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Approaches to Visual Search: Feature Integration Theory and Guided Search

Abstract and Keywords

In her original Feature Integration Theory, Anne Treisman proposed that we process a limited set of basic preattentive, visual features in parallel across the visual field. Binding those features together into coherent, recognizable objects requires selective attention of item after item. In Treisman’s original conception, searches were divided into parallel feature searches and other serial self-terminating searches. Wolfe’s Guided Search model added the idea that the deployment of attention could be guided by preattentive information. In this view, the efficiency of search is related to the effectiveness of guidance on a continuum from perfect guidance, in the case of simple feature pop-out, to no guidance when no basic features distinguish target from distractors. This chapter reviews the evidence for different basic, preattentive features and describes the current understanding of the rules of guidance, the mechanics of visual search, and the relationship of these processes to visual awareness.

Keywords: visual search, preattentive, feature integration, guided search, selective attention, object recognition, parallel processing, serial selection, pop-out, binding


The visual world is full of objects and it is an interesting fact that we can see more of that world than we can understand at any given moment. Thus, looking at this picture of a park just off Market St in San Francisco (Fig.2.1), you immediately see a rectangle filled with visual stuff but you do not immediately know the answer to fairly basic questions that one might ask about the objects in the scene. What colour is the bus? (white). Are there any people present? (no). This chapter will be devoted to the investigation of these limits on our perception through the theoretical lenses of Feature Integration Theory (Treisman 1988; Treisman and Gelade 1980) and Guided Search Theory (Wolfe 1994, 2007; Wolfe, Cave, and Franzel 1989). The chapter will be organized around the four terms that make up the names of the theories. Features: What are the basic features that are seen immediately in that rectangle of an image? Integration: How are those features combined into object representations? In particular, we will focus on the need for attention-demanding feature ‘binding’ in object recognition. Guidance: How can unbound features be used to guide Search for those objects or properties of a scene that are not immediately available to us when we look at a scene?

Visual Search

Approaches to Visual SearchFeature Integration Theory and Guided SearchClick to view larger

Figure 2.1 A park in San Francisco.

Most of the work discussed in this chapter will involve the visual search paradigm in which an observer looks for one or more targets in a display containing some distractor (p. 12) items. Search tasks are ubiquitous in daily life. Where is my coffee cup, the cellphone, the keyboard, the mouse, etc.? Such searches are typically concluded so quickly that we do not even register them as searches. We notice when the search is more prolonged. Where on stage is my child amidst the rest of the school choir? Moreover, our civilization has created socially important search tasks from airport baggage security (Gale, Mugglestone, Purdy, and McClumpha 2000; Rubenstein 2001) to medical image perception (Berbaum et al. 1998; Krupinski, Berger, Dallas, and Roehrig 2003; Kundel and Nodine 2004; Nodine, Krupinski, and Kundel 1993). These typically require trained expert searchers.

Beyond its face validity as a task that we perform all the time, visual search is a useful paradigm in the lab because it gives us a way to quantify the capacity limitations described in our initial example. With the San Francisco image, we can assert that you were not immediately aware of the presence of the bus or the absence of humans. With stimuli like those in Fig. 2.2, we can measure that.

Approaches to Visual SearchFeature Integration Theory and Guided SearchClick to view larger

Figure 2.2 Search tasks: in each display, there are two targets; one, rather obviously, is red. The other is the square of medium size.

Suppose that we showed observers a succession of displays like this and asked them to press one key if a red square was present and another if no red square was present. We could measure reaction time (or ‘response time’—in either case, abbreviated ‘RT’) and/or accuracy as a function of ‘set size’, the number of items in the display. We would find, to a first approximation, that the set size did not matter. Observers would be as fast and accurate with the 33-item display on the right as they are with the 9-item display on the left. The slope of the RT x set size function will be near zero. We can call such searches ‘efficient’ searches. In contrast, if observers were asked to find the item of medium size amidst the large and small items of Fig.2.2, we would obtain a very different pattern of results. RTs would increase roughly linearly with set size. The slope of the RT x set size function would probably be in the range of 20–40 msec/item for trials having (p. 13) a medium-sized target. For target-absent trials, the slope would be roughly twice the target-present slope. (In fact, the ratio of absent to present slopes in an experiment with targets on 50% of trials appears to be reliably a bit more than 2:1—a fact that has theoretical importance, to be discussed below (Wolfe 1998).) If the display was presented only briefly or the observer was forced to respond by some very short deadline, we would find that the error rate for the 33-item display would be higher than the error rate for the 9-item display in the medium-target task but not in the red-target task. These speed–accuracy trade-off methods can be powerful tools in theoretical analysis of search tasks (Dukewich and Klein 2009; Guest and Lamberts 2011; McElree and Carrasco 1999). We can call tasks producing these sorts of results ‘inefficient’.

Feature Integration Theory

Approaches to Visual SearchFeature Integration Theory and Guided SearchClick to view larger

Figure 2.3 Conjunction search: look for the small red items.

The distinction between what we are calling ‘efficient’ (Neisser 1963) and ‘inefficient’ (Atkinson, Holmgren, and Juola 1969) was central to the development of Treisman’s Feature Integration Theory (Treisman and Gelade 1980). She argued that there was a limited set of basic features that could be processed ‘preattentively’, in parallel. In Fig. 2.2, colour is the example of such a feature. A target, defined by a unique basic feature would ‘pop out’ of a display. It would be available to awareness and for action without apparent capacity limitations. In contrast, many other search tasks, even if they involved quite simple perceptual discriminations (e.g. the distinction between big, medium, and small) showed a pattern of search results that Treisman argued was consistent with a serial self-terminating search; a search that proceeded one item after another until the target was discovered or the search was terminated—in the simplest case, after examining every item and determining that each was not the target. In a serial, self-terminating search, (p. 14) observers would need to examine half of the items, on average, when the target was present and all of the items when it was absent. This predicted a 2:1 slope ratio between target-absent and target-present RT x set size functions; hence, the theoretical significance of the finding that those slope ratios may be reliably greater than 2:1.

The description of search tasks as ‘parallel’ and ‘serial’ has proven enduringly popular—to the distress of theoreticians who note that the patterns of results can be explained in many ways (Townsend 1971, 1990; Townsend and Wenger 2004; J. Palmer 1995; J. Palmer and McLean 1995). It is to avoid those theoretical commitments that we use the theory-neutral terms ‘efficient’ and ‘inefficient’.

Conjunction searches like the one illustrated in Fig. 2.3 were critical to Treisman’s originally dichotomous view of search. In Fig. 2.3, the target is defined by the conjunction of two features. It is the small red item. A sole small item in a field of large items or a sole red item in a field of black items would pop out. However, the small red item did not. The phenomenal experience of conjunction search was not a pop-out experience, and in a large body of Treisman’s data, the RT x set size functions had slopes in the inefficient range. Treisman’s conclusion was that while basic features were processed in parallel across the field, serial deployment of attention was required to integrate two or more features together. To change the language a little, serial attention was needed to ‘bind’ features to object representations. This was Treisman’s solution to the ‘binding problem’. There are multiple definitions of the problem (Treisman 1996). A useful way to think about it is in neural terms. If we stay with Fig. 2.3, there would be neurons that respond to an item’s size and others that respond to its colour. How would one know which colours went with which size? Having higher-order cells for all possible combinations of all features at all locations seems implausible. Thus, there was a problem, and a limited-capacity, attention-directed binding process was the solution in Feature Integration Theory.

Here we are talking about the binding of two properties of one object, colour and size. However, as Treisman (2006) explains, binding is a more general issue. She identifies no fewer than seven types of binding. Some of these are conceptually quite similar to (p. 15) ‘property binding’ (her name in this paper for the binding of basic features like colour and size). For instance, knowing that two parts belong to one object is also a binding problem. Other forms could be similar at an abstract or computational level while being rather different phenomenologically. For instance, she notes that the fine discrimination of specific orientations (e.g. 22 deg. from 26 deg.) is thought to rely on ratios of two or more broadly tuned orientation channels (Olzak and Thomas 1986). So, in some sense, the outputs of those two coarse channels must be bound to produce the fine resolution. Unlike the binding of colour and size, the unbound components are not introspectively available in this case of what Treisman calls ‘range binding’.

