Why Combining Text and Visualization Could Improve Bayesian Reasoning: A Cognitive Load Perspective
February 01, 2023 Β· Declared Dead Β· π International Conference on Human Factors in Computing Systems
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Authors
Melanie Bancilhon, AJ Wright, Sunwoo Ha, Jordan Crouser, Alvitta Ottley
arXiv ID
2302.00707
Category
cs.HC: Human-Computer Interaction
Citations
17
Venue
International Conference on Human Factors in Computing Systems
Last Checked
4 months ago
Abstract
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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