정보시각화: 디자인을 위한 지각

Evolutionary perspective:

The theory of sensory languages is based on the idea that the human visual system has evolved as an instrument to perceive the physical world. It rejects the idea that the visual system is a truly universal machine. It was once widely held that the brain at birth was an undifferentiated neural net, capable of configuring itself to perceive in any world, no matter how strange.

…

This Tabula rasa view has been overthrown as neurologists have come to understand that the brain has a great many specialized regions. —p10-11

Perception is designed for action:

J. J. Gibson assumed that we perceive in order to operate on the environment. Perception is designed for action. Gibson called the perceivable possibilities for action Affordances; he claimed that we perceive these properties of the environment in a direct and immediate way. This theory is clearly attractive from the perspective of visualization, because the goal of most visualization is decision making. …

He claimed that we do not perceive points of light; rather, we perceive possibilities for action. We perceive surfaces for walking, handles for pulling, space for navigating, tools for manipulating, and so on. In general, our whole evolution has been geared toward perceiving useful possibilities for action. —p18

The concept of Affordances, loosely construed, can be extremely useful from a design perspective. The idea suggests that we build interfaces that beg to be operated in appropriate and useful ways. … This is the kind of design approach advocated by Donald Norman in his famous book, The design of everyday things. Nevertheless, on-screen widgets present affordances only in an indirect sense. They borrow their power from our ability to represent pictorially, or otherwise, the affordances of the everyday world. Therefore, we can be inspired by affordance theory to produce good designs, but we cannot expect much help from that theory in building a science of visualization. —p20

World as an information display:

We can think of the world itself as an information display. Objects may be used as tools or as construction materials, or they may be obstacles to be avoided. Every intricate surface reveals the properties of the material from which it is made. Creatures signal their intentions inadvertently or deliberately through movement. There are almost infinite levels of detail in nature, and we must be responsive to both small and large things, but in different ways: large things, such as boulders, are obstacles; smaller things, such as rocks, can be used as tools; still smaller things, such as grains of sand, are useful by the handful. If our extraordinary skill in perceiving the information inherent in the environment can be applied to data visualization, we will have gained a truly powerful tool. —p29

A strategy for designing a visualization is to transform the data so that it appears like a common environment a kind of data landscape. We should then be able to transfer skills obtained in interpreting the real environment to understanding our data. —p30

What perception is for:

Understanding the general properties of the environment is important for a more basic reason. When trying to understand perception, it is always useful to think about what perception is for. The Theory of evolution tells us that the visual system must have survival value, and adopting this perspective allows us to understand visual mechanisms in the broader context of useful skills, such as navigation, food seeking (which is like information seeking), and tool use (which depends on object-shape perception). —p30

Texture is critical to perception:

J. J. Gibson pointed out that surface texture is one of the fundamental visual properties of an object. In visual terms, a surface is merely an unformed patch of light unless it is textured. Texture is critical to perception in a number of ways. The texture of an object helps us see where an object is and what shape it has. On a larger scale, the texture of the ground plane on which we walk, run, and crawl is important in judging distances and other aspects of space. …

Generally speaking, most surfaces have clearly defined boundaries; diffuse, cloudlike objects are exceptional. Perhaps because of this, we have great difficulty in visualizing uncertain data as fuzzy clouds of points. At present, most computerized visualizations present objects as smooth and untextured. …

Perhaps visualization designers have avoided texturing surfaces by applying the general esthetic principle that we should avoid irrelevant decoration in displays “Chart junk,” to use Edward Tufte’s memorable phrase. But texturing surfaces is not chart junk, especially in 3D visualizations. Even if we texture all objects in exactly the same way, this can help us perceive the orientation, shape, and spatial layout of a surface. Textures need not be garish or obtrusive, but when we want something to appear to be a 3D surface, it should have at least a subtle texture. —p34

Evolutionary role and importance of color perception:

Color also tells us much that is useful about the material properties of objects. This is crucial in judging the condition of our food. Is this fruit ripe or not? It this meat fresh or putrid? What kind of mushroom is this? It is also useful if we are making tools. What kind of stone is this? Clearly, these can be life-or-death decisions. …

The role that color plays ecologically suggests ways that it can be used in information display. It is useful to think of color as an attribute of an object rather than as its primary characteristic. It is excellent for labeling and categorization, but poor for displaying shape, detail, or space. —p97-98

Color vision deficiency is very common:

