What is Interaction for Data Visualization?

International audienceInteraction is fundamental to data visualization, but what "interaction" means in the context of visualization is ambiguous and confusing. We argue that this confusion is due to a lack of consensual definition. To tackle this problem, we start by synthesizing an inclusive view of interaction in the visualization community-including insights from information visualization, visual analytics and scientific visualization, as well as the input of both senior and junior visualization researchers. Once this view takes shape, we look at how interaction is defined in the field of human-computer interaction (HCI). By extracting commonalities and differences between the views of interaction in visualization and in HCI, we synthesize a definition of interaction for visualization. Our definition is meant to be a thinking tool and inspire novel and bolder interaction design practices. We hope that by better understanding what interaction in visualization is and what it can be, we will enrich the quality of interaction in visualization systems and empower those who use them

From Information to Choice: A Critical Inquiry Into Visualization Tools for Decision Making

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

Accounting for Availability Biases in Information Visualization

International audienceThe availability heuristic is a strategy that people use to make quick decisions but often lead to systematic errors. We propose three ways that visualization could facilitate unbiased decision-making. First, visualizations can alter the way our memory stores the events for later recall, so as to improve users' long-term intuitions. Second, the known biases could lead to new visualization guidelines. Third, we suggest the design of decision-making tools that are inspired by heuristics, e.g. suggesting intuitive approximations, rather than target to present exhaustive comparisons of all possible outcomes, or automated solutions for choosing decisions

The Attraction Effect in Information Visualization

International audience—The attraction effect is a well-studied cognitive bias in decision making research, where one's choice between two alternatives is influenced by the presence of an irrelevant (dominated) third alternative. We examine whether this cognitive bias, so far only tested with three alternatives and simple presentation formats such as numerical tables, text and pictures, also appears in visualiza-tions. Since visualizations can be used to support decision making — e.g., when choosing a house to buy or an employee to hire — a systematic bias could have important implications. In a first crowdsource experiment, we indeed partially replicated the attraction effect with three alternatives presented as a numerical table, and observed similar effects when they were presented as a scatterplot. In a second experiment, we investigated if the effect extends to larger sets of alternatives, where the number of alternatives is too large for numerical tables to be practical. Our findings indicate that the bias persists for larger sets of alternatives presented as scatterplots. We discuss implications for future research on how to further study and possibly alleviate the attraction effect

Supporting Domain Characterization in Visualization Design Studies With the Critical Decision Method

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

Truncating the Y-Axis: Threat or Menace?

Bar charts with y-axes that don't begin at zero can visually exaggerate effect sizes. However, advice for whether or not to truncate the y-axis can be equivocal for other visualization types. In this paper we present examples of visualizations where this y-axis truncation can be beneficial as well as harmful, depending on the communicative and analytic intent. We also present the results of a series of crowd-sourced experiments in which we examine how y-axis truncation impacts subjective effect size across visualization types, and we explore alternative designs that more directly alert viewers to this truncation. We find that the subjective impact of axis truncation is persistent across visualizations designs, even for designs with explicit visual cues that indicate truncation has taken place. We suggest that designers consider the scale of the meaningful effect sizes and variation they intend to communicate, regardless of the visual encoding

La visualisation d’information pour la prise de décision : identifier les biais et aller au-delà du paradigme de l'analyse visuelle

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

The Attraction Effect in Information Visualization

Supplementary material for the papers "The Attraction Effect in Information Visualization" and "Testing the Attraction Effect on Two Information Visualization Datasets

A Critical Reflection on Visualization Research: Where Do Decision Making Tasks Hide?

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