For those forms of binding, where the unbound properties are available, illusory conjunctions have been an important phenomenon, marshalled in support of Feature Integration Theory. The phenomenon is not easily demonstrated on the static page, but take a quick glance at Fig. 2.4 and then cover it up so that you cannot see it before returning to this text.

If you followed instructions, you are probably in a good position to list many of the colours and shapes that are present in the figure. You are probably quite sure that there was a diamond and, if asked, you are quite sure that there was no circle present. You are in a less confident position to report the conjunctions of colour and shape. What colour was the diamond? Odds are that you would not say ‘blue’ since nothing in the image was blue. However, Treisman found that there was quite a good chance that you would report seeing the diamond in the colour of one of the other shapes (Treisman and Schmidt 1982), perhaps yellow. It was as if the colours and shapes were floating free at some level in your visual system and, especially once the image was no longer present to provide a clear answer, those features could bind to form illusory conjunctions.

Approaches to Visual SearchFeature Integration Theory and Guided SearchClick to view larger

Figure 2.4 Stimuli for an illusory conjunction demonstration. Take a quick glance at the figure, cover it up, and return to the text.

The original idea was that features were completely free-floating, but even in the first glimpse, the world doesn’t look like a soup of loose features, and subsequent work reveals a role for location (Prinzmetal and Keysar 1989; Hazeltine, Prinzmetal, and Elliot 1997). The illusory conjunction phenomenon went well beyond simple shapes and colours to include, for example, letters and words (Prinzmetal 1991; Treisman and Souther 1986; Virzi and Egeth 1984) and clock times (Goolkasian 1988). Thus, basic features were not (p. 16) the only units involved. It has been argued that illusory conjunctions are basically a phenomenon of memory (Briand and Klein 1989; Tsal 1989a, 1989b) and, indeed, memory for basic features can be quite terrible. In one experiment, Wolfe et al. showed observers an array of 20+ red and green dots. At one moment, signalled by a tone, one of the dots brightened and either did or did not change colour from red to green or green to red. Observers were close to chance performance when asked if the new colour was the same as the old one (Wolfe, Reinecke, and Brawn 2006). Observers knew that they had been looking at red and green dots, but the binding of colour to dot was clearly very fragile.

Still, binding errors need not be entirely in the remembered past tense. When items are relatively close to each other, especially in the periphery, observers experience ‘crowding’ phenomena (Levi 2008; Pelli and Tillman 2008) in which it may be possible to see features but not know how those features are bound together or even how they are bound to a location. That is, you might know there is a line at a location but you may be unable to determine its orientation. As Treisman would predict, attention allows illusory conjunctions to be resolved (Scolari, Kohnen, Barton, and Awh 2007) but the spatial grain of attention in the periphery is coarse (S. He, Cavanagh, and Intriligator 1996; Intriligator and Cavanagh 2001). So, away from fixation, illusory conjunctions may be a fact of life, even when the image remains continuously visible. Rosenholtz et al. talk about ‘mongrels’ that are created in the periphery by a system with weak spatial localization abilities and in which image statistics (e.g. average orientation) are calculated over multiple items. Portilla and Simoncelli (2000) developed a method for generating natural-looking textures from a set of image statistics. That is, if you took a picture of a forest, extracted the Portilla and Simoncelli statistics, and then synthesized a new image from those statistics, it would not be the original image but it would look forest-like. Rosenholtz et al. note that if this process is run over a standard search display like a search for a T among Ls, it creates new images in which some of the Ls have come to look like Ts. Arguing that this is the situation for vision away from the point of fixation, they have used the phenomenon to develop a theory of search performance (Rosenholtz, Chan, and Balas 2009). Even if crowding and mongrels are not quite the same things as illusory conjunctions, the problem of jumbled, possibly misbound features is similar. Attention-demanding binding is the Feature Integration solution to that problem (or, in more recent formulations, a leading solution among several—Treisman 2006).

Integration: Is Attentive Binding Really Necessary?

Various challenges have been presented to the idea that binding requires attention and that binding is required for object identification. Quite early, Houck and Hoffman did an experiment with the McCollough Effect (Houck and Hoffman 1986). The McCollough effect is an orientation-contingent, colour after-effect based on adapting to, say, red (p. 17) vertical and green horizontal gratings. After adaptation, black and white vertical gratings would look greenish while black and white horizontal gratings would look pink (McCollough 1965). Houck and Hoffman found that the effect could be produced without attention to the adapting stimuli even though the effect clearly requires some sort of association of orientation and colour. If attention is required to bind colour to orientation, how could orientation-contingent colour adaptation occur without attention? One could attack the notion of ‘without attention’. It is remarkably difficult to guarantee that a stimulus is unattended and it is virtually impossible to convince reviewers of this claim. As a consequence, more recent papers often refer to the ‘near-absence’ of attention (Reddy and Koch 2006; Reddy, Wilken, and Koch 2004).

Putting aside that methodological issue, there are situations where simple spatial co-occurrence of two features is all that is needed for the task at hand. Houck and Hoffman show one such case. A similar account could be invoked to explain other situations where conjunctive properties influence behaviour, apparently without attentive binding (Mordkoff, Yantis, and Egeth 1990). A particularly interesting case has to do with our remarkable ability to determine if humans or animals are present in scenes apparently without directing attention to the target object (VanRullen and Thorpe 2001; Kirchner and Thorpe 2006; Evans and Treisman 2005).

It may be that loose collections of unbound features are adequate to discriminate animal from non-animal (Treisman 2006), as is cartooned in Fig. 2.5.

Approaches to Visual SearchFeature Integration Theory and Guided SearchClick to view larger

Figure 2.5 An unbound collection of features might be all that is required to determine if the ‘animal’ is on the left or right. This is only a cartoon as we do not know exactly what those ‘unbound features’ would look like.

Approaches to Visual SearchFeature Integration Theory and Guided SearchClick to view larger

Figure 2.6 Search for the pluses with purple vertical and green horizontal components. Before attention arrives, all of these items are unbound collections of the same green, purple, vertical, and horizontal features.

Figure 2.5 is a cartoon because we cannot easily list or portray unbound ‘animal’ or ‘oven’ features but the point is that some discriminations in laboratory tasks will not require binding. Co-occurrence may be enough, though it must be confessed that other results such as the ability to identify a face in the ‘near-absence’ of attention are harder to explain with this unbound bundle of features idea (Reddy and Koch 2006). (p. 18)

Perhaps the best way to demonstrate the critical role of attentive binding is to create simple stimuli that eliminate the effectiveness of spatial co-occurrence of features. Figure 2.6 shows an example. You are a looking for ‘pluses’ with purple vertical and green horizontal components. You will find that this is an inefficient search. (There are three targets. Did you find them all?) Wolfe and Bennett (1997) argued that this was hard because before the arrival of attention, each one of these items was an unbound collection of purple, green, vertical, and horizontal features. As all the features of one item are in more or less the same location, associated with the same object, attention and binding are required before it can be determined if purple goes with vertical or horizontal.

One could object that, in fact, a purple-vertical-green-horizontal plus is just a green-vertical-purple-horizontal plus, rotated 90 degrees and, as a consequence, it is not surprising that one is hard to find amidst the other. Figure 2.7 shows stimuli intended to counter that argument. Here the targets and distractors are clearly very different objects. The targets look like puzzle pieces and the distractors look like some unusually rectangular, single-celled organism with a flagellum. These are designed to have very similar preattentive, unbound features. Each has a closed region and some straight and curved lines. The result, in the upper of the two search displays of Fig. 2.7, is a relatively inefficient search (Wolfe and Bennett 1997). Very similar stimuli produce an easier search in the second example because the targets are now the only items with the preattentively available attribute of ‘closure’ (Chen 1982; Elder and Zucker 1993, 1998).

