How I Enjoy Trading Stocks Online : The Principles of Cognitive Perception and Intuition

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Our visual perception of the world seems to be based on a just-in-time architecture in which attention is directed to the right object at the right time. If the co-ordination mechanisms involved can be handled correctly, it would open up the prospect of "seeing" abstract datasets in a way that is as natural and effortless as seeing the physical world.

A brief overview of these developments and their implications can be found in Rensink, A related opportunity is the greater use of visual analogy or metaphor. Here, the emphasis is no longer on bypassing conscious thought, but on using modes of thought best suited for reasoning about visuospatial objects and processes. For example, when reasoning about physical force, a highly useful metaphor is the directed line, or arrow. A more modern example is the desktop, which allows a user to reason about possible actions on their computer.

As in the case of visual perception, many - if not most - developments to date have been based on a relatively shallow understanding of the mechanisms involved. But given that cognitive scientists have learned much more about metaphor, it may be time to consider its use in a more sophisticated fashion. Ultimately, visualizations might be able to create mental images that correspond in a natural way to the structure of any process or task. For an interesting discussion of this, see Paley, A third direction of potential importance is the creation of more powerful evaluation methods based on the methodologies developed in experimental psychology.

Psychologists have spent centuries learning what to do and not to do to obtain precise measurements of various aspects of human behaviour. It would be good to learn from this. Of course, some of these techniques have already been adapted to evaluation. But as in the case of cognitive and perceptual mechanisms, the transfer of knowledge here is far from complete, and there is much that could still be done. For example, consider evaluating how well a given scatterplot design conveys the correlation in a dataset. In the past, this was done by presenting the viewer with the scatterplot and asking for a numerical estimate of the perceived correlation.

But a more powerful approach is to borrow the experimental methodology of measuring just noticeable differences jnds: Results based on this approach show both precision and accuracy to be specified over all correlations by two functions governed by only two parameters. As a consequence, a given scatterplot design can be completely evaluated based on just two simple measurements. For details, see Rensink and Baldridge, A final direction to consider - perhaps the most challenging of all - is to develop a systematic way of ensuring that visualization designs make optimal or at least, good use of human perception and cognition.

In theory, this could result in a "science of design". In practice, this might not be possible, if only because the number of possible designs is so immense and our understanding of human cognition so incomplete. But it may be possible to follow the example of several other areas of design, and aim for a set of principles that would at least constrain the space of possibilities to consider. For example, constraints based on physical forces or material properties can be applied to any architectural design, determining whether or not it is viable.

There is no a priori reason why a similar approach would not also work for visualization. The efforts of Bertin are perhaps a start in this direction, providing suggestions about the kinds of graphic representation that might be applied to various kinds of problems. Work by Tufte, Mackinlay, Ware, and others have extended this further. But however useful these suggestions are, we are still a long way from a solid foundation for thinking about effective visualizations. Many foundational issues are still poorly understood.

What is really going on in a visualization? Is there a way to describe this process precisely and objectively? Is it even possible in principle to determine if a given visualization draws upon the perceptual and cognitive resources of the viewer in an optimal way? The answers to these questions and others like them will be difficult to find.

But they will determine the extent to which we can enable humans and machines to best combine their respective strengths. Stephen Few wrote an excellent description of data visualization and the necessity for designing graphics to take advantage of our knowledge of human perception and cognition.

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In this commentary I question who is responsible for the myriad of visualizations that ignore this knowledge: In addition, I point out important work that deserves greater exposure on the integration of geo-spatial and other forms of data display, a topic on Few's most-needed list. I end with additional sources for learning more.

Certainly, software vendors are responsible for offering many graph forms that hinder rather than help the reader to understand the data. The vendors offer graphs to wow the audience rather than to communicate clearly and they create demand for ineffective graphs. But they are not solely responsible for the myriads of graphs with perceptual problems. People learn from what they see and they see many ineffective graphs. The software users then demand software that allows them to imitate these ineffective designs.

This gets us in a chicken and egg situation: Do vendors produce these awful visualizations because their customers demand them, or do the customers become attracted to them when they see what vendors market? An example of the ineffective ways includes pseudo-third dimensions in bar charts. Figure 1 shows a pseudo-three-dimensional bar chart in Excel. Almost no one reads it correctly. I describe other problems with this graph in Creating More Effective Graphs [1].

A number of graphic artists have made major contributions to the field of data visualization. However, there are some graphic artists who have no appreciation of numbers and don't realize that the representation of numbers in graphs should be proportional to the numbers they represent. As a result, it is common to see graphs that are not drawn to scale. Some graph designers want to give the impression of better performance than is actually the case and intentionally design graphs that mislead to achieve this impression.

Other graph designers may be more concerned with demonstrating their technological abilities or artistic abilities than in communicating clearly and accurately. Until recently, our educational system did not provide training in communicating numbers. Today, there are some excellent courses at the college level but the majority of people receive little, if any, training in presenting numerical information.

Therefore, many graph designers are unaware of the principles of effective graphs. Some of the problems occur from a lack of proofreading and careless errors. As an analogy, a current style in fashion is high-heeled shoes. A quick search on "dangers of high heels" revealed that there has been an increase in the number of bunion operations on wearers of high heels as well as foot pain, back pain and neck pain.

In some cases the Achilles tendon grows shorter. Balance is affected so that the risk of falls is greater. The list of problems goes on and on. Is the shoe designer, the shoe manufacturer, the retail outlet that sells the shoes or the customer who buys them responsible for this increase in medical problems? Is this situation analogous to the data visualization one? Both cause serious problems: I hope that these questions stimulate interesting discussion.

