an undergraduate intern on how to interpret this data. Not unexpectedly, all experts were more skillful than any of the novices at describing and explaining what they were seeing. However, the novices showed a wide range of performance. Along the continuum from weakest novice to strongest expert, proficiency developed in the following order: making qualitative observations of salient features, making simple interpretations, making quantitative observations. The eye-tracking analysis examined how the experts and novices invested 20 ...
A Crowdsourced Study of Visual Strategies for Mitigating Confirmation Bias
2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
Confirmation bias is a type of cognitive bias that involves seeking and prioritizing information that conforms to a pre-existing view or hypothesis that can negatively affect the decision-making process. We investigate the manifestation and mitigation of confirmation bias with an emphasis on the use of visualization. In a series of Amazon Mechanical Turk studies, participants selected evidence that supported or refuted a given hypothesis. We demonstrated the presence of confirmation bias and investigated the use of five simple visual representations, using color, positional, and length encodings for mitigating this bias. We found that at worst, visualization had no effect in the amount of confirmation bias present, and at best, it was successful in mitigating the bias. We discuss these results in light of factors that can complicate visual debiasing in non-experts.