Approaches to Visual SearchFeature Integration Theory and Guided SearchClick to view larger

Figure 2.7 A search for something like puzzle pieces among distractors with tails. This is easier in the lower panel because there the targets are the only items exhibiting ‘closure’.

Treisman’s fundamental point about binding seems to be valid. If you eliminate the usefulness of spatial co-occurrence and if no basic feature distinguishes targets from distractors, an inefficient search is going to be required as the observer binds one item after another in the effort to identify a target. (p. 19)

Guidance: Efficient Conjunction Search and the Role of Guidance

Approaches to Visual SearchFeature Integration Theory and Guided SearchClick to view larger

Figure 2.8 If you search for the red ‘K’, you will guide attention to red items and away from black. Reproduced from Egeth, H. E., Virzi, R. A. & Garbart, H. Searching for conjunctively defined targets. Journal of Experimental Psychology: Human Perception and Performance, 10, 32–39 © 1984, American Psychological Association.

While it may be true that binding requires attention, it turns out not to be true that search tasks fall into two neat categories; efficient feature, ‘pop-out’ searches that do not require binding and can be done in parallel and all other searches that do require binding and are thus inefficient. While Treisman’s original data were broadly consistent with this view, it rapidly became clear that conjunction searches, in particular, did not need to be inefficient. In the 1980s, exceptions started to appear (Alkhateeb, Morland, Ruddock, and Savage 1990; Dehaene 1989; McLeod, Driver, and Crisp, 1988; Nakayama and Silverman 1986; Sagi 1988; Zohary and Hochstein 1989). At first, it seemed like there might be a set of exceptions to the general rule, rather like irregular verb forms. Maybe stereopsis (Nakayama and Silverman 1986) or motion (McLeod et al. 1988) were special (p. 20) features that operated under different rules of binding. However, by 1989, Wolfe et al. had data showing quite efficient search for colour x orientation searches and enough other examples had surfaced that it was time for a modification of the basic Feature Integration story (Wolfe, Cave, and Franzel 1989).

In retrospect, the key idea was made obvious by Egeth et al. (1984) and illustrated in Fig. 2.8. If you are asked to search for the letter ‘V’, you will perform some sort of relatively inefficient search. (It is complicated because letters are complicated bundles of features.) If you are asked to look for a red ‘T’, you will, again, perform some sort of relatively inefficient search, but this time only through the red items. A black item is simply never going to be a red T and you can use that knowledge about the basic features of the target to guide attention to items that have the features that make them more likely to be the target. If half of the items were red in this example, then the slope of the RT x set size function would be half that of an unguided letter search because half of the letters could be eliminated without ever being attended. The key observation of Guided Search (Wolfe et al. 1989) was that this could be a general property of search tasks. If observers were asked to search for a red vertical item among red horizontals and green verticals, preattentive, basic feature information could be used to guide attention toward red items and toward vertical items. The intersection of the sets of red and vertical items would be an excellent place to look for a red vertical target. Indeed, were guidance perfect, search for a basic conjunction of two features like colour and orientation should have been perfectly efficient. Such searches are more efficient than original Feature Integration Theory predicted but the slopes are greater than zero. We will return to the apparent imperfection of guidance later. For the present, where Feature Integration Theory saw two types of search—parallel and serial—Guided Search saw two ends of a continuum of guidance. Efficient ‘parallel’ search tasks were those where guidance was adequate to allow the deployment of attention to the target item first time, every time. Inefficient searches were those where no guidance was possible beyond guidance to the presence of an object in a location. Each of these items needed to be attended to, one after the other, until the target was found (p. 21) or the search was abandoned. In between were guided searches, where some basic feature information could be used to prioritize some items as more worthy of attention than others. The slope of the RT x set size function, in this view, becomes an estimate of the percentage of items that remain candidate targets even after guidance has done its work. If unguided, inefficient search has a standard slope of, say, 40 msec/item, then a search task producing a slope of 10 msec/item would imply that guidance could eliminate 3/4 of the items from consideration.

Working out the details of this straightforward idea is not entirely straightforward. The remainder of this chapter will discuss the nature of guiding features and the mechanics of Guided Search. While this chapter will focus on Guided Search, it is worth noting that there is a family of models that share aspects of Feature Integration and/or Guided Search architecture to greater or lesser extent (Cave 1999; Tsotsos 2011; E. Cohen and Ruppin 1999; Vidyasagar 1999; Hubner 2001; Lee, Buxton, and Feng 2005; Mozer and Baldwin 2008).


The nature of guiding features

In Treisman’s original formulation, ‘the visual scene is initially coded along a number of separable dimensions, such as color, orientation, spatial frequency, brightness, direction of movement’ (Treisman and Gelade 1980). In her terminology, ‘colour’ would be a ‘dimension’ and ‘red’ a feature within that dimension. We often use the term ‘attribute’ for ‘dimension’ since dimension also gets used to talk about depth—which is a preattentive attribute. The cognitive architecture for early Feature Integration and early Guided Search was something like what is shown in Fig. 2.9. (It might or might not map directly onto neural structures.)

The stimulus was decomposed into a set of basic features. With the deployment of selective attention, the features of a particular object could be bound into a recognizable object with bound features in a particular location. In early Guided Search, the main addition to the standard Feature Integration story would be the idea that information about the decomposed features could be used to guide the deployment of attention. Thus, in Fig. 2.9, an intention to look for something horizontal would make use of the orientation map to deploy attention toward items showing some horizontal-ness in that map.

Approaches to Visual SearchFeature Integration Theory and Guided SearchClick to view larger

Figure 2.9 Cartoon of the cognitive architecture for early Feature Integration Theory and early Guided Search.

The architecture of the current version of Guided Search is somewhat different. The important change is that the representation that guides attention has been pulled out of the path from early vision to bound, recognized objects. This change was made when it became clear that the properties of the guiding representation were not the same as the building blocks of perception. Guidance is based on representations that do not ‘see’ the world as we experience it. This point is discussed more extensively elsewhere (p. 22) (Wolfe 2005, 2007, 2012; Wolfe and Horowitz 2004; Wolfe, Reijnen, Van Wert, and Kuzmova 2009). A single example will suffice here (Lindsey et al. 2010).

Approaches to Visual SearchFeature Integration Theory and Guided SearchClick to view larger

Figure 2.10 Search for the desaturated targets among saturated and achromatic distractors. Adapted from Lindsey, D. T., Brown, A. M., Reijnen, E., Rich, A. N., Kuzmova, Y., and Wolfe, J. M., Color Channels, not Color Appearance or Color Categories, Guide Visual Search for Desaturated Color Targets, Psychological Science, 21 (9) pp. 225–31, copyright © 2010 by SAGE Publications. Reprinted by Permission of SAGE Publications.

Suppose that your task is to search for desaturated targets. In the example in Fig. 2.10, the distractors would be fully saturated reds or blues and unsaturated whites. The targets would be pinkish on the left and pale blue on the right. This electronic or printed figure will not be colourmetrically precise, but the stimuli in the experiment were designed so that the targets lay perceptually exactly halfway between the distractors (details in Lindsey et al. 2010). This was done for a wide range of colours. Interestingly, searches for (p. 23) the pinkish (maybe ‘skin-coloured’) targets were hundreds of msec faster than searches for other desaturated colours. In perceptual space, the distance from blue to pale blue might be the same as the distance from red to pale red. However, in the guiding representation, the red–pale red difference is, apparently, much more significant.

