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CSc4730/6730 Scientific Visualization
Lecture 22 Cognitive Basis of Information Visualization Ying Zhu Georgia State University
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References Jeremy M. Wolfe, How Might the Rules that Govern Visual Search Constrain the Design of Visual Displays? Society for Information Display Symposium Digest of Technical Papers, 36(1), pp Colin Ware, “Information Visualization”, Morgan Kaufmann, 2nd edition, 2004. Chapter 5 “Visual Attention and Information that Pops Out”
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Outline Visual search Visual attention
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Overview One of the goals of data visualization is to effectively guide reader’s attention to important information In visual search attention can be guided toward a target item among distracting items by a limited set of basic features. This paper will summarize the rules of effective guidance
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Basic visual search If an observer is asked to find a target among a number of similar distracters that observer will behave as though she is processing those items at a rate of one every msec
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Basic Visual Search This assumes that the observer does not need to move her eyes to each item in turn because eye movements proceed at a much slower rate of 3-4 sec. It also assumes that each item can be recognized rapidly once attention is directed to it.
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Basic visual search This is "face-in-the-crowd" search where one face is much like all others. Why don't we have to search in this laborious fashion most of the time? The core of the answer is that we can use simple information to guide our attention to more complex targets
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Guided search If basic feature information differentiates the target from, at least, some of the distracters, search can be speeded.
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Guided search
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Basic features Only a limited number of stimulus properties can guide attention. The exact list is a matter of debate but there are probably not fewer than a dozen guiding features and not more than two dozen
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Basic features The most efficient searches are those with a target item defined by a single basic feature The target is different from distracters by a single feature
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Basic features Some features are fairly obvious candidates like color and orientation. Others, like line termination are less obvious and many, seemingly reasonable, candidates like intersection type do not work
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Basic features
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Top-down vs. Bottom-up guidance
Basic features in a display can guide attention in either of two modes. Top-down guidance Bottom-up guidance
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Top-down vs. Bottom-up guidance
Top-down guidance is based on the observer's attentional set (e.g. "look for red"). The reader knows what feature(s) to look for
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Top-down vs. Bottom-up guidance
An item might attract attention without the observer needing to know anything about that item in advance. This is bottom-up guidance and is based on the local differences between an item and its neighborhood
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Top-down vs. Bottom-up guidance
Strong bottom-up guidance is sometimes referred to as "attentional capture". However, you cannot assume that your flashing ad will grab eyeballs on a webpage. Observers can usually learn to avoid capture by new stimuli if they form a top-down attentional set to do so.
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Similarity If you are trying to attract attention, it is important to keep in mind that bottom-up guidance is based on differences. The larger the target-distracter differences, the more salient the target.
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Similarity
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Similarity The larger the differences among distractors, the less salient the target will appear.
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Similarity This is why you want to limit the number of colors you use in data visualization Rainbow color coding may look pretty but make visual search slow
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Categorical processing
Top-down guidance has a very limited “vocabulary”. Consider orientation. While it is easy to tell if a line is vertical, it is not easy to find a vertical line among lines tilted 20 deg to the left and right
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Categorical processing
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Categorical processing
It is easier to ask that attention be guided to items that are defined categorically. In data visualization design, limit the number of variations in visual feature (color, size, orientation, etc.) Use a small visual “vocabulary” In orientation, the categories are steep and shallow, left and right.
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Categorical processing
Search for the 10 deg tilted item is fairly easy since it is the only “steep” item
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Categorical processing
Search for the 10 deg tilted item is harder because it is neither uniquely “steep” nor uniquely “left-tilted”.
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Categorical processing
In size, the guiding vocabulary seems to be just “big” and “small”. It is hard to find the medium-sized target in the following picture.
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Categorical processing
This does not mean that multiple orientations, sizes, colors, etc. cannot be used in a display. However, the choice of features is constrained when it is important that users be guided to one item without being distracted by others.
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Crowding Just as the deployment of attention is coarsely coded to a feature attribute like orientation, it is also coarsely coded in space,
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Crowding Look at the center of Figure Five where it says "Look Here".
If you now attend to the pattern at the top of the figure, you will probably find yourself unable to count the lines.
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Crowding If you keep your eyes in place and shift attention to the letters at the bottom of the figure, you will find yourself unable to read the letters in the middle of the array. As with the lines, you are unable to restrict your attention to a single letter when it is crowded by others.
