Why Combining Text and Visualization Could Improve Bayesian Reasoning: A Cognitive Load Perspective

要旨

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.

著者
Melanie Bancilhon
Washington University in St Louis, St Louis, Missouri, United States
Amanda Wright
Washington University in St. Louis, St Louis, Missouri, United States
Sunwoo Ha
Washington University in St. Louis, St. Louis, Missouri, United States
R. Jordan Crouser
Smith College, Northampton, Massachusetts, United States
Alvitta Ottley
Washington University in St. Louis, St. Louis, Missouri, United States
論文URL

https://doi.org/10.1145/3544548.3581218

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: Making Sense & Decisions with Visualization

Hall D
6 件の発表
2023-04-26 23:30:00
2023-04-27 00:55:00