About 10% of the male population and about 1% of the female population have some form of color vision deficiency. The most common deficiencies are explained by lack of either the long-wavelength-sensitive cones (Protanopia) or the medium-wavelength-sensitive cones (Deuteranopia). Both protanopia and deuteranopia result in an inability to distinguish red and green, meaning that the cherries in Figure 4.1 are difficult for people with these deficiencies tosee. One way to describe color vision deficiency is by pointing out that the three-dimensional color space of normal color vision collapses to a two-dimensional space, as shown in Figure 4.3(b). An unfortunate result of using color for information coding, is the creation of a new class of people with a disability. Color blindness already disqualifies applicants for some jobs such as those of telephone linespeople, because of the myriad colored wires, and pilots, because of the need to distinguish color-coded lights. —p99-100

Common color words across cultures:

In a remarkable study of more than 100 languages from many diverse cultures, anthropologists Brent Berlin and Paul Kay showed that primary color terms are remarkably consistent across cultures (Figure 4.11). In languages with only two basic color words, these are always black and white; if a third color is present, it is always red; the fourth and fifth are either yellow and then green, or green and then yellow; the sixth is always blue; the seventh is brown, followed by pink, purple, orange, and gray in no particular order. The key point here is that the first six terms define the primary axes of an opponent color model. This provides strong evidence that the neural basis for these names is innate. —p112

Color mapping extends data dimensions:

Color mapping can be used to extend the number of displayable data dimensions to five or six in a single scatter plot. …

The technique is to create a scatter plot in which each point is a colored patch rather than a black point on a white background. Up to five data variables can be mapped and displayed as follows:

Variable 1 - x-axis position

Variable 2 - y-axis position

Variable 3 - amount of red

Variable 4 - amount of green

Variable 5 - amount of blue

In a careful evaluation of cluster perception in this kind of display, we concluded that color display dimensions could be as effective as spatial dimensions in allowing the visual system to perceive clusters. For this task, at least, the technique produced an effective five-dimensional window into the data space.

There is a negative aspect of the color-mapped scatter plot. Although identifying clusters and other patterns can be easy using this technique, interpreting them can be difficult. A cluster may appear greenish because it is low on the red variable rather than high on the green variable. The use of color can help us to identify the presence of multidimensional clusters and trends, but once the presence of these trends has been ascertained, other methods are needed to analyze them. An obvious solution is to map data variables to the color opponent axes described earlier. However, our experiments with this practice showed that the results were still not easy to interpret and that it was difficult to make efficient use of the color space.

Adding color is by no means the only way to extend a scatter plot to multiple dimensions, although it is one of the best techniques. In Chapter 5, we will consider other methods, which use shape and motion. —p142-143

Summarized lessons:

The important lessons are relatively few, and we summarize them here:

To show detail in a visualization, always have considerable luminance contrast between foreground and background information. Never make the difference only through chromatic variation. This should be obvious in the case of text, although many PowerPoint presentations still violate this rule. It also applies to such problems as the visual display of flow fields, where small color-coded arrows or particle traces are used.

Use only a few colors if they are distinct codes. It is easy to select six distinct colors, but if 10 are needed they must be chosen with care. If the background is varied, then attempting to use more than 12 colors as codes is likely to result in failure.

Black or white borders around colored symbols can help make them distinct by ensuring a luminance contrast break with surrounding colors.

Red, green, yellow, and blue are hard-wired into the brain as primaries. If it is necessary to remember a color coding, these colors are the first that should be considered.

When color-coding large areas, use muted colors, especially if colored symbols are to be superimposed.

Small color-coded objects should be given high-saturation colors.

When a perceptually meaningful ordering is needed, use a sequence that varies monotonically on at least one of the opponent color channels. Examples are red to green, yellow to blue, low saturation to high saturation, and dark to light. Variation on more than one channel is often better, such as pale yellow to dark blue.

If it is important to show variations above and below zero, use a neutral value to represent zero and use increases in saturation toward opposite colors to show positive and negative values.

Color contrast can cause large errors in the representation of quantity. Contrast errors can be reduced with borders around selected areas, or by using muted, relatively uniform backgrounds.

For the reproduction of smooth color sequences, several million colors are needed under optimal viewing conditions. In this case, care must be taken to calibrate the monitor and to take into account monitor gamma values.

When reproducing complex, continuously shaded images, it is critical to preserve the color relationships and to make sure that, under the particular lighting conditions, neutral values are perceived as neutral.

Beware of oversaturating colors, especially when a printed image is to be the end product.

