August 2016
Volume 16, Issue 12
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2016
Perceptual averaging of scientific data: Implications of ensemble representations for the perception of patterns in graphs
Author Affiliations
  • Stefan Uddenberg
    Dept. of Psychology, Yale University
  • George Newman
    School of Management, Yale University
  • Brian Scholl
    Dept. of Psychology, Yale University
Journal of Vision September 2016, Vol.16, 1081. doi:10.1167/16.12.1081
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      Stefan Uddenberg, George Newman, Brian Scholl; Perceptual averaging of scientific data: Implications of ensemble representations for the perception of patterns in graphs. Journal of Vision 2016;16(12):1081. doi: 10.1167/16.12.1081.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

One of the most prominent trends in recent visual cognition research has been the study of ensemble representations, as in the phenomenon of perceptual averaging: people are impressively accurate and efficient at extracting average properties of visual stimuli, such as the average size of an array of objects, or the average emotion of a collection of faces. Here we explored the nature and implications of perceptual averaging in the context of a particular sort of ubiquitous visual stimulus: graphs of numerical data. The most common way to graph numerical data involves presenting average values explicitly, as the heights of bars in bar graphs. But the use of bar graphs also leads to biased perception and reasoning, as observers implicitly behave as if data are more likely to be contained within the bars themselves, even when they depict averages (as in the so-called 'within-the-bar bias', perhaps due to object-based attention). Here we tested observers' ability to perceive and remember average values via perceptual averaging when they viewed entire distributions of values. Observers had to extract and report (via mouse clicks) the average values of two distributions, depicted either as bar graphs or as 'beeswarm plots' (a kind of one-dimensional scatterplot, in which each datapoint is depicted by a non-overlapping dot — with no explicit representation of the average value). Observers were surprisingly accurate at extracting average values from beeswarm plots. Indeed, observers were just as accurate at reporting averages from visible beeswarm plots as they were when simply recalling the heights of bars from bar graphs. Even reports of average values from beeswarms made from memory were highly accurate (though not as accurate as when the beeswarms were visible). These results collectively demonstrate that perceptual averaging operates efficiently when viewing scientific data, and could be exploited for information visualization.

Meeting abstract presented at VSS 2016

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