News

11

Sep 2011

Mark Shovman, Measuring the Effectiveness of Abstract Data Visualisations


Abstract:
In natural and social sciences, novel insights are often derived from visual analysis of data. But what principles underpin the extraction of meaningful content from these visualisations? Abstract data visualisation can be traced at least as far back as 1801; but with the increase in the quantity and complexity of data that require analysis, standard tools and techniques are no longer adequate for the task. The ubiquity of computing power enables novel visualisations that are rich, multimodal and interactive; but what is the most effective way to exploit this power to support analysis of large, complex data sets? Often, the lack of fundamental theory is pointed out as a central ‘missing link’ in the development and assessment of efficient novel visualisation tools and techniques.

In this talk, I will present some first steps towards the theory of visualisation comprehension, drawing heavily on existing research in natural scene perception and reading comprehension. The central inspiration is the Reverse Hierarchy Theory of perceptual organisation, which is a recent (2002) development of the near-centennial Laws of Gestalt. The proposed theory comes complete with a testing methodology (the ‘pop-out’ effect testing) that is based on our understanding of the cognitive processes involved in visualisation comprehension.

About Mark:
Mark Shovman is a SICSA Lecturer in Information Visualisation in the Institute of Arts, Media and Computer Games Technology in the University of Abertay Dundee. He is an interdisciplinary researcher, studying the perception and cognition aspects of information visualisations, computer games, and immersive virtual reality. His recent research projects include the application of dynamic 3D link-charts in Systems Biology; alleviating cyber-sickness in VR helmets; and immersive VR as an art medium. Mark was born in Tbilisi, Georgia, and lived in Jerusalem, Israel since 1990. He can be found on LinkedIn