Priming

If you identify something, even if it is a fleeting meaningless encounter, you will identify it faster if you see it again in the near future (Bartram, 1974). This effect is called priming. Most studies of priming involve intervals between the two events of minutes or hours, but Cave and Squire (1992) showed priming effects for picture naming that lasted for weeks.

Priming effects can occur even if information is not consciously perceived, and because of this priming is sometimes called implicit memory. Bar and Biederman (1998) exposed pictorial images to subjects so briefly that it was impossible for them to identify the objects. They followed the brief image exposure with what is called a visual mask, a random pattern shown immediately after the target stimulus to remove the target from the visual iconic store (a short-term buffer that holds the visual image for a fraction of a second), and they rigorously tested to show that subjects performed at chance levels when reporting what they had seen. Nevertheless, 15 minutes later, this unperceived exposure substantially increased the chance of recognition. Although the information was not consciously perceived, exposure to the particular combination of image features apparently primed the visual system to make subsequent recognition easier. They found that the priming effect decreased substantially if the imagery was displaced sideways. They concluded that the mechanism of priming is highly image dependent and not based on high-level semantic information.

Lawson et al. (1994) devised a series of experiments in which subjects were required to identify a specified object in a series of briefly presented pictures. Recognition was much easier if subjects had been primed by visually similar images that were not representations of semantically related objects. They argued that this should not be the case if objects are recognized on the basis of a high-level, 3D structural model of the kind that we will discuss later in this chapter; only image-based storage can account for their results. All of this adds support to the image-based theory of object recognition, because the effects are based on two-dimensional (2D) image information.

Also adding support to the multiple-view, image-based theory of object recognition is neurophysiological evidence from recordings of single cells in the inferotemporal cortexes of monkeys. Perrett et al. (1991) discovered cells that respond preferentially to particular views of faces. Figure 8.2 shows some of their results. One cell (or cell assembly) responds best to a three-quarter view of a face; another to profiles, either left or right; still another to a view of a head from any angle. We can imagine a kind of hierarchical structure, with the cell assemblies that respond to particular views feeding into higher level cell assemblies that respond to any view of the object.

Figure 8.2 The responses of three neurons in the temporal cortex of a monkey to faces in different orientations. At the top left is a cell most sensitive to a right profile; the right cell responds well to either profile; the cell at the bottom responds to a head irrespective of orientation.

Searching an Image Database

Presenting images rapidly in sequence (RSVP) may be a useful way to allow users to scan picture databases (Wittenburg et al., 1998; de Bruijn et al., 2000). The fact that people can search rapidly for an image in a sequence of up to 10 pictures per second suggests that presenting images using RSVP may be efficient. Contrast this with the usual method of presenting image collections in a regular grid of small thumbnail images. If it is necessary to make an eye movement to fixate each thumbnail image, it will not be possible to scan more than three to four images per second. Even though RSVP seems promising, there are a number of design problems that must be solved in building a practical interface. Once a likely candidate image is identified as being present in an RSVP sequence, the particular image must be extracted from the set. By the time a user responds with a mouse click several images will have passed, more if the user is not poised to press the stop button. Thus, either controls must be added for backing up through the sequence, or part of the sequence must be fanned out in a conventional thumbnail array to confirm that candidate’s presence and study it further (Wittenburg et al., 1998; Spence, 2002).

Rapid serial presentation may also provide a way of searching video content by viewing a rapidly presented sequence of selected frames (Tse et al., 1998). Wildemuth et al. (2003) suggested that a speed up of 64× faster than the original video may be optimal in allowing viewers to get the gist of what is occurring. Video data compressed in this way might make it possible to review a day’s worth of video in a few minutes.

Life Logging

It is becoming possible to have a personal memory data bank containing video and audio data collected during every waking moment through the course of a person’s lifetime. This can be achieved with an unobtrusive miniature camera, perhaps embedded in a pair of eyeglasses, and, assuming continuing progress in solid-state storage, the data can be stored in a device weighing a few ounces and costing a few hundred dollars (Gemmel et al., 2006). The implications of such life logging devices seem profound at first encounter; they appear to represent the ultimate memory aid—the user need never forget anything.

