IAT 814 Introduction to Visual Analytics Symbols vs Perceptual Science Sep 11, 2013IAT 8141

Visualization based on science  Visualization based on science – –not recognition of arbitrary symbols  Semiotics of graphics: Bertin, Saussure –The craft of designing visual languages? –The perceptual system has built-in capabilities  Understanding of perceptual mechanisms is fundamental to a science of visualization  Experimental semiotics (Ware) Sep 11, 2013IAT 8142

Sensory vs arbitrary symbols  Sensory: –You can see and understand without training. –Match the way our brains are wired –Object shape, color, texture  Arbitrary: –Must be learned –Having no perceptual basis –The word “dog” Sep 11, 2013IAT 8143

Arbitrary representations  Strengths –Formally powerful –Capable of rapid change –May already be learned –Visually concise  Weaknesses –Can be hard to learn –Can be easy to forget –Same symbol, different meaning –Different symbol, same meaning Sep 11, 2013IAT 8144

Sensory representations  Strengths –Can be understood without training –Resistant to instructional bias –Processed very quickly, and in parallel –Valid across cultures  Weaknesses –Poor mappings can be misunderstood, quickly and without effort, even with instruction and training. –Can’t be unlearned Sep 11, 2013IAT 8145

Sensory symbols  “Symbols and aspects of visualizations that derive their expressive power from their ability to use the perceptual power of the brain without learning”.  Empirically testable (ha!) Sep 11, 2013IAT 8146

Building a Visualization: Steps  Collect the data (lab work, simulation, archives, ……)  Transform the data into –a format readable and manipulable by the visualization software –the form most likely to reveal information  Visualization algorithms and computational treatments run on graphics hardware or software renderers  Human views and interacts with the visualization –Changes parameters, techniques, view options  User studies to evaluate effectiveness –ideally! Sep 11, 2013IAT 8147

What’s a good visualization?  Make a model that captures the essence of a information system  Model = abstraction with –The important things in –The unimportant things out  Different visualizations provide different levels of detail, –Show and hide different things –Support different abstractions  Useful to aid understanding, not just realistic representations (what color is a carbon atom?)  Map the important part of the tasks onto techniques that show the relevant characteristics best Sep 11, 2013IAT 8148 Acts of rhetoric!