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Steven Franconeri; Segmentation, structure, and shape perception in data visualizations. Journal of Vision 2018;18(10):1352. doi: https://doi.org/10.1167/18.10.1352.
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The human visual system evolved and develops to perceive scenes, faces, and objects in the natural world, and this is where vision scientists justly focus their research. But humans have adapted that system to process artificial worlds on paper and screens, including data visualizations. I'll demonstrate two examples of how studying the visual system within such worlds can provide vital cross-pollination for our basic research. First a complex line or bar graph can be alternatively powerful, or vexing, for students and scientists. What is the suite of our available tools for extracting the patterns within it? Our existing research is a great start: I'll show how the commonly encountered 'magical number 4' (Choo & Franconeri, 2013) limits processing capacity, and how the literature on shape silhouette perception could predict how we segment them. But even more questions are raised: what is our internal representation of the 'shape' of data – what types of changes to the data can we notice, and what changes would leave us blind? Second, artificial displays require that we recognize relationships among objects (Lovett & Franconeri, 2017), as when you quickly extract two main effects and an interaction from a 2x2 bar graph. We can begin to explain these feats through multifocal attention or ensemble processing, but soon fall short. I will show how these real-world tasks inspire new research on relational perception, highlighting eyetracking work that reveals multiple visual tools for extracting relations based on global shape vs. contrasts between separate objects.
Meeting abstract presented at VSS 2018
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