A key issue, though, is the interface to the stored data. If we want to recall a meeting we know happened sometime in 2004 we clearly cannot replay the entire year’s worth of data to find the event, even very fast. But the most serious problem for the life logging concept is that seeing a video replay is not at all the same as remembering. A replay of some forgotten event, such as a meeting, will be more akin to a re-experience, one that occurs without the context of the goals of the person involved, even if it is oneself. When people review their own videos, they do not spontaneously remember what happened; instead, they must mentally reconstruct it (Sellen et al., 2007). A meaningful reconstruction of a particular meeting may require a review of videos of other activities for weeks prior to the event, together with relevant documents and e-mail communications between the participants. The result is that a reconstruction of a single meeting might take days of work if a well-designed interface to the data is available.

Such arguments have led researchers to suggest that the main value of life logging is to jog the memory of the participant rather than being a substitute for memory. Accordingly, a study by Sellen et al. (2007) investigated the value of video imagery in helping people recall personal events using their SenseCam system. They found that a few days later SenseCam imagery roughly doubled the number of events that could be recalled, from two to four, for a particular half-day interval.

Another SenseCam application that has been explored is its use as an aid for the memory impaired. A trial was conducted in collaboration with a woman who had damage to the hippocampus area, a part of the brain that is involved in the ability to form long-term memories. In a comparison between careful note taking, the use of SenseCam video, and no aids, the SenseCam approach proved most effective, but the method involved the woman reviewing recent videos with her husband to construct meaning from them. So, again, it was not the video itself that provided the memory; rather, it was the video being used as a tool in the active construction of memories.

Overall, the research results suggest that video imagery can indeed help to support memory to some extent, but it does not provide anything close to perfect recall.

Geon Theory

Figure 8.4 provides a somewhat simplified overview of a neural network model of structural object perception, developed by Hummel and Biederman (1992). This theory proposes a hierarchical set of processing stages leading to object recognition. Visual information is decomposed first into edges, then into component axes, oriented blobs, and vertices. At the next layer, 3D primitives such as cones, cylinders, and boxes, called geons, are identified. A selection of geons is illustrated in Figure 8.5(a). Next, the structure is extracted that specifies how the geon components interconnect; for example, in a human figure, the arm cylinder is attached near the top of the torso box. Finally, object recognition is achieved.

Figure 8.4 A simplified view of Hummel and Biederman’s (1992) neural network model of form perception.

Figure 8.5 According to Biederman’s geon theory, the visual system interprets 3D objects by identifying 3D component parts called geons. (a) A sample of geons. (b) A human figure constructed from geons.

Silhouettes

Silhouettes appear to be especially important in determining how we perceive the structure of objects. The fact that simplified line drawings are often silhouettes may, in part, account for our ability to interpret them. At some level of perceptual processing, the silhouette boundaries of objects and the simplified line drawings of those objects excite the same neural contour extraction mechanisms. Halverston (1992) noted that modern children tend to draw objects on the basis of the most salient silhouettes, as did early cave artists. Many objects have particular silhouettes that are easily recognizable—think of a teapot, a shoe, a church, a person, or a violin. These canonical silhouettes are based on a particular view of an object, often from a point at right angles to a major plane of symmetry. Figure 8.6 illustrates canonical views of a teapot and a person.

Figure 8.6 Many objects have canonical silhouettes, defined by the viewpoints from which they are most easily recognized. In the case of the man, the overall posture is unnatural, but the component parts—hands, feet, head, etc.—are all given in canonical views.

David Marr suggested ways in which the brain might use silhouette information to extract the structures of objects (Marr, 1982). He argued that “buried deep in our perceptual machinery” are mechanisms that contain constraints determining how silhouette information is interpreted.

Three rules are embedded in this perceptual machinery:

Under Marr’s default assumptions, contour information is used in segmenting an image into its component solids. Marr and Nishihara (1978) suggested that concave sections of the silhouette contour are critical in defining the ways in which different solid parts are perceptually defined. Figure 8.8 illustrates a crudely drawn animal that we nevertheless readily segment into head, body, neck, legs, and so on. The most important features for this segmentation are concavities in the silhouette. Marr and Nishihara also proposed that the axes of the parts become cognitively connected to form a structural skeleton, so the object description consists of component parts and a description of how they are connected.

Figure 8.8 Concave sections of the silhouette define subparts of the object and are used in the construction of a structural skeleton. Redrawn from Marr & Nishihara (1978).

