Colin Ware - Information Visualization: Perception for Design

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Cognitive neuroscience has made rapid progress in the last few years. When this book first appeared in the year 2000, there was no unified model of how we think visually. In the intervening years, the theory of predictive cognition has appeared and it provides a new and much more solid foundation for the later chapters of this edition. The brain is now regarded as first and foremost as a prediction engine. For example, theories of how we predict the future and remember the past are now unified. To recall the past we construct a likely scenario for what might have happened using a few recalled fragments together with relevant aspects of our cognitive model of the world, we do not simply pull it out of a memory like a video replay; predicting the future uses the same mechanisms except that we construct a likely future scenario based on a knowledge of the present and the same cognitive model of the world. We remember in order to predict the future and that the neural mechanisms are the same. In addition, the idea that cognition occurs in a distributed fashion, combining brain function with cognitive tools, such as visualizations, is now mainstream. We are all cognitive cyborgs. The later chapters of Information Visualization: Perception for Design have been rewritten, reorganized, and extended to reflect this new understanding. is about the design of efficient thinking tools.

In addition to these major changes, the book has been revised and updated throughout to take recent research into account. In many cases the changes are small, the replacement of a figure or a brief description of a recent study. In other places, whole sections, such as one on narrative, have undergone major rewrites to take incorporate current thinking on the subject.

Now let me tell you how this book came about. In 1973, after I had completed my masters degree in the psychology of vision, I was frustrated with the overly focused academic way of studying perception. Inspired by the legacy of freedom that seemed to be in the air in the late 1960s and early 1970s, I decided to become an artist and explore perception in a very different way. But after three years with only very small success, I returned, chastened, to the academic fold, though with a broader outlook, a great respect for artists, and a growing interest in the relationship between the way we present information and the way we see. After obtaining a doctorate in the psychology of perception at the University of Toronto, I still did not know what to do next. I moved into computer science, via the University of Waterloo and another degree, and have been working on data visualization, in one way or another, ever since. In a way, this book is a direct result of my ongoing attempt to reconcile the scientific study of perception with the need to convey meaningful information. It is about art in the sense that form should follow function, and it is about science because the science of perception can tell us what kinds of patterns are most readily perceived.

Why should we be interested in visualization? Because the human visual system is a pattern seeker of enormous power and subtlety. The eye and the visual cortex of the brain form a massively parallel processor that provides the highest bandwidth channel into human cognitive centers. At higher levels of processing, perception and cognition are closely interrelated, which is the reason why the words understanding and seeing are synonymous. However, the visual system has its own rules. We can easily see patterns presented in certain ways, but if they are presented in other ways they become invisible. Thus, for example, the word goggle , shown in the accompanying figure, is much more visible in the version shown at the bottom than in the one at the top. This is despite the fact that identical parts of the letters are visible in each case and in the lower figure there is more irrelevant noise than in the upper figure. The rule that applies here, apparently, is that when the missing pieces are interpreted as foreground objects then continuity between the background letter fragments is easier to infer. The more general point is that when data is presented in certain ways the patterns can be readily perceived. If we can understand how perception works, our knowledge can be translated into guidelines for displaying information. Following perception-based rules, we can present our data in such a way that the important and informative patterns stand out. If we disobey these rules, our data will be incomprehensible or misleading.