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Crowding In data visualization, labels should be placed as close to the visualization items as possible
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Combining features We can guide attention based on the combination of multiple sources of information. Conjunction search Under most real world situations, knowing that a target is a moving, big, red, shiny object is likely to guide attention to only a few items even if the specifications of "moving", "red" etc. are quite crude.
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Combining features Under most circumstances, the crude descriptors will limit the set of possible targets to a small subset of all items. The exceptions are the "needle in a haystack" searches where all items have much the same set of features.
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Combining features Guidance to conjunctions is also subject to some fairly severe limitations. Most notably, we do not appear to be able to guide attention to the conjunction of two values drawn from the same feature space Color-color conjunction Shape-shape conjunction Size-size conjunction
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Combining features On the left of figure, it is hard to find the target defined by a conjunction of two colors, red & blue
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Combining features Interestingly, it is easier if one color is the color of the whole item and the other color is the color of a part (The red item with the blue center in Figure)
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Combining features Be careful when you code multiple colors in one visual object Colored stacked bar chart Colored pie chart Colored line chart, etc.
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Objects All else being equal, attention is guided to objects and that attention spreads throughout an object (though the precise definition of objects is not clear) However, it proves to be very difficult to guide attention to a specified part of an object.
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Objects It is hard to find green horizontal when it is embedded in objects that all contain green and vertical (left). It is easier if there is only one green horizontal object (right).
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Objects If you encode information in a part of an object, it may be too subtle to be noticed
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Visual attention Attention is both a low-level and a high-level property of vision Here we focus on low-level mechanisms that help us understand what is more readily available to attention
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Visual attention Experiments have shown that humans do not perceive much unless we have at least some expectation and need to see it In most systems, brief, unexpected events will be missed
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Useful field of view Useful field of view (UFOV) is the size of the region from which we can rapidly take in information UFOV varies greatly With greater target density, the UFOV becomes smaller and the attention is more focused With a low target density, a larger area can be attended
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Tunnel vision and stress
In tunnel vision, the UFOV is narrowed so that only the most important information, normally the center of the field of view, is processed When cognitive load goes up, the UFOV shrinks
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Motion in attracting attention
Studies have shown that UFOV function can be far larger for detection of moving targets than for detection of static targets Subjects can respond in less than 1 sec to moving targets 20 degrees from the line of sight If static targets are used, performance falls off rapidly beyond about 4 degrees from fixation
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Pre-attentive Processing
We can do certain things to symbols that make it much more likely to “pop out” Pre-attentive processing determines what visual objects are offered up to our attention An understanding of what is processed preattentively is probably the most important contribution that vision science can make to data visualization
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Example
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Example The time taken to find the target is independent of the number of distracters
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Why is this important? In displaying information, it is often useful to be able to show things “at a glance”. If you want people to be able to identify instantaneously some mark on a map as being of type A, it should be differentiated from all other marks in a pre-attentive way
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Example
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Pre-attentively processed features
Form Line orientation Line length Line width Line collinearity Size Curvature Spatial grouping
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Preattentively processed features
Blur Added marks Simple numerosity (e.g. less than 4) Color Hue Intensity
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Preattentively processed features
Motion Flicker Direction of motion Spatial position 2D position Stereoscopic depth Convex/concave shape from shading
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Preattentive symbols Preattentive symbols become less distinct as the variety of distractors increases Two factors are important in determing whether something stands out preattentively: The degree of difference of the target from the nontargets The degree of difference of the nontargets from each other
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Which visual dimensions are preattentively stronger?
It’s hard to answer this question because it always depends on the strength of particular feature and the context
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Coding with combination of features
What happens if we wish to search for a gray square, not just something that gray or something that is square? This kind of search (called conjunction search) is slow if the surrounding objects are squares and other gray shapes Conjunction searches are generally not preattentive
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Coding with combination of features
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Highlighting The purpose of highlighting is to make something stand out from other information As a rule of thumb, use whatever graphical dimension is least used otherwise in the design If texture is not used elsewhere, use texture Use motion (but may be too strong a cue)
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Semantic depth of field
Use depth of field for highlighting
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References Jeremy M. Wolfe, How Might the Rules that Govern Visual Search Constrain the Design of Visual Displays? Society for Information Display Symposium Digest of Technical Papers, 36(1), pp Colin Ware, “Information Visualization”, Morgan Kaufmann, 2nd edition, 2004. Chapter 5 “Visual Attention and Information that Pops Out”
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