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