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Ronald A. Rensink; Information Visualization and the Study of Visual Perception. Journal of Vision 2018;18(10):1350. doi: 10.1167/18.10.1350.
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© ARVO (1962-2015); The Authors (2016-present)
Information visualization and vision science can interact in three different (but compatible) ways. The first uses knowledge of human vision to design more effective visualizations. The second adapts measurement techniques originally developed for experiments to assess performance on given visualizations. And a third way has also been recently proposed: the study of restricted versions of existing visualizations. These can be considered as "fruit flies", i.e., systems that exist in the real world, but are still simple enough to study. This approach can help us discover why a visualization works, and can give us new insights into visual perception as well. An example of this is the perception of Pearson correlation in scatterplots. Performance here can be described by two linked laws: a linear one for discrimination and a logarithmic one for perceived magnitude (Rensink & Baldridge, 2010). These laws hold under a variety of conditions, including when properties other than spatial position are used to convey information (Rensink, 2014). Such behavior suggests that observers can infer probability distributions in an abstract two-dimensional parameter space (likely via ensemble coding), and can use these to estimate entropy (Rensink, 2017). These results show that interesting aspects of visual perception can be discovered using restricted versions of real visualization systems. It is argued that the perception of correlation in scatterplots is far from unique in this regard; a considerable number of these "fruit flies" exist, many of which are likely to cast new light on the intelligence of visual perception.
Meeting abstract presented at VSS 2018
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