Why Combining Text and Visualization Could Improve Bayesian Reasoning: A Cognitive Load Perspective
Melanie Bancilhon, Amanda Wright, Sunwoo Ha, R. Jordan Crouser, Alvitta Ottley
摘要
Investigations into using visualization to improve Bayesian reasoning and advance risk communication have produced mixed results, suggesting that cognitive ability might affect how users perform with different presentation formats. Our work examines the cognitive load elicited when solving Bayesian problems using icon arrays, text, and a juxtaposition of text and icon arrays. We used a three-pronged approach to capture a nuanced picture of cognitive demand and measure differences in working memory capacity, performance under divided attention using a dual-task paradigm, and subjective ratings of self-reported effort. We found that individuals with low working memory capacity made fewer errors and experienced less subjective workload when the problem contained an icon array compared to text alone, showing that visualization improves accuracy while exerting less cognitive demand. We believe these findings can considerably impact accessible risk communication, especially for individuals with low working memory capacity.
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引用它的顶会 Paper3
- A Novel Lens on Metacognition in VisualizationMengyu Chen, Andrew Yang, Seungchan Min, Kristy A. Hamilton 等CHI 2025 · 被引用 7 次
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- Examining Effort in 1D Uncertainty Communication Using Individual Differences in Working Memory and NASA-TLXSpencer C. Castro, P. Samuel Quinan, Helia Hosseinpour, Lace M. K. PadillaIEEE VIS 2021 · 被引用 35 次
- Does Interaction Improve Bayesian Reasoning with Visualization?Abigail Mosca, Alvitta Ottley, Remco ChangCHI 2021 · 被引用 14 次
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