Consequently, the current version of Guided Search adopts an architecture like that cartooned in Fig. 2.11. Between early visual processes and bound representations of objects, there is a tight bottleneck. Access to that bottleneck is gated by a guiding representation that is itself an abstraction from early vision and, as noted, does not ‘see’ the world exactly as we see it. Once an object is selected, re-entrant processes (Di Lollo, Enns, and Rensink 2000; Hochstein and Ahissar 2002) reach back to make contact with the perceptual properties of the object. Treisman’s current thinking also holds that re-entry is required for binding (Bouvier and Treisman 2010).

What are the features? (Wolfe and Horowitz 2004—revised)

A great deal of work has gone into defining the set of stimulus attributes that guide attention. Not all of this work is done by people who have adopted a Guided Search viewpoint, so in different papers, these attributes might be described as preattentive dimensions, pop-out features, etc. The goal is to determine the set of attributes that support efficient search and the guidance of attention.

Approaches to Visual SearchFeature Integration Theory and Guided SearchClick to view larger

Figure 2.11 Guided Search architecture with the ‘guiding representation’ pulled out of the main path from early vision to perception.

Wolfe and Horowitz (2004) created a list of guiding attributes. Here that list is updated in a set of five annotated tables. Tables 1–3 1.1–1.3 rank candidates into categories of the ‘undoubted’, the ‘probable and possible’, and the ‘doubtful’ attributes. In addition, Table 2.4 is reserved for ‘complicated’ attributes and Table 2.5 introduces the idea of (p. 24) (p. 25) (p. 26) (p. 27) (p. 28) preattentive properties that modulate other guiding attributes without guiding attention in their own right. The referencing is extensive but not exhaustive.

Table 2.1 The Undoubted Feature Dimensions: ‘Undoubted’ means that the status of these properties is attested by a large body of work with converging methods

The Undoubted Feature Dimensions



Bauer, Jolicoeur, & Cowan 1998; Bauer, Jolicoeur, & Cowan 1996; Brawn & Snowden 1999; Carter 1982; D’Zmura 1991; Daoutis, Pilling, & Davies 2006; Duncan 1988; Farmer & Taylor 1980; Green & Anderson 1956; Lindsey et al. 2010; Monnier & Nagy 2001; Nagy & Sanchez 1990; Nagy, Young, & Neriani 2004; Smith 1962; Treisman & Gormican 1988; Treisman & Souther 1985



Braddick & Holliday 1991; Burr, Baldassi, Morrone, & Verghese 2009; Dick, Ullman, & Sagi 1987; Horowitz, Wolfe, DiMase, and Klieger 2007; Kawahara 1993; McLeod et al. 1988; Muhlenen & Muller 1999; Nakayama & Silverman 1986; Nothdurft 1993b; Rosenholtz 2001a; Takeuchi 1997



Bergen & Julesz 1983; Cavanagh, Arguin, & Treisman 1990; Foster & Ward 1991a, 1991b; Moraglia 1989a; Sagi 1990; Wolfe & Friedman-Hill 1992; Wolfe, Friedman-Hill, Stewart, & O’Connell 1992; Wolfe, Klempen, & Shulman 1999

Size (incl. length & spatial freq.)

Cavanagh et al. 1990; Found & Muller 2001; Moraglia 1989b; D. Sagi 1988; Stuart 1993; Treisman & Gormican 1988; Verghese & Nakayama 1994; Verghese & Pelli 1994; L. G. Williams 1966



(1) . Colour is certainly a feature. The currently interesting questions have to do with what aspects of colour signals guide attention as noted in the example in Fig. 2.10.

(2) . It is possible that motion could be decomposed into separate attributes of speed and direction (Driver, McLeod, & Dienes 1992).

(3) . We are calling this dimension ‘size’ but it covers a number of properties that, again, might be treated separately. It is possible that spatial frequency should be treated as its own dimension (Bilsky & Wolfe 1995), especially considering its possible role in guidance in scenes (Oliva, Torralba, Castelhano, & Henderson 2003). See also Alvarez & Cavanagh 2008.

Table 2.2 Probable and possible feature dimensions: The items can make a reasonable case for their status as guiding attributes. However, more data would be needed to address dissenting opinions or the possibility of alternative explanations

Probable and Possible Feature Dimensions


Luminance onset (flicker)

Spalek, Kawahara, & Di Lollo 2009; Theeuwes 1995; Yantis & Jonides 1990

Luminance polarity

Gilchrist, Humphreys, & Riddoch 1996; Theeuwes & Kooi 1994


Vernier offset

Fahle 1991a, 1991b


Stereoscopic depth & tilt

Z. J. He & Nakayama 1992; Holliday & Braddick 1991; McSorley & Findlay 2001; Moore, Elsinger, & Lleras 2001; Nakayama & Silverman 1986; O’Toole & Walker 1997; Sousa, Brenner, & Smeets 2009


Pictorial depth cues

Aks & Enns 1993; Enns & Rensink 1990, 1993; Enns, Rensink, & Douglas 1990; Epstein, Babler, & Bownds 1992; Sun & Perona 1996a; Von Grünau & Dubé 1994



Bergen & Julesz 1983; Cheal & Lyon 1992; Chen 1982, 1990; Kristjánsson & Tse 2001; Pilon & Friedman 1998; Pomerantz & Pristach 1989; Treisman & Gormican 1988; Tsal, Meiran, & Lamy 1995; Wolfe & Bennett 1997


Line termination

Donnelly, Humphreys, & Riddoch 1991; Julesz & Bergen 1983; Taylor & Badcock 1988



Chen 1982; Elder & Zucker 1994, 1998; Enns 1986; Kanbe 2009; Kovacs & Julesz 1993; Treisman & Souther 1985; D. Williams & Julesz 1992


Topological status

Chen 1982, 1990, 2005; Rubin & Kanwisher 1985



Fahle 1991b; Foster & Savage 2002; Gurnsey, Humphrey, & Kapitan 1992; Sakai, Morishita, & Matsumoto 2007; Treisman & Gormican 1988; Wolfe, Yee, & Friedman-Hill 1992


Lighting direction (shading)

Adams 2008; Aks & Enns 1992; Braun 1993; Kleffner & Ramachandran 1992; Ostrovsky, Cavanagh, & Sinha 2004; Ramachandran 1988; Sun & Perona 1996a, 1996b; Symons, Cuddy, & Humphrey 2000


Glossiness (lustre)

Wolfe & Franzel 1988



Braddick & Holliday 1991; Franconeri & Simons 2003; Skarratt, Cole, & Gellatly 2009; Takeuchi 1997



Reijnen, Krummenacher, & Wolfe 2011; Taylor & Badcock 1988; Treisman & Gormican 1988


Aspect ratio

Treisman & Gormican 1988


(4) . Luminance polarity clearly supports efficient search but it might be nothing more than the black-white or luminance axis of a 3D colour space. Thus it could be grouped with colour.

(5) . The difficulty with Vernier as a guiding property in its own right is that it might be reducible to an orientation cue (Findlay 1973; Fahle & Harris 1998).

(6) . The taxonomy of depth cues as guiding attributes is not clear. Maybe it is a single, broad dimension of something like 3D layout (cf. Oliva & Torralba 2001) combining a variety of depth cues including stereopsis, the various pictorial depth cues, and shading into a representation of the 3D world. The relevant experiments would combine different guiding depth cues in a single display in order to see if they could act independently.

(7) . Depth guides attention in the sense that a ‘near’ item will pop out among far, for example. However, it also acts to modulate features like size. An item that is little in the image may be big in the world if it is far away. See ‘modulators’ in Table 5.

(8) . Rather like depth, it is not clear if ‘shape’ is one guiding dimension or many. Here we hedge our bets, listing shape and a collection of other properties that might be part of a family of shape attributes. To see the problem, consider line termination, closure, and curvature. Each supports efficient search, but are they actually independent attributes? Do an ‘O’ and a ‘C’ differ in closure or line termination or both? The issue has been complicated by the failure to settle on a generally accepted set of shape features (Kourtzi & Connor 2011; Logothetis, Pauls, & Poggio 1995; Yamane, Carlson, Bowman, Wang, & Connor 2008; Zhang et al. 2011).

