Interaction enables users to navigate large amounts of data effectively, supports cognitive processing, and increases data representation methods. However, there have been few attempts to empirically demonstrate whether adding interaction to a static visualization improves its function beyond popular beliefs. In this paper, we address this gap. We use a classic Bayesian reasoning task as a testbed for evaluating whether allowing users to interact with a static visualization can improve their reasoning. Through two crowdsourced studies, we show that adding interaction to a static Bayesian reasoning visualization does not improve participants' accuracy on a Bayesian reasoning task. In some cases, it can significantly detract from it. Moreover, we demonstrate that underlying visualization design modulates performance and that people with high versus low spatial ability respond differently to different interaction techniques and underlying base visualizations. Our work suggests that interaction is not as unambiguously good as we often believe; a well designed static visualization can be as, if not more, effective than an interactive one.
Structured Visualization Design Knowledge for Grounding Generative Reasoning and Situated Feedback
University of Vienna（维也纳大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Carnegie Mellon University（卡内基梅隆大学）；Faculty of Computer Science, University of Vienna（维也纳大学计算机学院）；Austrian Institute of Technology（奥地利技术研究所）
Graphionale: How Graph Visualizations of LLM Rationales Affect Human Decision Making
Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究联盟）；National University of Singapore（新加坡国立大学）；Purdue University（普渡大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Massachusetts Institute of Technology（麻省理工学院）
A Smartphone App to Support Sedentary Behavior Change by Visualizing Personal Mobility Patterns and Action Planning (SedVis): Development and Pilot Study
VizAnchor: Decoding Manipulation Intent from Tampering Visualizations via Dual-Anchor Reasoning
School of Data Science and Engineering, East China Normal University（华东师范大学数据科学与工程学院）；Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology（香港科技大学新兴跨学科领域分部）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）
Automated Visualization Code Synthesis via Multi-Path Reasoning and Feedback-Driven Optimization
AI Research, Enhans, Seoul, South Korea Innovation \& Technology, KAIST, Daejeon, South Korea Department of Computer Science, University of California, Berkeley, CA, United States Department of Electrical
