arXiv subjects

Anna L. Chinni

Publications and source records attributed to Anna L. Chinni.

3 recordsLinked to original sources

arXiv2026

Guidelines Are Not Rules: Characterizing Terminologies around Visualization Design Guidelines

A common expectation in visualization research is that outcomes recommend how researchers and practitioners take action or make design decisions. We often express these as "guidelines." Yet, the term "guideline" is both ambiguous and loosely defined, and what one researcher considers a guideline may be too broad, too loose, or too strict for another. We take a closer look at a broader set of terms that can express desirable results around visualization research, and untangle how these words are understood in the community in relation to other similar terms. We base our work on an exploratory study with experts, followed by a crowdsourcing study with a separate mapping phase (n=30) and rating phase (n=42) targeting input from the broader visualization community, and an analysis of the use of terminology in 3,877 IEEE VIS papers published from 1990 to 2024. Based on our findings, we call for more nuanced, precise discussions of research outcomes and their communication to the broader community, including practitioners and students.

cs.HC↗

arXiv2026

Predicting affective connotation of visualizations from their constituent colors

With increasing evidence that affective connotation (emotional association) is an important aspect of visual communication, there is a need for methods to predict affective connotation of visualizations. Many aspects of visualization design, including colors, textures, and shapes, can contribute to affective connotation, and a key question is how multiple design properties combine to determine the emotion association of a whole visualization. In this study, we focused specifically on color and tested whether it is possible to predict the affective connotation of whole visualizations by aggregating the emotion associations of the individual, constituent colors (additivity hypothesis). We also tested whether accounting for the size of colored regions, as determined by the underlying dataset, improved predictions (data-dependence hypothesis). We found that for colormap data visualizations in which colors were well-distributed across all colors in the color scale, the mean estimated associations of individual colors effectively predicted emotional associations of the maps as a whole (additivity; Exp. 1). For colormaps whose underlying datasets were biased to map more to colors at one end of the color scale, emotional associations were better predicted by a weighted mean that accounted for color frequency in the colormap (data-dependence; Exp. 2). Effects of additivity and data-dependence generalized to dot plots and bar charts (Exp. 3). These results suggest it is viable to predict affective connotation of whole visualizations from their individual design components, which has important implications for automating affective visualization design to support visual communication.

cs.HC↗

arXiv2026

Divided Attention Amplifies the Importance of Expectation-Aligned Visualization Design

Studies have shown that visualization design affects interpretability when visualization interpretation is the user's sole task. However, in real-world settings, users often engage with visualizations while performing concurrent tasks, such as when users simultaneously monitor alerts or respond to messages. Such divided attention may alter how users interpret visualizations, potentially increasing the importance of designs that align with viewer expectations. We investigated this possibility through two experiments comparing visualization interpretation under single-task and dual-task conditions. Specifically, we examined how well-established inferred mappings between color, spatial position, and semantic concepts affect interpretation when users perform a concurrent task, both with unlimited viewing time (Exp. 1) and under limited viewing time (Exp. 2). Our results show that divided attention amplifies the performance gap between expectation-aligned and expectation-violating designs, affecting response time, interpretation accuracy, and the ability to produce a judgment under time constraints. To explain these results, we model the user's decision-making process using a Linear Ballistic Accumulator (LBA) framework. Our findings highlight the increased importance of aligning visualization designs with viewer expectations under divided attention and introduce a process-oriented modeling approach to understanding how expectation and multitasking shape visualization interpretation.

cs.HC↗