(9) . The earlier evidence for guidance by shading (e.g. Ramachandran’s ‘eggs’: Ramachandran 1988) has been undermined somewhat by later work (Cavanagh 1999; Ostrovsky et al. 2004). It is possible that shading information should be grouped with other cues like stereopsis, as part of one, omnibus 3D depth property.

(10) . The evidence for shininess or gloss comes from a single experiment on binocular lustre (Wolfe & Franzel 1988). Unpublished work in our lab casts doubt on the generality of the finding.

(11) . ‘Expansion’ and/or ‘looming’ cues are somewhat problematic because they might be decomposed into a depth cue, a size cue, a motion cue, or some combination of these, though an ability to deploy attention to something that might hit you in the head seems like a good idea.

(12) . Recent evidence shows that numerosity (does this cluster contain more dots than the other clusters?) is, at best, a rather weak feature, requiring large (>3:1) ratios between target and distractor numerosities (Reijnen et al. 2011).

Table 2.3 Doubtful cases and probable non-features: These are the proposed preattentive features where the preponderance of data seems to argue against their role as guiding features. It must be acknowledged that other authors would come to different conclusions about some of these attributes. Moreover, some attributes have their status on the basis of single experiments and would benefit from further study

Doubtful cases & probable non-features



Flowers & Lohr 1985; Frith 1974; Johnston, Hawley, & Farnham 1993; Q. Wang, Cavanagh, & Green 1994; Wolfe 2001; Zhaoping & Frith 2011


Learned features (e.g. letters)

Atkinson et al. 1969; Golcu & Gilbert 2009; Grice & Canham 1990; Kinchla 1974; Kinchla & Collyer 1974; Shiffrin & Gardner 1972


Alphanumeric category

Brand 1971; Duncan 1983; Jonides & Gleitman 1972; Krueger 1984



Bergen & Adelson 1988; Bergen & Julesz 1983; Julesz 1981, 1984; Julesz & Bergen 1983; Julesz & Krose 1988; Nothdurft 1991; Wolfe & DiMase 2003


Optic flow

Braddick & Holliday 1991; Bravo 1998; Royden, Wolfe, & Klempen 2001, but see Rushton, Bradshaw, & Warren 2007

Colour change

Theeuwes 1995

3D volumes (e.g. geons)

J. M. Brown, Weisstein, & May 1992; Pilon & Friedman 1998


Correani, Scot-Samuel, & Leonards 2006


Material type

Wolfe & Myers 2010

Scene category

Greene & Wolfe 2011


Morgan, Giora, & Solomon 2008


Doi & Ueda 2007; Palanica & Itier 2011; M. A. Williams, Moss, & Bradshaw 2002; Von Grünau & Anston 1995


Eye of origin/binocular rivalry

Paffen, Hooge, Benjamins, & Hogendoorn 2011; Shneor & Hochstein 2006; Wolfe & Franzel 1988; Zhaoping 2008


Your name

Bundesen, Kyllingsbaek, Houmann, & Jensen 1997


Batty, Cave, & Pauli 2005; Lipp 2006; Notebaert, Crombez, Van Damme, De Houwer, & Theeuwes 2011; Öhman, Flykt, & Esteves 2001; Soares, Esteves, Lundqvist, & Ohman 2009; Tipples, Young, Quinlan, Broks, & Ellis 2002


Biological motion

Pratt, Radulescu, Guo, & Abrams 2010; L. Wang, Zhang, He, & Jiang 2010



(13) . There are various claims for a guiding role for novelty and/or familiarity. The phenomena seem to be rather weak. For instance, as a general rule, a basic feature will continue to guide attention in the presence of some distractor heterogeneity in an irrelevant attribute. Thus, vertical will pop out among horizontal even if all the items are of different colour. However, while a novel mirror-reversed N might pop out among Ns (Wang et al. 1994), it is not clear that it would pop out among a heterogeneous set of normal letters.

(14) . Is it possible to learn a new preattentive feature? This is a long-standing question in visual search. Much of the work involves alphanumeric characters as over-learned sets of stimuli (Caerwinski, Lightfoot, & Shiffrin 1992; Malinowski & Hübner 2001; Sigman & Gilbert 2000; Sireteanu & Rettenbach 1995). The problem is that it is very hard to tell the difference between learning a new feature and learning to better exploit existing signals (e.g. line terminations, closure, etc.). This is a case where reasonable researchers could and do disagree (Shiffrin & Schneider 1977).

(15) . It was thought that a letter might pop out among numbers and vice versa but these results (e.g. the ‘zero-oh’ effect) have been hard to replicate.

(16) . Intersection once seemed to be a good candidate for feature status but more recent results have demoted it to ‘unlikely’ status (Wolfe & DiMase 2003).

(17) . People have a strong belief that they can detect when someone is staring at them. They even believe that they can detect someone staring at them from behind them (Simons & Chabris 2010). However, while we are very good at assessing someone else’s gaze direction, especially if it is toward us (Watt, Craven, & Quinn 2007), we are probably not able to do so in a search setting and/or without attending to that person—but see Stein, Senju, Peelen, & Sterzer 2011.

(18) . Correani et al. (2006) found that luminosity did support efficient search. However, a series of control experiments showed this to be attributable to local luminance effects and not to luminosity itself.

(19) . Wolfe and Franzel (1988) had argued that eye-of-origin information and binocular rivalry signals were not available to guide search. However, more recent results suggest some sensitivity to those signals.

(20) . There is little question that threatening stimuli elicit threat-specific responses as seen, for example, in the responses of phobics to snakes or spiders (LoBue & DeLoache 2008; Rakison & Derringer 2008; Reinecke, Rinck, & Becker 2006). However, there does not appear to be a specific role for ‘threat’ in guiding search once other features are controlled (e.g. snakes have thin, curvy, and pointed attributes as well as possibly frightening ones).

(21) . Biological motion is a special stimulus (Blake 1993; Blake & Shiffrar 2007; Johansson 1973), but to date there is not a convincing demonstration that it is capable of supporting efficient visual search. It is possible that motion that implies animacy will have feature status (Gao, McCarthy, & Scholl 2010; Gao, Newman, & Scholl 2009; Gao & Scholl 2011).

Table 2.4 Complicated cases



Faces (familiar, upright, angry, real, schematic, etc.)

D. V. Becker, Anderson, Mortensen, Neufield, & Neel 2011; S. I. Becker, Horstmann, & Remington 2011; Devue, Van der Stigchel, Brèdart, & Theeuwes 2009; Doi & Ueda 2007; Eastwood, Smilek, & Merikle 2001; Frischen, Eastwood, & Smilek 2008; Von Grünau & Anston 1995; Hansen & Hansen 1988; Hershler & Hochstein 2005, 2006; Horstmann, Bergmann, Burghaus, & Becker 2010; Langton, Law, Burton, & Schweinberger 2008; Nothdurft 1993c; D. G. Purcell, Stewart, & Skov 1996; Suzuki & Cavanagh 1995; Tong & Nakayama 1999; VanRullen 2006; M. A. Williams et al. 2002)


Other semantic categories (e.g. ‘animal’)

Levin, Takarae, Miner, & Keil 2001


(22) . No candidate features have generated more controversy than the family of face features. There are many demonstrations of apparently efficient search for real faces, schematic faces, angry faces, happy faces, and so forth. There are also many papers pointing to feature confounds in these stimuli. There are good reviews in some recent articles on the topic (S. I. Becker et al. 2011; Frischen et al. 2008). In previous versions of this list, faces were placed in the unlikely category (Wolfe and Horowitz 2004) but the literature is so large and so persistent that it seems best to describe the case as ‘complicated’ and leave its resolution to the future.