One of the consequences of structural theories of perception is that certain simplified views should be easier to read. There are practical advantages to this; for example, a clear diagram may sometimes be more effective than a photograph. This is exactly what Ryan and Schwartz (1956) showed when they found that a hand could be perceived more rapidly in the form of a simplified line drawing than in the form of a photograph (see Figure 8.9), but this result should not be overgeneralized. Other studies have shown that time is required for detailed information to be perceived (Price & Humphreys, 1989; Venturino & Gagnon, 1992). Simplified line drawings may be most appropriate only when rapid responses are required.

Figure 8.9 A photograph and a simplified line drawing of a hand. Ryan and Schwartz (1956) showed that a cartoon drawing was recognized more rapidly than a photograph.

Although image-based theories and structure-based theories of object recognition are usually presented as alternatives, it may be that both kinds of processes occur. If geons are extracted based on concavities in the silhouette, certain views of a complex object will be much easier to recognize. Further, it may well be that viewpoint-dependent aspects of the visual image are stored in addition to the 3D structure of the object. Indeed, it seems likely that the brain is capable of storing many kinds of information about an object or scene if they have some usefulness. The implication is that, even though 3D objects in a diagram may be more effective in some cases, care should be taken to provide a good 2D layout. Both image-based cues and structural cues should be clearly presented.

The Geon Diagram

Biederman’s geon theory, outlined earlier, can be applied directly to object display design. If cylinders and cones are indeed perceptual primitives, it makes sense to construct diagrams using these geon elements. This should make the diagrams easy to interpret if a good mapping can be found from the data to a geon structure. The geon diagram concept is illustrated in Figure 8.11(b). Geons are used to represent the major components of a compound data object, whereas the architecture of the data object is represented by the structural skeleton linking the geons. The size of a geon becomes a natural metaphor for the relative importance of a data entity, or its complexity or relative value. The strength of the connections between the components is given by the neck-like linking structures. Additional attributes of entities and relationships can be coded by coloring and texturing.

Figure 8.11 (a) Unified Modeling Language (UML) diagram. (b) Geon diagram constructed using a subset of Biederman’s geon primitives. Both diagrams show the same set of entities and relationships.

We evaluated the geon diagram concept in a comparison with Unified Modeling Language (UML) diagrams (Irani et al., 2001). UML is a widely used, standardized diagramming notation for representing complex systems. Equivalent diagrams were constructed by matching geon elements to UML elements, as shown in Figure 8.11(a). We found that when the task involved rapid identification of substructures in a larger diagram, participants performed both faster and with only half the errors using the geon diagrams. Another experiment showed that geon diagrams were easier to remember.

In Biederman’s theory, surface properties of geons, such as their colors and textures, are secondary characteristics. This makes it natural to use the surface color and texture of the geon to represent data attributes of a data object. The important mappings between data and a geon diagram are as follows.

Although the geon diagram is a 3D representation, there are reasons to pay special attention to the way it is laid out in 2D in the x, y plane. As discussed earlier, some silhouettes are especially effective in allowing the visual system to extract object structure. A commonsense design rule is to lay out structural components principally on a single plane. A diagramming method resembling the bas-relief stone carvings common in classical Rome and Greece may be optimal. Such carvings contain careful 3D modeling of the component objects, combined with only limited depth and a mainly planar layout.

Abstract semantics may be expressible, in a natural way, through the way geons are interconnected. In the everyday environment there is meaning to the relative positioning of objects that is understood at a deep, possibly innate level. Because of gravity, above is different from below. If one object is inside another transparent object, it is perceived either as contained by that other object or as a part of it. Irani et al. (2001) suggested that the semantics inherent in the different kinds of relationships of real-world objects might be applied to diagramming abstract concepts. Based on this idea, the researchers developed a set of graphical representations of abstract concepts. Some of the more successful of these mappings are illustrated in Figure 8.12 and listed as follows. Only some of these are given formal status as guidelines.

Figure 8.12 Certain spatial relationships between objects can readily represent abstract concepts. (a) That objects belong to the same class is better shown by shape than by color. (b) A part-of relationship. (c) A dependency relationship. (d) Strong and weak relationships.

Mental Images

Much of dual coding theory is uncontroversial. It has been known for decades that there are different neural processing centers for verbal information and visual information. Examples of purely verbal processing brain regions are Broca’s area, which is part of the frontal cortex that when damaged results in an inability to speak intelligibly, and Wernicke’s area, which results in an inability to comprehend speech when damaged.

