Judging proportion with graphs: the summation model

Hollands, Justin G.; Spence, Ian

doi:10.1002/(sici)1099-0720(199804)12:2<173::aid-acp499>3.0.co;2-k

Cited by 60 publications

(61 citation statements)

References 27 publications

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“…Specifically, both the treemaps and the bubble charts do not have axes, which Heer and Bostock discuss as a possible difficulty for users [24]. Also, the stacked bar charts used more graphical elements than the other charts, which Hollands and Spence found to increase cognitive effort [26].…”

Section: Discussionmentioning

confidence: 99%

Influencing visual judgment through affective priming

Harrison

Skau

Franconeri

et al. 2013

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Recent research suggests that individual personality differences can influence performance with visualizations. In addition to stable personality traits, research in psychology has found that temporary changes in affect (emotion) can also significantly impact performance during cognitive tasks. In this paper, we show that affective priming also influences user performance on visual judgment tasks through an experiment that combines affective priming with longstanding graphical perception experiments. Our results suggest that affective priming can influence accuracy in common graphical perception tasks. We discuss possible explanations for these findings, and describe how these findings can be applied to design visualizations that are less (or more) susceptible to error in common visualization contexts.

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Section: Discussionmentioning

confidence: 99%

Influencing visual judgment through affective priming

Harrison

Skau

Franconeri

et al. 2013

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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“…5B) or bar graphs as a means of displaying proportions. However, careful experiments indicated that for some tasks, pie charts are as effective as divided bar charts, and for other tasks they are actually more effective (Hollands & Spence, 1998;Simkin & Hastie, 1986;Spence & Lewandowsky, 1991). Simple judgments (e.g., comparing the population of Europe and Africa in Fig.…”

Section: The Importance Of Objective Measuresmentioning

confidence: 99%

“…Other task analyses provide models of the elementary perceptual and cognitive processes necessary to carry out various data interpretation tasks with different types of displays (e.g., Gillan & Callahan, 2000;Gillan & Lewis, 1994;Hollands & Spence, 1998;Lohse, 1993;Simkin & Hastie, 1986;Spence & Lewandowsky, 1991). Elementary processes might include visual search to find an element in a display, scanning to estimate the distance between two components, and mental superimposition to compare the size of two components.…”

Section: How Cognitive Models Inform Display Designmentioning

confidence: 99%

The Cognitive Science of Visual‐Spatial Displays: Implications for Design

Hegarty

2011

Topics in Cognitive Science

258

205

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This paper reviews cognitive science perspectives on the design of visual-spatial displays and introduces the other papers in this topic. It begins by classifying different types of visual-spatial displays, followed by a discussion of ways in which visual-spatial displays augment cognition and an overview of the perceptual and cognitive processes involved in using displays. The paper then argues for the importance of cognitive science methods to the design of visual displays and reviews some of the main principles of display design that have emerged from these approaches to date. Cognitive scientists have had good success in characterizing the performance of well-defined tasks with relatively simple visual displays, but many challenges remain in understanding the use of complex displays for ill-defined tasks. Current research exemplified by the papers in this topic extends empirical approaches to new displays and domains, informs the development of general principles of graphic design, and addresses current challenges in display design raised by the recent explosion in availability of complex data sets and new technologies for visualizing and interacting with these data.

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“…Assuming concentration addition, their cumulative risk to Cd, Ni, and Zn is shown in Fig. 7, through the coupling of a cartographic display with pie charts, effective for conveying proportion [19]. Color is an important visual attention guide and influences risk perception [59].…”

Section: ^^^^~^^^^^ Kmmentioning

confidence: 96%

Mapping Cumulative Environmental Risks: Examples from the EU NoMiracle Project

Pistocchi

Groenwold

Lahr

et al. 2010

Environ Model Assess

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We present examples of cumulative chemical risk mapping methods developed within the NoMiracle project. The different examples illustrate the application of the concentration addition (CA) approach to pesticides at different scale, the integration in space of cumulative risks to individual organisms under the CA assumption, and two techniques to (1) intégrate risks using data-driven, parametric statistical methods, and (2) cluster together áreas with similar occurrence of different risk factors, respectively The examples are used to discuss some general issues, particularly on the conventional nature of cumulative risk maps, and may provide some suggestions for the praetiee of cumulative risk mapping.

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Judging proportion with graphs: the summation model

Cited by 60 publications

References 27 publications

Influencing visual judgment through affective priming

Influencing visual judgment through affective priming

The Cognitive Science of Visual‐Spatial Displays: Implications for Design

Mapping Cumulative Environmental Risks: Examples from the EU NoMiracle Project

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