Table 2.5 Modulators



Cast shadows

Rensink & Cavanagh 2004

Amodal completion

Rensink & Enns 1998; Wolfe et al. 2011

Apparent depth

Aks & Enns 1996; Champion & Warren 2008; Wheatley, Cook, & Vidyasagar 2004


(23) . The entries under this category do not appear to be preattentive features in their own right. However, they are properties that seem to be computed prior to the deployment of attention and that have an influence on other basic features. Thus, apparent depth can change the apparent size of an item and it is the apparent size, rather than the retinal or image size, that is critical in search (Aks & Enns 1996). The amodal completion of contours behind occluders can disrupt a feature. Rensink and Enns (1998) showed that the size of an element in the image could be lost when that element was tied to another element by amodal completion. On the other hand, it is possible to create oriented items that are only oriented if unoriented elements are tied together by amodal completion. Though these oriented bars are visually compelling, this orientation feature does not guide attention (Wolfe et al. 2011).

To summarize, there appear to be one to two dozen guiding attributes. Some of these are more powerful directors of attention than others. It seems clear that attributes like colour and motion guide easily and effectively while an attribute like numerosity may guide but only rather weakly. It would be wonderful if we could rank order attributes in terms of their effectiveness. However, while some direct comparison between features has been done (Nothdurft 1993a), a comprehensive hierarchy does not exist. There are hierarchies within dimensions as well. Red really does seem to be a particularly powerful guiding colour, but again, there is a vast amount we do not know, including why a feature like red should be more effective than some other colour. There is plenty of interesting speculation (Changizi, Zhang, and Shimojo 2006).

Guidance by scene-based features: The next frontier

Approaches to Visual SearchFeature Integration Theory and Guided SearchClick to view larger

Figure 2.12 Find the two people in this scene. Which one did you find first, and why?

For all the detail and complexity of the feature list already presented, it has become increasingly clear that these features are not the whole story when it comes to describing sources of guidance. This can be illustrated if you look for people in Fig. 2.12. You will not search randomly and you will rapidly find the man on the sidewalk, halfway down the street. Eye movement data indicate that you will search first in locations where people are likely to be (Ehinger, Hidalgo-Sotelo, Torralba, and Oliva 2009; Torralba, Oliva, Castelhano, and Henderson 2006). People are on horizontal surfaces. They do not generally float. In addition, you know something about the size of people. Because you can very rapidly extract the spatial layout of a scene (Greene and Oliva 2009), you should (p. 29) be able to determine if an object is a candidate human on the basis of the interaction of the size of an object in the image and its apparent depth (Sherman, Greene, and Wolfe 2011). Both of these factors may have kept you from noticing the other man, apparently very small and perched on the ledge of the first window on the right. The pixels and the rough local contrast are the same for each man (courtesy of Photoshop) but one target is far more plausible. If you found the plausible target first, it was probably scene guidance that directed your attention. While the classical features can be useful, the apparent efficiency of search in real-world scenes cannot be based on those features alone (Vickery, King, and Jiang 2005; Wolfe, Alvarez, Rosenholtz, Kuzmova, and Sherman 2011).

Our understanding of scene guidance is relatively young but it seems clear that observers make use of what can be called scene semantic guidance (forks are likely to be next to plates) and scene syntactic guidance (paintings hang on walls) (Castelhano and Heaven 2010; Henderson, Brockmole, Castelhano, and Mack 2007; Henderson and Ferreira 2004; Neider and Zelinsky 2006; Vo and Henderson 2009). Some of this guidance is probably relatively slow. After all, you cannot look for forks next to plates until you have identified the plates. However, the guidance that is based on scene structure and category can be based on information that is available very quickly from the global processing of the ‘gist’ of a scene (Fei-Fei, Iyer, Koch, and Perona 2007; Kirchner and Thorpe 2006; Oliva 2005; Sanocki and Epstein 1997). It is possible to know that you are viewing a man-made, navigable, urban scene before you have selectively attended to the various objects that make up that scene (Greene and Oliva 2009).

(p. 30) Two paths to awareness

The ability to extract some information from scenes without selective attention to objects reflects the working of a non-selective pathway from the stimulus to visual awareness (Wolfe, Vo, Evans, and Greene 2011). The capabilities of this pathway should not be overstated. You will not recognize specific objects without selective attention and binding. However, a non-selective pathway is a useful addition to the diagram in Fig. 2.11. This elaboration is shown in Fig. 2.13. Early visual processing of a scene feeds a non-selective pathway that can provide some information about spatial layout and the gist of the scene. It also feeds the Guiding Representation, here represented by colour, shape, and now gist—a scene guidance component. Finally, early vision provides the input to the selective pathway that supports object recognition. It has a selective attentional bottleneck whose selections are modulated by the Guiding Representation.

Approaches to Visual SearchFeature Integration Theory and Guided SearchClick to view larger

Figure 2.13 Guided Search architecture with the addition of a non-selective pathway. The non-selective pathway supports awareness of visual ‘stuff’ across the entire visual field in parallel. It is capable of limited semantic processing (e.g. of scene ‘gist’). It is not subject to the attentional bottleneck and it is not capable of most acts of recognition.

At any given moment, the contents of visual awareness include visual ‘stuff’ at all locations, provided by the non-selective pathway and one (or perhaps a few) bound objects, provided by the selective pathway. A more detailed account of this two-pathway architecture can be found in Wolfe et al. (2011).

(p. 31) Guidance: The Rules

The ability of a preattentive feature to guide attention is highly rule-governed. There are general rules that appear to operate over all dimensions and rules specific to a single dimension.

  1. 1. The greater the difference, along a preattentive dimension, between the target and the distractors, the more efficient the search (Duncan and Humphreys 1989). Thus, it will be easier to find vertical among 30 deg. tilted items than among 15 deg. tilted items.

  2. 2. The greater the differences between the distractors (distractor heterogeneity), the less efficient the search (Duncan and Humphreys 1989). Thus, it may be harder to find vertical among a mix of 15 and 30 deg. tilted distractors than among homogeneous 15 deg. distractors, even though the average difference between target and distractors is greater for the heterogeneous example (Rosenholtz 2001b).

  3. 3. The minimum target–distractor difference required to produce efficient search will be much greater than the just noticeable difference for those stimuli. Thus, with attentional scrutiny, it is possible to tell the difference between a vertical line and one tilted 1–2 deg. from vertical. It is not possible to search efficiently for vertical among 2 deg. distractors. Efficient search will require something more like 10 to 15 deg. differences (Foster and Ward 1991b; Foster and Westland 1998).

  4. 4. For purposes of guidance, differences are greater across categorical boundaries. It will be easier to find a steep item among shallow items than to find the steepest item among other steep items even if the angular differences are the same in the two conditions (Wolfe, Friedman-Hill, et al. 1992).

  5. 5. The detailed properties of guiding attributes need to be worked out separately for each attribute and are not necessarily predicted from conscious perception of the attribute. As noted earlier, for purposes of guidance, pink/peach/skin(?) colours seem to have special status (Lindsey et al. 2010). As another example, in orientation, one might wonder if guidance is represented in a 90 deg. or 180 deg. framework. Obviously, a simple line has the same orientation after a 180 deg. rotation but a polar object (e.g. a Christmas tree or sailboat) does not have the same appearance after a 180 deg. rotation. Visual search ignores object polarity. The biggest orientation difference is 90 deg., not 180 deg. (Wolfe et al. 1999).

  6. 6. Guidance will be stronger if the observer sees the actual guiding feature (e.g. the colour ‘red’) rather than merely the name of the feature (e.g. the word ‘red’) just prior to the appearance of the search display. This priming can be produced by a deliberate cue before the onset of the trial (Wolfe, Horowitz, Kenner, Hyle, and Vasan 2004). Similarly, finding the target on one trial effectively (p. 32) primes that feature for the next (Kristjánsson and Driver 2008; Maljkovic and Nakayama 1994).