In his section on future directions, Few mentions areas that offer the potential for enrichment including the integration of geo-spatial displays with other forms of display for seamless interaction and simultaneous use. Several researchers have made advances in this area.

For example, the micromap designs of Dan Carr [1] and [2] add a geographic context to statistical information, allowing for the joint exploration of statistical and geographic patterns in data. As illustrated in Figure 2, statistical graphics, here dots, are linked to small maps by color. In the first row, we can see that Maryland is represented by red dots and so Maryland is shaded red on the right-hand map. Sorting by poverty level, we see that not only are poverty and education inversely related, but that there is a geographic clustering of southern U.

Data visualization does not belong to a single academic discipline. Statisticians, computer scientists, psychologists, graphic designers and others practice and contribute to data visualization. The university programs and resources that Few mentions lean heavily towards computer science. A few excellent programs joining statistical graphics with computer science are available at George Mason University, Iowa State, and the University of Augsburg. There are many others. I will leave it to other commentators to add excellent programs in cognitive psychology and graphic design.

Although the Joint Statistical Meetings are not exclusively devoted to statistical graphics and data visualization, there are as many sessions sponsored by the Statistical Graphics Section as many a smaller conference contains. One addition I would make to the "what's needed" list is better communication between the computer scientists, graphic designers, psychologists and statisticians. More joint conferences and attending each other's conferences would help each discipline benefit from the research of the others.

One important topic Stephen Few only mentions briefly in his very well-written and comprehensive piece is interaction. While static charts and visualizations are undoubtedly useful, they make little use of the immense computing power that is readily available to us today. Interaction in visualization enables the fast exploration and discovery of data patterns that the user may not even have expected. It is also possible to reduce the amount of data shown at the same time, providing clearer visualizations, while still giving the user the option to get that information on demand at any time.

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Ben Shneiderman captured the role of interaction in his famous visual information seeking mantra Shneiderman, Abstract information spaces require an overview so the user has an idea where to even find data, but then it is necessary to zoom in to see details. Filtering data is important when dealing with larger datasets. Finally, details on what is shown and also what is not shown can be retrieved by the user as needed. All of these steps require interaction, where the user tells the visualization what he or she wants to see.

Among the simplest interactions are tooltips or other data displays that appear when the user points at a part of a visualization. Also, a vertical line could be drawn from the end of the active bar to the scale at the top, to make it easier to see the bars in context.

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This type of interaction is effortless and easy to discover: Displaying numbers in charts is also rather common. But the real power comes from the more advanced interactions. Brushing lets the user selects data points that get highlighted in one or more views of the same data. When several views are involved, the fact that all of them highlight the same data points is commonly referred to as linking and the views are called coordinated multiple views. Consider this example of linked bar charts of data about passengers on the Titanic.

Each bar chart represents one data dimension class, gender, age, and survived , and shows a histogram of how many people were in each of the categories. To find out how many people survived in each category, we will select the relevant bar, which will brush those data points in all the views.

We can now compare survival rates for different sexes, classes, etc. The mechanism is very similar for individual data points rather than summary data like in this example. Brushing and linking make it possible to find out high-dimensional relationships in the data by trying out different possibilities. Metaphors have a somewhat complicated history in visualization. There is not even a clear understanding what a metaphor even is: What I want to add here is a combination of both, perhaps best summarized as structure: Caroline Ziemkiewicz and I have done work on this topic, and have found that the big-picture structure plays a bigger role than most people would assume.

When comparing different types of tree visualizations, we found that different studies had come to different conclusions as to which method works better based on which metaphor was used in the question: A being contained in B, or A being below B in the hierarchy. We did a study and found that there was, indeed, a compatibility effect between the linguistic metaphor used in the question and the visual metaphor of the visualization Ziemkiewicz and Kosara, We recently showed that there is an apparent effect of gravity between objects in a visualization that can distort the perception of distance Ziemkiewicz and Kosara, While we know a lot about how to create reasonable visualizations, there is still a lot we do not know or are not yet aware of.

Even seemingly basic knowledge like how the layout of a visualization influences our reading of the data still needs more work to be understood and turned into useful recommendations and best practices. Interaction is not exactly a new topic in visualization research, but is still rather rudimentary in many visualization and charting programs. To really unlock the power of visualization, these programs will need more advanced capabilities as well as ways to educate their users about their interactive features.

Visualization has a lot more to offer than what most people are aware of today. In 9 chapters, we'll cover: Your browser is outdated. Please switch to a modern web browser to improve performance and avoid security risks. For companies Frequently asked questions Contact us. Log in Join our community Join us.

The Psychology of Graphic Images: The perception of correlation in scatterplots. Computer Graphics Forum , Robbins Stephen Few wrote an excellent description of data visualization and the necessity for designing graphics to take advantage of our knowledge of human perception and cognition. Almost no one reads this simple chart correctly. The numbers plotted are 1, 2, and 3. Plot it yourself in Excel if you don't believe me. An example of a micromap design from Carr and Pickle [1].

Courtesy of Robert Kosara. Topics in this book chapter:. Share with your friends:. New to UX Design? We're giving you a free ebook! Objects that are close together are perceived as a group. Objects that share similar attributes e. Objects that are connected e. Data visualization practices focused on the needs of statisticians.

The Encyclopedia of Human-Computer Interaction, 2nd Ed.

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