How People with Low and High Graph Literacy Process Health Graphs: Evidence from Eye-tracking

• Published in: Journal of behavioral decision making Vol. 29; no. 2-3; pp. 271 - 294
• Main Authors: Okan, Yasmina, Galesic, Mirta, Garcia-Retamero, Rocio
• Format: Journal Article
• Language: English
• Published: Chichester Blackwell Publishing Ltd 01.04.2016; Wiley Periodicals Inc
• Subjects: Decision making; eye movements; Eyes & eyesight; graph comprehension; graph literacy; Graph representations; Graphs; individual differences; Literacy; medical decision making
• ISSN: 0894-3257; 1099-0771
• DOI: 10.1002/bdm.1891

Graphs facilitate the communication of important quantitative information, often serving as effective decision support tools. Yet, graphs are not equally useful for all individuals, as people differ substantially in their graph literacy—the ability to understand graphically presented information. Although some features of graphs can be interpreted using spatial‐to‐conceptual mappings that can be established by adults and children with no graphing experience (e.g., “higher bars equal larger quantities”), other features are linked to arbitrary graph conventions (e.g., axes labels and scales). In two experiments, we examined differences in the processes underlying the comprehension of graphs presenting medical information in individuals with low and high graph literacy. Participants' eye movements were recorded while they interpreted graphs in which information in conventional features was incongruent with that conveyed by spatial features. Results revealed that participants with low graph literacy more often relied on misleading spatial‐to‐conceptual mappings and misinterpreted the data depicted. Higher graph literacy was often associated with more time spent viewing the conventional features containing essential information for accurate interpretations. This suggests that individuals with high graph literacy are better able to identify the task‐relevant information in graphs, and thus attend to the relevant features to a larger extent. Theoretical, methodological, and prescriptive implications for customization of decision‐support systems are discussed. Copyright © 2015 John Wiley & Sons, Ltd.