It is the idea that we can “think” visually that is relatively recent. One line of evidence comes from mental imaging. When people are asked to compare the size of a light-bulb with the size of a tennis ball, or the green of a pea with the green of a pine tree, most claim that they use mental images of these objects to carry out the task (Kosslyn, 1994). Other studies by Kosslyn and his coworkers show that people treat objects in mental images as if they have real sizes and locations in space. Recently, positron emission tomography (PET) has been used to reveal which parts of the brain are active during specific tasks. This shows that when people are asked to perform tasks involving mental imaging the visual processing centers in the brain are activated. When they mentally change the size and position of an imagined object, different visual areas of the brain are activated (Kosslyn et al., 1993). In addition, if visual processing centers in the brain are damaged, mental imaging ability is disrupted (Farah et al., 1992). It would seem that when we see a cow and when we mentally visualize a cow, the same neural pathways are excited, at least in part. Indeed, modern visual memory theory takes the position that visual object processing and visual object recognition are part of the same process. To some extent, the visual memory traces of objects and scenes are stored as part of the processing mechanism; thus, it is not necessary for an object to be fully processed for recognition to take place (Beardsley, 1997). This can account for the great superiority of recognition over recall. We can easily recognize that we have seen something before, but reproducing it in a drawing or with a verbal description is much more difficult.

This implies that the simple dual coding theory illustrated in Figure 8.15 is misleading in one important respect. The diagram implies that memories for visual inputs are stored after processing through the visual system; however, image memory is not a separate storage bin but an integral part of the perceptual system. Visual images are analyzed on their way through the system and visual memories are activated by the incoming information as they simultaneously shape it. There is no separate store; memory is distributed through the brain at every level of processing.

Object Categorization

Categorization is the abstraction of things and ideas into groups and most if not all categories have verbal labels. The words cheese, tree, plant, company, and bacteria are all category labels. Virtually all of the things we see and think of as objects are classified automatically in the brain within 100 ms of our seeing them. When we see a spoon, not just its shape is registered, but the verbal label also becomes activated. A large array of concepts relating to culinary activity and eating may become primed and brought to a state of readiness.

Objects that we know well combine clusters of attributes that are visual with clusters of attributes that are verbally related concepts. They may also have properties that awaken activities in our movement control systems, in the case of things that we may pick up and manipulate or use as tools. Kahneman et al. (1992) named this collection of visual and nonvisual properties an object file (see Figure 8.16).

Figure 8.16 The object file is a proposed cognitive mechanism that links multiple attributes of an object. These attributes can be both visual and nonvisual.

Even objects that are unfamiliar can be categorized by their utility. A fist-sized chunk of any material may be used as a projectile, as potential building material, or, if it is hard, as a tool.

Our modern understanding of how the human brain categorizes objects began with the pioneering research of Eleanor Rosch (1973, 1975). Prior to this, from the time of Aristotle, object classification had been treated as if the brain did a formal logical analysis of sensory data. This approach leads to a world in which things belong to categories with sharp boundaries. Either something is a fruit, or it is not. Rosch’s work showed that the way we actually perceive objects is much more flexible. People perceive apples and oranges to definitively be fruits, but they are much less certain about cucumbers and tomatoes.

Rosch discovered that there are certain categories she called basic level, with other categories above and below. The concept of dog is a basic-level category, with animals as a superordinate category and particular breeds as subordinate categories. Basic-level categories are the most commonly used broad categories and are learned first by infants. People are more likely to categorize a particular animal, such as pet canine, as a dog than as members of the higher level category of animal. Rosch defined basic-level categories in terms of three criteria: They have similar shape; this is obviously true of dogs, which are much more mutually similar than, say, the category of animals. They have similar motor interactions; that is, we tend to do the same things with members of a particular class. They have similar nonvisual attributes, which refers to all the nonvisual properties we learn to be associated with objects, including the materials they are made from and their likely associations with other objects. Because of the visual similarity of basic-level categories, Rosch observed that usually a single drawing can be used to represent the entire class. Such drawings are also classified with the shortest reaction times.

Later work by Jolicoeur et al. (1984) added refinements to Rosch’s work. He and his co-workers found that certain category members are identified faster than others even though they are at the same level of the hierarchy. For example, a medium-sized canine with neither especially long or short legs will be categorized as a dog faster than a dachshund.

