- Email: [email protected]
The pertinence of research on visual search to radiologic practice
Evidence of Innovation Steven E. Seltzer, MD, Editor
The Pertinence of Research on Visual Search to Radiologic Practice Jeremy M. Wolfe, PhD
isual search is a topic of interest to academic vision scientists and to radiologists. However, it is a long w a y from the vision research lab to the visual tasks faced daily by radiologists. Visual search tasks are performed in both venues, but stimuli are different. In the lab, subjects generally view an image of a set of discrete items placed on a h o m o g e n e o u s background. Radiological images are continuous. In the lab, feedback about performance is usually presented after each trial, and subjects k n o w that targets will a p p e a r on, say, 50% of trials. Radiology provides no such assurance. Finally, in the lab, subjects are asked to respond as quickly and as accurately as possible" with error rates of 5-10% considered tolerable. There is no preference for false-positive errors over misses. The constraints in radiology are different [1]. With that preamble, is there any reason b e y o n d simple curiosity for radiologists to be interested in the experiments and conclusions of scientists w h o study this artificial form of visual search? In this article, I will describe a few of the conclusions about visual search that have b e e n reached in the laboratory. It is for others to decide if they have application to the specific problems facing radiologists. I will illustrate with sample stimuli rather than with an extensive presentation of the data. The data backing these various assertions have b e e n published elsewhere as noted in the references [reviewed in 2]. Most of the experiments to be discussed have the general form s h o w n in Figure 1. Subjects are s h o w n a set of items on a computer screen and are asked to determine if some target is or is
V
A Stimulus
IIii
II
j
Target Present? Yes or no? FIGURE 1, The basic laboratoryvisual search task
not present. Their accuracy and response times (RT) are measured. Feedback about accuracy is given to the subject, and the trial sequence is repeated several hundred times, with the locations of items and the presence or absence of a target randomly determined for each trial. The n u m b e r of items (set size) is the independent variable. In our experiments, data from about 10 subjects is then averaged and RT is examined as a function of set size. If RT does not vary with set size (the fiat, lower functions in Fig. 2), we can conclude the subject could process all items at once ("in parallel"). If RT increases linearly with set size and the "no" slope is twice the "yes" slope, w e suspect a "serial," item-by-item search that ends w h e n the subject finds a target or w h e n all
From the Center for Ophthalmic Research, Brigham & Women's Hospital and Harvard Medical School, Boston, MA. Address reprint requests to J. M. Wolfe, PhD, Center for Ophthalmic Research, Brigham & Women's Hospital, 221 Longwood Ave., Boston, MA 02115. Received April 22, 1994, and accepted for publication after revision July 14, 1994.
Acad Radio11995;2:74-78 © 1994, Association of University Radiologists
74
Vol. 2, No. 1, January 1995
WOLFE
Results Calculate .., ~To- serial RT x Set Size ..°." slopes Yes - serial OOO 00
...."
The set of basic features includes most of the obvious properties, such as motion and stereoscopic depth. However, some more complex properties work, including shininess and pictorial depth cues such as shading and linear perspective [4]. The set of features that produce target trial slopes near 0 msec/item is extensively reviewed in Wolfe [2].
°0O0
No - parallel Yes - parallel Set Size (# of items) FIGURE 2. RT x set size functions
candidate targets have b e e n rejected. A serial search generally has a slope of 20-30 msec/item for yes trials and 40-60 for no trials. Since only half of the items must be examined on average for yes trials, this suggests that 20-25 simple items can be examined each second [3]. Most, but not all, laboratory search tasks produce results on the continuum b e t w e e n these parallel and serial endpoints. Using experiments of this sort, a n u m b e r of conclusions have b e e n reached: 1. A set of basic f e a t u r e s c a n be p r o c e s s e d in parallel.
When one item differs from a h o m o g e n e o u s set of distractors, it is easy to find if it differs in a basic property such as color, orientation, or size (Fig. 3).
' I I 'S"i l'l " I I ' II I
This parallel processing of multiple items does not work w h e n targets and distractors share all their basic features. In Figure 4, the search is for a 2 a m o n g 5s (S a m o n g mirror-reversed Ss). The identification of any one item is trivial, but items must be identified one after another until the target is found. The RT × set size functions for a search of this sort would be in the serial range of 20-30 msec/item for target-present trials and twice that for target-absent [3]. 3. If t h e items are hard to find, s e a r c h is very inefficient.
Even though none of the parallel processes can differentiate between targets and distractors in the 2 versus 5 example, at least they can locate all the items. This parallel preprocessing produces a set of locations that can be checked in series. If the items are embedded in the right sort of continuous background, even that source of parallel help is lost and search becomes a slow hunt for the items. In Figure 5, making the Ss part of a network of "rivers" slows search to 102 msec/item for target trials and 206 msec/item for blank trials [6].
5s5 #ss
I
||
IOIll
2. If there is no basic feature i n f o r m a t i o n , search is serial.
"!
FIGURE 3. In a feature search, number of items makes little difference in time needed to find a target.
5 5 # 5 5 5 5 5 5 55 S 5 5 5 55 5 555555 5 5 5 5 5 5 55 FIGURE 4. In a serial, self-terminating search, each additional distracting item adds to average time needed to find a target.
75
PERTINENCE OF VISUAL SEARCH RESEARCH
Target=
5
Distractors=
~
Vol. 2, No. 1, January 1995
5. If distractors differ from each other, search can be inefficient.
etc
If w e introduce noise by varying the b a c k g r o u n d items, the signal detection task b e c o m e s more onerous, and it can be difficult to distinguish even large targetdistractor differences. In both stimuli in Figure 6, the vertical target is separated from the closest distractor by 30 ° . Search is harder in the left stimulus because distractors are heterogeneous. Data for a similar experiment p r o d u c e d serial slopes of about 20 msec/item for target trials and 40 msec/item for blank trials [11]. A general rule seems to be that search can be efficient only if the target is categorically unique. In orientation, categories s e e m to be words such as "steep," "shallow," "left," and "right" [11]. In the left stimulus of Figure 6, the vertical target is difficult to find because it is not uniquely steep but merely steeper than the other steep lines.
FIGURE 5. