論文アブストラクト： Communicating the right affect, a feeling, experience or emotion, is critical in creating engaging visual communication. We carried out three studies examining how different color properties (lightness, chroma and hue) and different palette properties (combinations and distribution of colors) contribute to different affective interpretations in information visualization where the numbers of colors is typically smaller than the rich palettes used in design. Our results show how color and palette properties can be manipulated to achieve affective expressiveness even in the small sets of colors used for data encoding in information visualization.
論文アブストラクト： Information visualizations use interactivity to enable user-driven querying of visualized data. However, users' interactions with their internal representations, including their expectations about data, are also critical for a visualization to support learning. We present multiple graphically-based techniques for eliciting and incorporating a user's prior knowledge about data into visualization interaction. We use controlled experiments to evaluate how graphically eliciting forms of prior knowledge and presenting feedback on the gap between prior knowledge and the observed data impacts a user's ability to recall and understand the data. We find that participants who are prompted to reflect on their prior knowledge by predicting and self-explaining data outperform a control group in recall and comprehension. These effects persist when participants have moderate or little prior knowledge on the datasets. We discuss how the effects differ based on text versus visual presentations of data. We characterize the design space of graphical prediction and feedback techniques and describe design recommendations.
論文アブストラクト： Observing trends and predicting future values are common tasks for viewers of bivariate data visualizations. As many charts do not explicitly include trend lines or related statistical summaries, viewers often visually estimate trends directly from a plot. How reliable are the inferences viewers draw when performing such regression by eye? Do particular visualization designs or data features bias trend perception? We present a series of crowdsourced experiments that assess the accuracy of trends estimated using regression by eye across a variety of bivariate visualizations, and examine potential sources of bias in these estimations. We find that viewers accurately estimate trends in many standard visualizations of bivariate data, but that both visual features (e.g., "within-the-bar" bias) and data features (e.g., the presence of outliers) can result in visual estimates that systematically diverge from standard least-squares regression models.
論文アブストラクト： We explore the relative merits of matrix, node-link and combined side-by-side views for the visualisation of weighted networks with three controlled studies: (1) finding the most effective visual encoding for weighted edges in matrix representations; (2) comparing matrix, node-link and combined views for static weighted networks; and (3) comparing MatrixWave, Sankey and combined views of both for event-sequence data. Our studies underline that node-link and matrix views are suited to different analysis tasks. For the combined view, our studies show that there is a perceptually complementary effect in terms of improved accuracy for some tasks, but that there is a cost in terms of longer completion time than the faster of the two techniques alone. Eye-movement data shows that for many tasks participants strongly favour one of the two views, after trying both in the training phase.