Canonical Views and Object Recognition

Palmer et al. (1981) showed that not all views of an object are equally easy to recognize. They found that many different objects have something like a canonical view from which they are most easily identified. From this and other evidence, a theory of object recognition has been developed, proposing that we recognize objects by matching the visual information with internally stored viewpoint-specific exemplars, or prototypes; the brain stores a number of key views of each object (Edelman & Buelthoff, 1992; Edelman, 1995). This is the image-based object memory theory introduced at the start of this chapter. The views are not simple snapshots; however, they allow recognition despite simple geometric distortions of the image that occur in perspective transformation. This explains why object perception survives the kinds of geometric distortions that occur when a picture is viewed and tilted with respect to the observer.

There are strict limits on the extent to which we can change an image before recognition problems occur. Palmer et al. (1981) had observers rate how well pictures taken from different perspectives resembled the object depicted. The results showed strongly that certain views were judged more typical than others (see Figure 8.17). Moreover, this had a large effect on the amount of time it took subjects to name the object shown. Other studies have revealed that objects are named faster when they are upright (Jolicoer, 1985), but changing the size of the represented object has a relatively small effect. Also, numerous studies show impaired face recognition if the faces are shown upside down (Rhodes, 1995).

Figure 8.17 Noncanonical and canonical views of a horse and a car.

There are many cases where simple images cannot be used to represent categories of objects. One reason is that most things belong to many overlapping sets of categories, and many categories do not have canonical object representations. Consider the category of pet. A pet can be a goldfish, an insect, or a snake, as well as the more typical dogs and cats. No simple sketch can represent all of these, as they do not share a canonical set of visual features. Some abstract categories are even more difficult. The philosopher Wittgenstein (1953) used the example of games to argue that categories should be thought of as loosely associated bundles of properties, rather than concepts that can be defined by a few formal rules. Board games, sports such as soccer, and games such as charades all belong to the game category. Such categories are very difficult to formally pin down, and, like pets, have no canonical representation.

What does this have to do with visualization? In many diagrams and charts it is common to use pictorial symbols to represent various kinds of categorical information. This is especially true of so-called infographics. Stacks of little house-shaped symbols and stacks of little car-shaped symbols may be used to represent the rates of home and car ownership in different countries. A hamburger is sometimes used to represent junk food in a chart. Broccoli has achieved similar status as a symbol for healthy food. Figure 8.21 shows an example of these symbols used in an information graphic. In this case, the effectiveness of hamburgers and broccoli does not have to do with their having a canonical visual form in the Roschean sense. Rather, if they are effective it is because of their culturally determined status as symbols for these categories.

Figure 8.18 A concept map surrounding the concept “information visualization.”

Figure 8.19 An entire week of CNN news stories is summarized in a ThemeScape. From Wise et al. (1995). Reproduced with permission.

Figure 8.20 A diagram showing the links between academic disciplines using clickstream data. From Bollen et al. (2009). Reproduced with permission.

Figure 8.21 In infographics, repeated pictorial icons are often used to represent quantity.

Concept Maps and Mind Maps

A technique that is promoted as a learning aid for students is called mind mapping (Jonassen et al., 1993). It consists of sketching out links between concepts, as illustrated in Figure 8.18. Usually, such maps are constructed informally by sketching them on paper, but computer-based tools also exist. Essentially, a concept map is a type of node–link diagram in which the nodes represent concepts and the links represent relationships between concepts. An individual can use a concept map as a tool for organizing his or her own personal concept structure, and it may reveal patterns of relationships between ideas that had not been evident when the concepts were stored internally. A concept map can also be constructed as a group exercise, in which case it becomes a tool for building a common understanding.

Most educational theory suggests that in order to learn concepts it is important that students actively work to integrate new ideas into the framework provided by their existing knowledge (Willis, 1995). This is the central theme of constructivist education theory that has its roots in the work of the Russian psychologist Lev Vygotsky (1978). Constructivism also emphasizes the social roots of knowledge and that much of our concept formation is shaped by social pressures (Karagiorgi & Symeou, 2005).

Superficially, concept maps would seem to fit well with constructivist theory. To construct such a map, students must actively draw out links between various concepts as they understand them. The problem is that the cognitive engagement tends to be somewhat superficial, since it does not require that students think about the nature of the links. For example, simply knowing that there is a link between disease and urban living is of marginal value, but if we know something about how diseases are propagated then we can design better sanitation systems. A more elaborate concept might be used to trace out propagation mechanisms, but written notes may be more effective for the reasons outlined in Chapter 9.