  7. 7. It is easier to find the presence of a feature than to find its absence. This is the root of many search asymmetries (Treisman and Gormican 1988; Treisman and Souther 1985) in which the search for A among B is notably more efficient than the search for B among A. Thus, it is easier to find the presence of a moving target among stationary distractors than to find a stationary target among moving distractors (Dick et al. 1987; Royden et al. 2001; Horowitz et al. 2007). There is a great deal more to be said about asymmetries (Wolfe 2001), much of it first said by Treisman, as noted above. Rosenholtz has noted that the designs of asymmetry experiments are sometimes themselves asymmetrical (Rosenholtz 2001a) and this is worth keeping in mind when evaluating search asymmetry results.

  8. 8. Not all search asymmetries are evidence for the preattentive processing of the stimulus property under study. Sometimes it is easier to find A among B than B among A, not because A pops out, but because it is easier to reject a succession of Bs as distractors. In these cases, both A vs B and B vs A tend to produce inefficient slopes. Thus, for example, if there is a real ‘anger superiority effect’ (Hansen and Hansen 1988), it may not be that ‘angry’ pops out, but rather that angry faces hold attention, making it harder to move through them when they are the distractors. As a result, search for an angry target among easily dismissed, happy distractors is more efficient than search for a happy target among hard-to-dismiss, angry distractors, but neither of these searches will be efficient.

  9. 9. It is possible to guide by more than one feature at a time. In Guided Search, this is how relatively efficient conjunction search is accomplished. If there are several target features, you can guide your attention to all of them. Indeed, higher-order conjunctions with three or even six defining features can be easier than the classic two-feature conjunction search (Wolfe et al. 1989; Wolfe 2010). There is a debate about how this is accomplished. We have argued that it is possible to simultaneously guide to multiple features (Friedman-Hill and Wolfe 1995). Huang and Pashler argue that guidance to multiple features must be done in a series of nested steps. For example, one might select all red items and then select the vertical items within the red set (Huang and Pashler 2007, 2012).

  10. 10. It is not possible to guide to two features from the same dimension/attribute at the same time. Conjunctions of two colours or two orientations are inefficient (Wolfe et al. 1990). Even though it is easy to identify an item that is red and yellow, it is not efficient to search for that item among red/blue and blue/yellow distractors.

  11. 11. It is relatively efficient to search for a conjunction of an item of one colour with a part of another colour. (Find the red thing with a yellow part among red things with blue parts and blue things with yellow parts.) This suggests that the (p. 33) preattentive representations of objects have some part–whole structure to them (Wolfe, Friedman-Hill, and Bilsky 1994). Interestingly, search for orientation x orientation conjunctions of items with part–whole structure is not efficient. The vertical item with an oblique part is relatively hard to find amidst vertical items with horizontal parts and horizontal items with oblique parts (Bilsky and Wolfe 1995). The difference between colour and orientation could be due to different susceptibility to rotation. If you tilt your head, a red thing with a yellow part is still a red thing with a yellow part. A vertical thing with a horizontal part might not be rotationally invariant in the same way.

Guided Search 2013: How Do We Search?

Figure 2.14 elaborates on Fig. 2.13 to provide a roadmap of the steps in visual search as imagined by the 2013 incarnation of the Guided Search model. Sections below refer to the letters on the figure:

Approaches to Visual SearchFeature Integration Theory and Guided SearchClick to view larger

Figure 2.14 Guided Search 2013: A. A set of attributes, including scene-based attributes like gist, is derived from early visual processing. B. A priority map is created from a weighted average of those sources of guidance. C. The priority map controls selection of items, one after another, for binding and recognition. D. Binding and recognition are modelled as a diffusion process. Since the diffusion takes longer than the time between attentional selections, the result is an asynchronous diffusion with multiple items in process at the same time. See text for further details.

(p. 34) A. As described in the figure, a set of basic attributes is extracted from the early stages of visual processing. For each attribute, two forms of guidance are possible. Bottom-up guidance is stimulus-driven, based on local differences. Bottom-up guidance is essentially the same as ‘salience’ (Nothdurft 2000; Donk and van Zoest 2008; Lamy and Zoaris 2009). Top-down guidance is user-driven, based on what the observer’s current understanding of the task demands. Thus, returning to Fig. 2.8, if you are looking for a red T, colour guidance will be directed to red. If you are looking for a black T, guidance will be directed to black. Nothing has changed in the stimulus or in the bottom-up salience of items. It is the top-down guidance that changes in this case. When neurophysiological studies refer to attention to multiple items in parallel (e.g. enhancing neural responses of all red items), they are generally describing what we refer to as top-down guidance (Bichot, Rossi, and Desimone 2005; Treue and Trujillo 1999; Carrasco, Eckstein, Verghese, Boynton, and Treue 2009).

The top-down/bottom-up terminology is not entirely unambiguous. Consider priming effects. Exposure to red will speed subsequent search for red. We have described this as a form of implicit top-down guidance (Wolfe et al. 2004) because something about the observer—in this case, their history—has changed the guidance of attention. Response to the same stimulus would be different if the observer’s history was different. Others see priming as an automatic process that should be considered to be ‘bottom-up’ (Kristjánsson and Campana 2010).

B. Guidance by different attributes is combined into a ‘priority map’. In the first versions of Guided Search this was called an ‘activation map’. ‘Priority map’ (Serences and Yantis 2006) better captures the role of this representation, which is to prioritize items in the visual input for selective attention, binding, and recognition. In the absence of top-down guidance, a priority map built from pure bottom-up signals would be a ‘salience map’ (Koch and Ullman 1985; Itti and Koch 2000; Parkhurst, Law, and Niebur 2002). Each attribute makes a weighted contribution to the priority map. Thus, if you are looking for red vertical items, the weights on colour and orientation will be set high and other attributes will be de-emphasized. In order to guide to ‘red’, one can imagine either setting a high weight for a separate feature, ‘red’, or enhancing red within the colour module and setting a high weight for guidance by colour. See the work on ‘dimension weighting’ for more detail on this issue (Found and Muller 1996; Muller, Reimann, and Krummenacher 2003; Zehetleitner, Krummenacher, Geyer, Hegenloh, and Müller 2011).

Weights can be adjusted but not to the full extent that the user might wish. Notably, it does not seem possible to set the weight on bottom-up salience signals to zero. There has been a long-standing debate about what sorts of salient signals capture attention. Claims have been made for luminance onsets (Jonides and Yantis 1988; Yantis and Jonides 1990), new objects (Yantis 1993), and for many specific attributes (Franconeri and Simons 2003; Rauschenberger 2003; Turatto and Glafano 2000; Pratt, Radulescu, Guo, and Abrams 2010). There are generally counterclaims (Gibson and Kelsey 1998), but on balance, it seems that a highly salient signal from an irrelevant attribute will have some influence on the course of search, regardless of the desires of the searcher.

(p. 35) Some researchers are not fond of the idea of a priority map (Chan and Hayward 2009; Huang and Pashler 2012). However, while the details vary, some version of such a map is a part of many models of search and has been the subject of much neurophysiological investigation (Bisley and Goldberg 2010; Fecteau and Munoz 2006; Gottlieb, Balan, Oristaglio, and Schneider 2009; Li 2002; Thompson and Bichot 2004).

C. The priority map is so named because its role is to prioritize the selection of items. Since Koch and Ullman (1985), it has been proposed that something like a winner-take-all operation selects the next item for attention. This is straightforward for the first selection in an image but what about subsequent selections? The critical question is whether attention ever revisits the same location/item during the course of a search. The original assumption was that items were not revisited. This was the assumption of Feature Integration and the first versions of Guided Search. The phenomenon of ‘Inhibition of Return’ (IOR) seemed to provide a mechanism to prevent resampling from the display (Klein 1988; Posner 1980) since IOR showed that it was harder to get attention back to a previously attended item than to direct attention to a previously unattended item. However, subsequent results suggested that IOR probably only marked the most recently attended items (Abrams and Pratt 1996; Pratt and Abrams 1995; Tipper, Weaver, and Watson 1996) making it hard to see how this mechanism could prevent revisitations once the set size becomes large.

