Effects of spatial compatibility on integration processes in graph comprehension

Huestegge, Lynn; Philipp, Andrea M.

doi:10.3758/s13414-011-0155-1

Cited by 20 publications

(30 citation statements)

References 53 publications

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“…Our results are the first to suggest that risk communicators who wish to promote risk‐avoidant behaviors without hindering people's risk understanding may consider the use of foreground‐only graphs including numerical labels. These labels should be placed next to the bars to capitalize on Gestalt principles of perceptual organization and facilitate integration processes . Foreground‐only displays with labels can encourage people to take steps to reduce risks that threaten their well‐being, without limiting their ability to make informed decisions based on an accurate risk understanding.…”

Section: Discussionmentioning

confidence: 99%

“…A key process for accurate graph interpretations is the so‐called integration phase, which involves inferring information from conventional features in graphs (e.g., axes labels, numerical values on scales, legends, or titles) and integrating this information with that inferred from the visual pattern . Unfortunately, there is increasing evidence that people often fail to incorporate information from such conventional features in their interpretations . Eye‐tracking data suggest that one of the reasons for this failure is that people do not allocate sufficient attention to such features …”

Section: Introductionmentioning

confidence: 99%

“…The positioning of the labels aimed to capitalize on the Gestalt principle of proximity, which suggests that the relative distance of elements to one another determines how these are grouped . Indeed, graphs that make an appropriate use of Gestalt principles of perceptual organization have been shown to facilitate the processes of relating visual patterns to variables and their values …”

Section: Introductionmentioning

confidence: 99%

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Designing Graphs that Promote Both Risk Understanding and Behavior Change

2017

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Graphs show promise for improving communications about different types of risks, including health risks, financial risks, and climate risks. However, graph designs that are effective at meeting one important risk communication goal (promoting risk‐avoidant behaviors) can at the same time compromise another key goal (improving risk understanding). We developed and tested simple bar graphs aimed at accomplishing these two goals simultaneously. We manipulated two design features in graphs, namely, whether graphs depicted the number of people affected by a risk and those at risk of harm (“foreground+background”) versus only those affected (“foreground‐only”), and the presence versus absence of simple numerical labels above bars. Foreground‐only displays were associated with larger risk perceptions and risk‐avoidant behavior (i.e., willingness to take a drug for heart attack prevention) than foreground+background displays, regardless of the presence of labels. Foreground‐only graphs also hindered risk understanding when labels were not present. However, the presence of labels significantly improved understanding, eliminating the detrimental effect of foreground‐only displays. Labels also led to more positive user evaluations of the graphs, but did not affect risk‐avoidant behavior. Using process modeling we identified mediators (risk perceptions, understanding, user evaluations) that explained the effect of display type on risk‐avoidant behavior. Our findings contribute new evidence to the graph design literature: unlike what was previously feared, we demonstrate that it is possible to design foreground‐only graphs that promote intentions for behavior change without a detrimental effect on risk understanding. Implications for the design of graphical risk communications and decision support are discussed.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Designing Graphs that Promote Both Risk Understanding and Behavior Change

2017

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“…Additionally, graph literacy and medicine‐related knowledge were not associated, and participants with low graph literacy knew as well as those with high graph literacy that graphs can be plotted misleadingly. An interesting question for future research would be to examine how graph literacy relates to other factors including working memory limitations (Huestegge & Philipp, ; Peebles & Cheng, , ), individual differences in spatial abilities (Feeney, Adams, Webber, & Ewbank, ), and math anxiety (Ashcraft & Kirk, ).…”

Section: Discussionmentioning

confidence: 99%

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

Okan

Galešić

García‐Retamero

2015

Behavioral Decision Making

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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., axis 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.Keywords: Graph comprehension, eye movements, medical decision making, individual differences, graph literacy GRAPH LITERACY AND PROCESSING OF HEALTH GRAPHS 3 How People with Low and High Graph Literacy Process Health Graphs: Evidence from Eye-TrackingGraphical displays such as line plots, bar charts, and icon arrays can serve as highly valuable tools for overcoming difficulties in the comprehension of numerical concepts, thus serving as highly effective decision support tools (Ancker, Senathirajah, Kukafka, & Starren, 2006; Garcia-Retamero & Cokely, 2011, 2013 Lipkus, 2007). Unfortunately, graphs are not equally useful for all individuals, as people in the general population differ substantially in their ability to understand graphically presented information (Galesic & Garcia-Retamero, 2011; Kutner, Greenberg, Jin, & Paulsen, 2006). These differences can affect the extent to which individuals benefit from visual displays (Gaissmaier et al., 2012; Garcia-Retamero & Cokely, 2014; Garcia-Retamero & Galesic, 2010; Okan, Garcia-Retamero, Cokely, & Maldonado, 2012). Yet the processes underlying graph comprehension in individuals with varying levels of graph literacy are not well understood. We used eye-tracking methodology to investigate this issue. Individual Differences in Graph LiteracyGraph literacy refers to ones ability to understand graphically presented information and includes general knowledge about making inferences from different graphic formats ...

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“…Grant & Spivey, 2002;Huestegge & Philipp, 2011). Only a small number of researchers has utilised eye-tracking techniques in the area of graph comprehension.…”

Section: Investigating Graph Comprehension Using Eye-tracking Techniquesmentioning

confidence: 99%

Eye Movements Indicate the Temporal Organisation of Information Processing in Graph Comprehension

Körner

Höfler

Tröbinger

et al. 2014

Applied Cognitive Psychology

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Hierarchical graphs (e.g. file system browsers and preference trees) represent objects (e.g. files and folders) as graph nodes and relations between them (e.g. sub-folder relations) as lines. We investigated the temporal organisation of two processes that are necessary for comprehending such graphs-search for the graph nodes and reasoning about their relation. We tracked eye movements to change graphs while participants interpreted them. In Experiment 1, we masked the graph at a time when search processes had finished but reasoning was hypothetically ongoing. We observed a dramatic deterioration in comprehension compared with unmasked graphs. In Experiment 2, we changed the relation between critical graph nodes after search for them had finished, unbeknownst to participants. Participants mostly based their response on the graph as presented after the change. These results suggest that comprehension processes are organised in a sequential manner, an observation that can potentially be applied to the interactive presentation of graphs.

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Effects of spatial compatibility on integration processes in graph comprehension

Cited by 20 publications

References 53 publications

Designing Graphs that Promote Both Risk Understanding and Behavior Change

Designing Graphs that Promote Both Risk Understanding and Behavior Change

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

Eye Movements Indicate the Temporal Organisation of Information Processing in Graph Comprehension

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