The best note-taking techniques appear to be hybrids combining concept organization techniques (using connecting lines and boxes) with more detailed textual information (Novak, 1981). Other structured note-taking methods can be effective, such as arranging ideas in a matrix (Kiewra, 1999).

There are other reasons for mapping concepts into a visual space. Recently, sophisticated computer algorithms have been developed to parse large text databases in order to understand how ideas, as expressed in society at large, are related to one another and how they develop over time. For example, the SPIRE system creates a classification of documents with respect to a keyword query and can be applied to databases consisting of hundreds of thousands of documents (Wise et al., 1995). The result of the SPIRE algorithm is a set of vectors in an n-dimensional space. To help people understand the resulting clusters of documents, Wise et al. created a visualization called a ThemeScape, which shows the two most important dimensions as a kind of data landscape. This is illustrated in Figure 8.19. Flags on top of hills label and identify the largest clusters of documents in this space. Essentially, a ThemeScape uses the two most significant dimensions of the abstract data space to create a smoothed 2D histogram. Spatial proximity and salience show the major concentrations of information and, to some extent, their relationships. This kind of display will be useful when two dimensions really do capture most of the variability in the data.

Other visualizations have been designed to map out the temporal evolution of larger themes in text databases. ThemeRiver is an application developed by Havre et al. (2000) designed to show how ideas become more prevalent over time, and then fade away. It has been used to show the temporal trajectory of major news stories.

Visualizations such as ThemeScape and ThemeRiver can perhaps be used to understand the zeitgeist of the time. Politicians want to know which issues are receiving the most press attention and these displays may help. But, like concept maps, both displays provide only the most superficial information about the relationships of ideas. In addition, because very high-dimensional data has been mapped into a low-dimensional space, proximities between concepts are only sometimes meaningful.

Other systems have been developed to map human knowledge into a node–link diagram. Bollen et al. (2009) used a very large university database derived from Internet searches carried out by university students and faculty. Their goal was to understand which areas of scholarly endeavor are most closely related. Their algorithm judged there to be a connection between disciplines when there were information selections (via mouse clicks) in different disciplines by the same person, close together in time. This is called clickstream data. The resulting map of knowledge is shown in Figure 8.20. It shows the physical sciences to the right and social sciences and clinical areas to the left, with the arts in the center. This particular layout is arbitrary, an artifact of the algorithm, but the connections between areas is meaningful. Highly interconnected areas mean that scholars were researching in both disciplines. Such a map might be used by university administrators thinking about the logical structure of faculties, or it might be used to organize government funding agencies according to the most closely linked scholarly areas.

Static Links

When text is integrated into a static diagram, the Gestalt principles discussed in Chapter 6 apply, as Figure 8.22 shows. Simple proximity is commonly used in labeling maps. A line drawn around the object and text creates a common region. A line or common region can also be used to associate groups of objects with a particular label. Arrows and speech balloons linking text and graphics also apply the principle of connectedness.

Figure 8.22 Gestalt principles used to guide the linking of text and graphics: (a) Proximity. (b) Continuity/connectedness. (c) Common region. (d) Common region combined with connectedness.

Priming, Categorization, and Trace Theory

We now return to the topic of priming and discuss how it affects categorization. Priming can have both positive and negative consequences with regard to categorization. In some ways, priming can be regarded as a biasing of perception. Ratcliff and McKoon (1996) made sketches of pairs of objects that were visually very similar but belonged to very different categories (Figure 8.23). They found normal priming when an image was shown for a second time, a week later, in a rapid naming task; however, when the similar image (within a different category) was shown a week later, naming was actually slowed.

Figure 8.23 Pairs of sketches developed by Ratcliff and McKoon (1996). Each pair has visual similarity, but the objects represented have very different uses.

They argued that the result adds support to a trace theory of cognitive skill learning. According to this theory, whenever we successfully complete a cognitive activity, such as identifying an object, all the various neural pathways that were activated at the time become strengthened, so that the next time the same object is presented, processing is facilitated. But, strengthening a set of pathways inevitably means that alternative pathways are less likely to be activated in similar circumstances, introducing a form of bias. One of the implications is that priming, and indeed all categorization, is a two-edged sword. Priming and categorization can lead to errors. Once we learn a particular interpretation of a pattern, we get faster at classifying it in a certain way, automatically classifying it at a glance, but this means that we are less likely to come up with alternative interpretations.

We shall return to the issue of priming, and sensory learning in general, in the following chapters as we begin to consider the process of visual thinking.