Horowitz and Wolfe did a series of experiments designed to directly test if items were being sampled with or without replacement during visual search and came to the conclusion that ‘visual search has no memory’ (Horowitz and Wolfe 1998, 2003)—at least, no memory for the deployments of covert attention. Their data were consistent with sampling with replacement, giving no role to IOR or other means of preventing revisitation. Other evidence suggested that this claim might be too strong (Peterson, Kramer, Wang, Irwin, and McCarley 2001; Shore and Klein 2000; Takeda 2004). Over the short timescale required for many standard laboratory search tasks, it seems most likely that neither extreme position is correct. Items are not sampled entirely without regard to the prior history of search but neither are they inhibited in a manner that can prevent some resampling. In Klein’s words, inhibition of return appears to be a ‘foraging facilitator’ (Klein and MacInnes 1999), one of several mechanisms that bias attention toward new items during search (Klein 2009).

When search is more prolonged, as it is in many real-world tasks, strategic plans can play a role in preventing revisiting. Reading is a simple example. If you search this page of text for the word ‘covert’, you will most likely start at the top left and read the page in a manner that eliminates most resampling from the display without requiring specific inhibition of or memory for rejected items. Your prospective plan of search serves the role that memory would serve (McDaniel, Robinson-Riegler, and Einstein 1998). Similar factors are probably at work in many real-world searches (Hollingworth 2009; Hollingworth and Henderson 2002).

D. In the 2013 version of Guided Search, the recognition/classification of an object is modelled as a diffusion process (Ratcliff 1978). While we have used a Ratcliff-style diffuser, there is, at present, no reason to choose between any particular member of the (p. 36) class of models in which information accumulates toward a threshold over time (Brown and Heathcote 2008; Donkin, Brown, Heathcote, and Wagenmakers 2011; Purcell et al. 2010). The unique aspect of the Guided Search version is that it proposes an asynchronous diffusion process. That is, items are selected one at a time and begin diffusing toward a target or distractor boundary once they are selected. Since the rate of selection (say, 20–40 Hz) is faster than the time required to identify an item (say, 150–300 msec), multiple items are diffusing at the same time. Metaphorically, this can be seen as a ‘pipeline’ or a ‘carwash’ (Moore and Wolfe 2001; Wolfe 2003), albeit a carwash in which one car could enter second and leave first.

This architecture is a hybrid of serial and parallel processing (E. Cohen and Ruppin 1999; Herd and O’Reilly 2005; Thornton and Gilden 2007; Townsend and Wenger 2004; Verghese 2001). Selection is imagined to be strictly serial though nothing very dramatic would change if a small number of items could be selected at one time. Diffusion is parallel in the sense that multiple items are undergoing the process of binding and recognition at the same time.

Diffusion models produce the positively skewed reaction time distributions that are characteristic of visual search (E. M. Palmer, Horowitz, Torralba, and Wolfe 2011; Wolfe, Palmer, and Horowitz 2010). Errors are produced when a distractor reaches the target boundary or a target reaches the distractor boundary. An error could also be generated if the search was terminated with a guess (see next section). Changes in the parameters of the diffuser can be used to model various effects seen in the data. Thus, if the boundaries are brought closer together, the error rate will rise and the RTs will decline; a speed-accuracy trade-off. False alarms and miss errors can be traded off against each other by changing the starting point of the diffuser.

When do we stop?

The architecture of Fig. 2.14 makes the process of finding a target reasonably clear. With some preattentive guidance, items are selected into the diffuser and search ends when one of those items goes over the target boundary. But what happens if there is no target or if no item reaches the target boundary or if there are an unknown number of targets in the display? At some point, search must end. When is it time to quit? Feature Integration assumed that observers would quit when all items had been examined on target absent trials (or almost all, with miss errors produced when observers quit without checking the target). Early versions of Guided Search argued that observers searched through all items that received guiding activation above some threshold. Neither of these accounts works well if distractors are not perfectly tracked during search. Moreover, in real scenes, it is completely unclear what the ‘set size’ might be or what it would mean to attend to all items (Wolfe, Alvarez, et al. 2011).

A different approach adds another diffuser to the model. In this diffuser, a signal accumulates over time and the trial is terminated when that signal crosses a quitting threshold. The threshold is set dynamically, based on the observer’s experience. After (p. 37) quitting correctly, the quitting threshold moves down, causing the observer to quit more quickly. After an error, it rises and the observer becomes more cautious about quitting. This adjustment can be observed in the pattern of RTs in search experiments (Chun and Wolfe 1996; Ishibashi, Kita, and Wolfe 2012).

Experiments that manipulate target prevalence are useful in constraining models of quitting behaviour in search (Colquhoun and Baddeley 1967; Fleck and Mitroff 2007; Wolfe, Horowitz, and Kenner 2005; Wolfe et al. 2007; Wolfe and VanWert 2010). When targets are rare, miss errors rise, false alarms fall, and RTs become shorter. If targets are common, miss errors fall, false alarms rise, and RTs become longer. To see this full pattern of behaviour, it is important to use stimuli that are ambiguous enough to produce false-alarm errors. Classic search experiments (find the T among Ls, etc.) tend not to produce false alarms. In contrast, real-world tasks like breast cancer screening (Wolfe, Birdwell, and Evans 2011) or airport baggage screening are characterized by ambiguous stimuli and very low prevalence. Wolfe and VanWert (2010) found that the effects of prevalence could be modelled as a change in criterion (which would be represented by a change in the starting point of the diffuser in Fig. 2.14) and a concurrent change in a quitting threshold. At low prevalence, the starting point moves toward the distractor bound, making miss errors more common, and the quitting threshold drops, making RTs shorter (and also increasing miss errors, if one assumes an ‘absent’ response when the quitting threshold is reached). Models that adjust only the starting point or only the quitting threshold, fail to capture the pattern of the data. It should be noted that search termination remains a complex and underinvestigated topic.

What We Didn’t Discuss

Having discussed search termination, it is time to consider chapter termination. Before ending, it is worth noting that there are a number of important topics that have been largely omitted here. Some of these will be discussed elsewhere in this volume. Any complete account of search would include some treatment of:

Eye movements: What is the relationship between covert deployments of attention and overt deployments of the eyes (Hwang, Wang, and Pomplun 2011; Kowler 2011; Malcolm and Henderson 2010; Neider, Boot, and Kramer 2010).

The psychophysics of simple searches: There is an important body of work on the fine-grained details of simple searches. The tasks used in these studies permit a degree of control that is not common in standard search tasks and not possible in real-world search tasks (Najemnik and Geisler 2005; J. Palmer, Verghese, and Pavel, 2000; Cameron, Tai, Eckstein, and Carrasco 2004; Dosher, Han, and Lu 2010; Baldassi and Verghese 2002).

Memory in repeated search: What happens when the same scene is searched more than once? Contextual cueing shows learning (Brockmole and Henderson 2006; Chun and (p. 38) Jiang 1998; Kunar, Flusberg, Horowitz, and Wolfe 2007). Repeated searches through simple, laboratory-style search displays do not produce an improvement in search efficiency (Wolfe, Klempen, and Dahlen 2000) but there is learning in repeated search through real scenes (Hollingworth 2009; Hollingworth and Henderson 2002; Vo and Wolfe 2012).

The neural basis of search: What might be the neural locus and operation of a priority map (Serences and Yantis 2006; Shipp 2004; Bisley and Goldberg 2010) or a diffuser (Ratcliff, Philiastides, and Sajda 2009) or a serial selection process (Buschman and Miller 2009; Chelazzi 1999) or set size effects (J. Y. Cohen, Heitz, Woodman, and Schall 2009)?

Of course, this is merely a sampling of the topics that have been studied from a neural perspective and a sampling of the topics, important to search, that have been omitted from this chapter.


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