Professional Differences: A Comparative Study of Visualization Task Performance and Spatial Ability Across Disciplines
Kyle Wm. Hall, Anthony Kouroupis, Anastasia Bezerianos, Danielle Albers Szafir, Christopher Collins
摘要
Problem-driven visualization work is rooted in deeply understanding the data, actors, processes, and workflows of a target domain. However, an individual's personality traits and cognitive abilities may also influence visualization use. Diverse user needs and abilities raise natural questions for specificity in visualization design: Could individuals from different domains exhibit performance differences when using visualizations? Are any systematic variations related to their cognitive abilities? This study bridges domain-specific perspectives on visualization design with those provided by cognition and perception. We measure variations in visualization task performance across chemistry, computer science, and education, and relate these differences to variations in spatial ability. We conducted an online study with over 60 domain experts consisting of tasks related to pie charts, isocontour plots, and 3D scatterplots, and grounded by a well-documented spatial ability test. Task performance (correctness) varied with profession across more complex visualizations (isocontour plots and scatterplots), but not pie charts, a comparatively common visualization. We found that correctness correlates with spatial ability, and the professions differ in terms of spatial ability. These results indicate that domains differ not only in the specifics of their data and tasks, but also in terms of how effectively their constituent members engage with visualizations and their cognitive traits. Analyzing participants' confidence and strategy comments suggests that focusing on performance neglects important nuances, such as differing approaches to engage with even common visualizations and potential skill transference. Our findings offer a fresh perspective on discipline-specific visualization with specific recommendations to help guide visualization design that celebrates the uniqueness of the disciplines and individuals we seek to serve.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper8
- Negative Emotions, Positive Outcomes? Exploring the Communication of Negativity in Serious Data StoriesXingyu Lan, Yanqiu Wu, Yang Shi, Qing Chen 等CHI 2022 · 被引用 43 次
- Do You See What I See? A Qualitative Study Eliciting High-Level Visualization ComprehensionGhulam Jilani Quadri, Arran Zeyu Wang, Zhehao Wang, Jennifer Adorno Nieves 等CHI 2024 · 被引用 37 次
- PREVis: Perceived Readability Evaluation for VisualizationsAnne-Flore Cabouat, Tingying He, Petra Isenberg, Tobias IsenbergIEEE VIS 2024 · 被引用 20 次
- My Model is Unfair, Do People Even Care? Visual Design Affects Trust and Perceived Bias in Machine LearningAimen Gaba, Zhanna Kaufman, Jason Cheung, Marie Shvakel 等IEEE VIS 2023 · 被引用 20 次
- Measuring Effects of Spatial Visualization and Domain on Visualization Task Performance: A Comparative StudySara Tandon, Alfie Abdul-Rahman, Rita BorgoIEEE VIS 2022 · 被引用 8 次
它引用的顶会 Paper1
相关 Paper
- Visual Task Performance and Spatial Abilities: An Investigation of Artists and MathematiciansSara Tandon, Alfie Abdul-Rahman, Rita BorgoCHI 2023 · 被引用 2 次
- Does Interaction Improve Bayesian Reasoning with Visualization?Abigail Mosca, Alvitta Ottley, Remco ChangCHI 2021 · 被引用 14 次
- Characterizing Visualization Perception with Psychological Phenomena: Uncovering the Role of Subitizing in Data VisualizationArran Zeyu Wang, Ghulam Jilani Quadri, Mengyuan Zhu, Chin Tseng 等IEEE VIS 2025
- A Framework for Multiclass Contour VisualizationSihang Li, Jiacheng Yu, Mingxuan Li, Le Liu 等IEEE VIS 2022 · 被引用 4 次
- Toward Filling a Critical Knowledge Gap: Charting the Interactions of Age with Task and VisualizationZack While, Ali SarvghadCHI 2025 · 被引用 5 次
