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In the face of complex decisions, people often engage in a three-stage process that spans from (1) exploring and analyzing pertinent information (intelligence); (2) generating and exploring alternative options (design); and ultimately culminating in (3) selecting the optimal decision by evaluating discerning criteria (choice). We can fairly assume that all good visualizations aid in the intelligence stage by enabling data exploration and analysis. Yet, to what degree and how do visualization systems currently support the other decision making stages, namely design and choice? To explore this question, we conducted a comprehensive review of decision-focused visualization tools by examining publications in major visualization journals and conferences, including VIS, EuroVis, and CHI, spanning all available years. We employed a deductive coding method and in-depth analysis to assess if and how visualization tools support design and choice. Specifically, we examined each visualization tool by (i) its degree of visibility for displaying decision alternatives, criteria, and preferences, and (ii) its degree of flexibility for offering means to manipulate the decision alternatives, criteria, and preferences with interactions such as adding, modifying, changing mapping, and filtering. Our review highlights the opportunities and challenges and reveals a surprising scarcity of tools that support all stages, and while most tools excel in offering visibility for decision criteria and alternatives, the degree of flexibility to manipulate these elements is often limited, and the lack of tools that accommodate decision preferences and their elicitation is notable. Future research could explore enhancing flexibility levels and variety, exploring novel visualization paradigms, increasing algorithmic support, and ensuring that this automation is user-controlled via the enhanced flexibility levels.

While domain characterization has become an integral part of visualization design studies, methodological prescriptions are rare. An underrepresented aspect in existing approaches is domain expertise. Knowledge elicitation methods from cognitive science might help but have not yet received much attention for domain characterization. We propose the Critical Decision Method (CDM) to the visualization domain to provide descriptive steps that open up a knowledge-based perspective on domain characterization. The CDM uses retrospective interviews to reveal expert judgment involved in a challenging situation. We apply it to study three domain problems, reflect on our practical experience, and discuss its relevance to domain characterization in visualization research. We found the CDM's realism and subjective nature to be well suited for eliciting cognitive aspects of high-level task performance. Our insights might guide other researchers in conducting domain characterization with a focus on domain knowledge and cognition. With our work, we hope to contribute to the portfolio of meaningful methods used to inform visualization design and to stimulate discussions regarding prescriptive steps for domain characterization.

International audience; It has been widely suggested that a key goal of visualization systems is to assist decision making, but is this true? We conduct a critical investigation on whether the activity of decision making is indeed central to the visualization domain. By approaching decision making as a user task, we explore the degree to which decision tasks are evident in visualization research and user studies. Our analysis suggests that decision tasks are not commonly found in current visualization task taxonomies and that the visualization field has yet to leverage guidance from decision theory domains on how to study such tasks. We further found that the majority of visualizations addressing decision making were not evaluated based on their ability to assist decision tasks. Finally, to help expand the impact of visual analytics in organizational as well as casual decision making activities, we initiate a research agenda on how decision making assistance could be elevated throughout visualization research.

This is a replication of Experiment 1 (https://osf.io/t82nq) with subtle changes to the experiment instructions that clarify further the nature of the tasks and decrease experiment duration. We also changed the numerical values of the trials from fixed across trials to variate across trials to increase the difficulty of the task.

Researchers tend to use the terms “judgment” and “decision making” interchangeably. This conflation of terminology leads to a lack of clarity on whether data visualization users make suboptimal decisions because they perform inaccurate judgments of probabilities, or because they use alternative yet currently unknown heuristics. We present an experiment in which participants were exposed to data visualizations and responded to questions involving probability and cost-benefit estimations. The goal is to understand how the task (i.e., judgment vs. decision making), the underline scenario (i.e., sports vs. humanitarian) as well as the type of visualization (i.e., quantile plots, densities, or probability bars) affect response accuracy.

Implicit Statistical Learning (ISL) studies how exposing individuals to repeated statistical patterns can help develop skills in the absence of conscious awareness, such as learning a language or detecting familiar shapes. This paper transposes ISL in the context of menu design learnability. Our analysis of menu patterns in various applications from the 80s to today reveals a consistent linear pattern with command names on the left and keyboard shortcut cues aligned on the right. We then develop a design space of menu patterns by manipulating two factors of ISL theory, spatial proximity (distance) and relative positioning between commands and shortcut cues. We empirically compare four menu patterns of this design space on whether they can improve keyboard shortcut adoption through two controlled experiments. Results did not capture clear effects among the menu patterns, suggesting that ISL in the context of HCI might involve more complex factors than initially anticipated, such as the time the users are exposed to the menu pattern. We reflect on the challenges in applying theories from cognitive science to HCI and hope that our systematic methodology and experiment designs will serve as a basis for encouraging more studies in the area.