—p143

Perception requires attention:

To understand better how we see when we are not primed for an experiment, Mack and Rock conducted a laborious set of experiments that only required one observation from each experiment. They asked subjects to look at a cross for a fraction of a second and report when one of the arms changed length. So far, this is like most other perception studies. But the real test came when they flashed up something near the cross that the subjects had not been told to expect. Subjects rarely saw this unexpected pattern, even though it was very close to the cross and they were attending to the display. Mack and Rock could only do this experiment once per subject, because as soon as subjects were asked if they had seen the new pattern they would have started looking for “unexpected” patterns, so hundreds of subjects were used. The fact that most subjects did not see a wide range of unexpected targets tells us 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. Mack and Rock initially claimed from their results that there is no perception without attention. However, because they found that subjects generally noticed larger objects, they were forced to abandon this extreme position. —p146

Useful field of view aka UFOV:

A concept called the Useful field of view has been developed to define the size of the region from which we can rapidly take in information. The UFOV varies greatly, depending on the task and the information being displayed. Experiments using displays densely populated with targets reveal small UFOVs, from 1 to 4 degrees of visual angle (Wickens, 1992). But Drury and Clement (1978) have shown that for low character densities (less than one per degree of visual angle), the useful visual field can be as large as 15 degrees. Roughly, the UFOV varies with target density to maintain a constant number of targets in the attended region. With greater target density, the UFOV becomes smaller and attention is more narrowly focused; with a low target density, a larger area can be attended. —p147

Tunnel vision and Stress (and Cognitive load):

A phenomenon known as Tunnel vision has been associated with operators working under extreme stress. In tunnel vision, the UFOV is narrowed so that only the most important information, normally at the center of the field of view, is processed. This phenomenon has been specifically associated with various kinds of nonfunctional behaviors that occur during problem handling in disaster situations. The effect can be demonstrated quite simply. Williams (1985) compared performance on a task that required intense concentration (high foveal load) to one that was simpler. The high-load task involved naming a letter drawn from six alternatives; the low-load task involved naming a letter drawn from two alternatives. They found a dramatic drop in detection rate for objects in the periphery of the visual field (down from 75% correct to 36% correct) as the task load increased. The Williams data shows that we should not think of tunnel vision strictly as a response to disaster. It may generally be the case that as Cognitive load goes up, the UFOV shrinks. —p147

The role of motion in attracting attention:

A study by Peterson and Dugas (1972) suggests that the UFOV function can be far larger for detection of moving targets than for detection of static targets. They showed that subjects can respond in less than 1 second to targets 20 degrees from the line of sight, if the targets are moving.

If static targets are used, performance falls off rapidly beyond about 4 degrees from fixation. This implies a useful field of at least 40 degrees across for the moving-targets task. However, this was merely the largest field that was measured. There is every reason to suppose that the useful visual field for moving targets is even larger; it may well encompass the entire visual field. Thus, motion of icons in user interfaces can be useful for attracting attention to the periphery of the screen (Bartram et al., 2003).

—p147

Iconic memory holds images for a while:

Figure 5.2 shows a collection of miscellaneous symbols. If we briefly flash such a collection of symbols on a screen say, for one-tenth of a second and then ask people to name as many of the symbols as they can, they typically produce a list of three to seven items. Several factors limit the number of items listed. The first is the short-lived visual buffer that allows us to hold the image for about one to two tenths of a second while we read the symbols into our Short-term memory. This visual buffer is called Iconic memory.

Any information that is retained longer than three-tenths of a second has been read into Visual working memory or Verbal working memory (discussed in Chapter 11). This is an artificial example, but it has to do with a process that is very general. In each fixation between Saccadic eye movements, an image of the world is captured in iconic memory; from this transient store higher-level processes must identify objects, match them with objects previously perceived, and take information into Working memory for symbolic analysis. —p147-148

Importance of Preattentive processing in Data visualization.

Perceptual independence problem and Visual processing channel:

Sometimes we may wish to display many different kinds of information in a single map. For example, we might wish to show sea-surface temperature and sea-surface salinity at the same time. Naturally, we would prefer that the different sources of information do not interfere with one another. It would be unfortunate if regions of high salinity appeared to have a greater apparent temperature than they really have, due to visual crosstalk between the way we display temperature and the way we display salinity. Thus, our goal is to create display methods that are perceptually independent.