International audience; We investigated retroactive transfer when users alternate between different interfaces. Retroactive transfer is the influence of a newly learned interface on users' performance with a previously learned interface. In an interview study, participants described their experiences when alternating between different interfaces, e.g. different operating systems, devices or techniques. Negative retroactive transfer related to text entry was the most frequently reported incident. We then reported a laboratory experiment that investigated the impact of similarity between two abstract keyboard layouts, and the number of alternations between them, on retroactive interference. Results indicated that even small changes in the interference interface produced a significant performance drop for the entire previously learned interface. The amplitude of this performance drop decreases with the number of alternations. We suggest that retroactive transfer should receive more attention in HCI, as the ubiquitous nature of interactions across applications and systems requires users to increasingly alternate between similar interfaces.

Compact visual summaries of spatio-temporal movement data often strive to express accurate positions of movers. We present SpatialRugs, a technique to enhance the spatial awareness of movements in dense pixel visualizations. SpatialRugs apply 2D colormaps to visualize location mapped to a juxtaposed display. We explore the effect of various colormaps discussing perceptual limitations and introduce a custom color-smoothing method to mitigate distorted patterns of collective movement behavior., EuroVis Workshop on Visual Analytics (EuroVA), Visual Analytics Methods and Applications, 1, 5, Juri F. Buchmüller, Udo Schlegel, Eren Cakmak, Daniel A. Keim, and Evanthia Dimara, CCS Concepts: Human-centered computing --> Visualization techniques

There are problems neither humans nor computers can solve alone. Computer-supported visualizations are a well-known solution when humans need to reason based on a large amount of data. The more effective a visualization, the more complex the problems that can be solved. In information visualization research, to be considered effective, a visualization typically needs to support data comprehension. Evaluation methods focus on whether users indeed understand the displayed data, can gain insights and are able to perform a set of analytic tasks, e.g., to identify if two variables are correlated. This dissertation suggests moving beyond this "visual analysis paradigm" by extending research focus to another type of task: decision making. Decision tasks are essential to everybody, from the manager of a company who needs to routinely make risky decisions to an ordinary person who wants to choose a career life path or simply find a camera to buy. Yet decisions do not merely involve information understanding and are difficult to study. Decision tasks can involve subjective preferences, do not always have a clear ground truth, and they often depend on external knowledge which may not be part of the displayed dataset. Nevertheless, decision tasks are neither part of visualization task taxonomies nor formally defined. Moreover, visualization research lacks metrics, methodologies and empirical works that validate the effectiveness of visualizations in supporting a decision. This dissertation provides an operational definition for a particular class of decision tasks and reports a systematic analysis to investigate the extent to which existing multidimensional visualizations are compatible with such tasks. It further reports on the first empirical comparison of multidimensional visualizations for their ability to support decisions and outlines a methodology and metrics to assess decision accuracy. It further explores the role of instructions in both decision tasks and equivalent analytic tasks, and identifies differences in accuracy between those tasks. Similarly to vision science that informs visualization researchers and practitioners on the limitations of human vision, moving beyond the visual analysis paradigm would mean acknowledging the limitations of human reasoning. This dissertation reviews decision theory to understand how humans should, could and do make decisions and formulates a new taxonomy of cognitive biases based on the user task where such biases occur. It further empirically shows that cognitive biases can be present even when information is well-visualized, and that a decision can be ``correct'' yet irrational, in the sense that people's decisions are influenced by irrelevant information. This dissertation finally examines how biases can be alleviated. Current methods for improving human reasoning often involve extensive training on abstract principles and procedures that often appear ineffective. Yet visualizations have an ace up their sleeve: visualization designers can re-design the environment to alter the way people process the data. This dissertation revisits decision theory to identify possible design solutions. It further empirically demonstrates that enriching a visualization with interactions that facilitate alternative decision strategies can yield more rational decisions. Through empirical studies, this dissertation suggests that the visual analysis paradigm cannot fully address the challenges of visualization-supported decision making, but that moving beyond can contribute to making visualization a powerful decision support tool.; Certains problèmes ne peuvent être résolus ni par les ordinateurs seuls ni par les humains seuls. La visualisation d'information est une solution commune quand il est nécessaire de raisonner sur de grandes quantités de données. Plus une visualisation est efficace, plus il est possible de résoudre des problèmes complexes. Dans la recherche en visualisation d'information, une visualisation est généralement considérée comme efficace quand elle permet de comprendre les données. Les méthodes d'évaluation cherchent à déterminer si les utilisateurs comprennent les données affichées et sont capables d'effectuer des tâches analytiques comme, par exemple, identifier si deux variables sont corrélées. Cette thèse suggère d'aller au-delà de ce ``paradigme de l'analyse visuelle'' et élargir le champ de recherche à un autre type de tâche: la prise de décision. Les tâches de décision sont essentielles à tous, du directeur d'entreprise qui doit prendre des décisions importantes à l'individu ordinaire qui choisit un plan de carrière ou désire simplement acheter un appareil photo. Néanmoins, les décisions ne se résument pas à la simple compréhension de l'information et sont difficiles à étudier. Elles peuvent impliquer des préférences subjectives, n'ont pas toujours de vérité de terrain, et dépendent souvent de connaissances externes aux données visualisées. Pourtant, les tâches de décision ne font pas partie des taxonomies de tâches en visualisation et n'ont pas été bien définies. De plus, la recherche manque de métriques, de méthodes et de travaux empiriques pour valider l'efficacité des visualisations pour la prise de décision. Cette thèse offre une définition opérationnelle pour une classe particulière de tâches de décision, et présente une analyse systématique qui identifie les visualisations multidimensionnelles compatibles avec ces tâches. Elle présente en outre la première comparaison empirique de techniques de visualisation multidimensionnelle basée sur leur capacité à aider la décision, et esquisse une méthodologie et des métriques pour évaluer la qualité des décisions. Elle explore ensuite le rôle des instructions dans les tâches de décision et des tâches analytiques équivalentes, et identifie des différences de performance entre les deux tâches. De même que les sciences de la vision informent la visualisation d'information sur les limites de la vision humaine, aller au-delà du paradigme de l'analyse visuelle implique de prendre en compte les limites du raisonnement humain. Cette thèse passe en revue la théorie de la décision afin de mieux comprendre comment les humains prennent des décisions, et formule une nouvelle taxonomie de biais cognitifs basée sur la tâche utilisateur. En outre, elle démontre empiriquement que des biais peuvent être présents même quand l'information est bien visualisée, et qu'une décision peut être ``correcte'' mais néanmoins irrationnelle, dans le sens où elle est influencée par des informations non pertinentes. Cette thèse examine finalement comment mitiger les biais. Les méthodes pour améliorer le raisonnement humain reposent souvent sur un entraînement intensif à des principes et à des procédures abstraites, qui se révèlent souvent peu efficaces. Les visualisations offrent une opportunité dans la mesure où ses concepteurs peuvent remodeler l'environnement pour changer la façon dont les utilisateurs assimilent les données. Cette thèse passe en revue la théorie de la décision pour identifier de possibles solutions de conception. De plus, elle démontre empiriquement que supplémenter une visualisation par des interactions qui facilitent des stratégies de décision alternatives peut mener à des décisions plus rationnelles. Via des études empiriques, cette thèse suggère que le paradigme de l'analyse visuelle n'est pas en mesure de relever tous les défis de la prise de décision aidée de la visualisation, mais qu'aller au-delà peut contribuer à faire de la visualisation un puissant outil de prise de décision.

International audience; Visualizations designed to support multi-attribute decisions often use colors to encode the identity of the attributes. This approachfacilitates mapping of attributes across multiple coordinated views but it has certain limitations: colors often communicatesemantics (e.g., red stands for “danger”) deemed to influence the user’s preference, and qualitative color palettes areof limited scalability. We are currently developing a tool with an alternative approach, DCPAIRS: a pairs plot based decisionmaking support tool that employs a compact overview of the decision space and uses visual encodings that communicate uncertaintyand suboptimal preference elicitation. Instead of encoding the identity of attributes we use colors for user-authoredannotations to support the decision making process. A use case scenario of a prospective undergraduate student choosing auniversity from the “QS world university ranking” dataset illustrates the functionality of the tool.