The concept of the Visual processing channel can be taken directly from vision research and applied to the independence problem. … The idea is that information carried on one channel should not interfere with information displayed on another. It is probably not the case that any of the perceptual channels we shall discuss are fully independent; nevertheless, it is certainly the case that some kinds of information are processed in ways that are more independent than others. A channel that is independent from another is said to be orthogonal to it. —p167

Gestalt laws:

The first serious attempt to understand pattern perception was undertaken by a group of German psychologists who, in 1912, founded what is known as the Gestalt school of psychology. The group consisted principally of Max Westheimer, Kurt Koffka, and Wolfgang Kohler (see Koffka, 1935, for an original text). The word gestalt simply means pattern in German. The work of the Gestalt psychologists is still valued today because they provided a clear description of many basic perceptual phenomena. They produced a set of GestaltLaws of pattern perception. These are robust rules that describe the way we see patterns in visual displays, and although the neural mechanisms proposed by these researchers to explain the laws have not withstood the test of time, the laws themselves have proved to be of enduring value. The Gestalt laws easily translate into a set of design principles for information displays. Eight Gestalt laws are discussed here:

Law of proximity

Law of similarity

Law of connectedness

Law of continuity

Law of symmetry

Law of closure

Law of relative size, and

Law of common fate

(the last concerns motion perception and appears later in the chapter). —p189

Proximity:

Spatial proximity is a powerful perceptual organizing principle and one of the most useful in design. Things that are close together are perceptually grouped together. … The application of the proximity law in display design is straightforward: the simplest and most powerful way to emphasize the relationships between different data entities is to place them in proximity in a display. —p189-190

Similarity:

The shapes of individual pattern elements can also determine how they are grouped. Similar elements tend to be grouped together. In both Figure 6.4(a) and (b), the similarity of the elements causes us to see the rows most clearly.

Color and texture are separable dimensions, and the result is a pattern that can be visually segmented either by rows or by columns. This technique can be useful if we are designing so that users can easily attend to either one pattern or the other. —p190-191

Connectedness:

Palmer and Rock (1994) argue that connectedness is a fundamental Gestalt organizing principle that the Gestalt psychologists overlooked. The demonstrations in Figure 6.6 show that connectedness can be a more powerful grouping principle than proximity, color, size, or shape. Connecting different graphical objects by lines is a very powerful way of expressing that there is some relationship between them. Indeed, this is fundamental to the node?link diagram, one of the most common methods of representing relationships between concepts. —p191

Continuity:

The Gestalt principle of continuity states that we are more likely to construct visual entities out of visual elements that are smooth and continuous, rather than ones that contain abrupt changes in direction. (See Figure 6.7.) The principle of good continuity can be applied to the problem of drawing diagrams consisting of networks of nodes and the links between them. It should be easier to identify the sources and destinations of connecting lines if they are smooth and continuous. This point is illustrated in Figure 6.8. —p191

Symmetry:

Symmetry can provide a powerful organizing principle. Figures 6.9 and 6.10 provide two examples. The symmetrically arranged pairs of lines in Figure 6.9 are perceived much more strongly as forming a visual whole than the pair of parallel lines. In Figure 6.10(a), symmetry may be the reason why the cross shape is perceived, as opposed to shapes in 6.10(b), even though the second option is not more complicated. A possible application of symmetry is in tasks in which data analysts are looking for similarities between two different sets of time-series data. It may be easier to perceive similarities if these time series are arranged using vertical symmetry, as shown in Figure 6.11, rather than using the more conventional parallel plots. —p192-194

Closure:

A closed contour tends to be seen as an object. The Gestalt psychologists argued that there is a perceptual tendency to close contours that have gaps in them. …

Wherever a closed contour is seen, there is a very strong perceptual tendency to divide regions of space into “inside” or “outside” the contour. A region enclosed by a contour becomes a common region in the terminology of Palmer (1992). He showed common region to be a much stronger organizing principle than simple proximity. This, presumably, is the reason why Venn-Euler diagrams are such a powerful device for displaying the interrelationships among sets of data. …

Closed contours are extremely important in segmenting the monitor screen in windows-based interfaces. The rectangular overlapping boxes provide a strong segmentation cue, dividing the display into different regions. In addition, rectangular frames provide frames of reference: the position of every object within the frame tends to be judged relative to the enclosing frame. (See Figure 6.15.) —p194-196

Relative Size:

In general, smaller components of a pattern tend to be perceived as objects. In Figure 6.16, a black propeller is seen on a white background, as opposed to the white areas being perceived as objects. —p196

Wagon-wheel effect:

When all the elements are identical, the brain constructs correspondences based on object proximity in successive frames. This is sometimes called the wagon-wheel effect, because of the tendency of wagon wheels in Western movies to appear to be rotating in the wrong direction.

Experiments by Fleet (1998) suggest that the maximum change per frame of animation for motion to reliably be seen in a particular direction is about l?3 for the basic representation shown in Figure 6.36(a). Given an animation frame rate of 60 frames per second, this establishes an upper bound of 20 messages per second that can be represented. …

There are many ways in which the correspondence limitation can be overcome by giving the graphical elements a different shape, orientation, or color. —p219

Motion as an attribute of a visual object:

A number of studies have shown that people can see relative motion with great sensitivity. For example, contours and region boundaries can be perceived with precision in fields of random dots if defined by differential motion alone (Regan, 1989; Regan and Hamstra, 1991). Human sensitivity to such motion patterns rivals our sensitivity to static patterns; this suggests that motion is an underutilized method for displaying patterns in data.

For purposes of data display, we can treat motion as an attribute of a visual object, much as we consider size, color, and position to be object attributes. —p220

Innated motion detection module:

In addition to the fact that we can perceive causality using simple animation, there is evidence that we are highly sensitive to motion that has a biological origin. In a series of now-classic studies, Gunnar Johansson attached lights to the limb joints of actors (Johansson, 1973). He then produced moving pictures of the actors carrying out certain activities, such as walking and dancing. These pictures were made so that only the points of light were visible, and, in any given still frame, all that was perceived was a rather random-looking collection of dots, as shown in Figure 6.42(a). A remarkable result from Johansson’s studies was that viewers of the animated movies were immediately conscious of the fact that they were watching human motion. In addition, they could identify the genders of the actors and the tasks they were performing. Some of these identifications could be made after exposures lasting only a small fraction of a second.

Another experiment pointing to our ability to recognize form from motion was a study by Heider and Semmel (1944). In this study, an animated movie was produced incorporating the motion of two triangles and a circle, as shown in Figure 6.42(b). People viewing this movie readily attributed human characteristics to the shapes; they would say, for example, that a particular shape was angry, or that the shapes were chasing one another. Moreover, these interpretations were consistent across observers. Because the figures were simple shapes, the implication is that patterns of motion were conveying the meaning. Other studies support this interpretation. Rime et al. (1985) did a cross-cultural evaluation of simple animations using European, American, and African subjects, and found that motion could express such concepts as kindness, fear, or aggression, and there was considerable similarity in these interpretations across cultures, suggesting some measure of universality. —p223-224

Enriching diagrams with simple animation:

Animation of abstract shapes can significantly extend the vocabulary of things that can be conveyed naturally beyond what is possible with a static diagram. The key result, that motion does not require the support of complex depictive representations (of animals or people) to be perceived as animate, means that simplified motion techniques may be useful in multimedia presentations. … More design work and more research are needed. —p224-225

Definition of visual object:

For our present purposes, an object can be thought of as any identifiable, separate, and distinct part of the visual world. Information about visual objects is cognitively stored in a way that ties together critical features, such as oriented edges and patches of color and texture, so that they can be identified, visually tracked, and remembered. Because visual objects cognitively group visual attributes, if we can represent data values as visual features and group these features into visual objects, we will have a very powerful tool for organizing related data. —p227

Two theories about object recognition:

Two radically different theories have been proposed to explain object recognition:

The first is image-based. It proposes that we recognize an object by matching the visual image with something roughly like a snapshot stored in memory.

The second type of theory is structure-based. It proposes that is analyzed in terms of primitive 3D forms and the structural interrelationships between them.

Both of these models have much to recommend them, and it is entirely plausible that each is correct in some form. It is certainly clear that the brain has multiple ways of analyzing visual input. Certainly, both models provide interesting insights into how to display data effectively. —p227

People have a truly remarkable ability to recall pictorial images:

In an arduous experiment, Standing et al. (1970) presented subjects with a list of 2560 pictures at a rate of one every 10 seconds. This was like the family slide show from hell, it took them more than seven hours spread over a four-day period. Amazingly, when subsequently tested, subjects were able to distinguish pictures from others not previously seen, with better than 90% accuracy. —p228

Rapid serial visual presentation experiments:

People can also recognize objects in images that are presented very rapidly. Suppose you asked someone, “Is there a dog in one of the following pictures?” and then showed them a set of images, rapidly, all in the same place, at a rate of 10 per second. Remarkably, they will be able to detect the presence, or absence, of a dog in one of the images most of the time. This experimental technique is called Rapid serial visual presentation(RSVP). Experiments have shown that the maximum rate for the ability to detect common objects in images is about 10 images per second (Potter and Levy, 1969; Potter, 1976). —p228

Attentional blink:

If, in a series of images, a second dog were to appear in an image within 350ms of the first, people do not notice it (or anything else). This moment of blindness is the Attentional blink (Coltheart, 1999). It is conjectured that the brain is still processing the first dog, even though the image is gone, and this prohibits the identification of other objects in the sequence. —p228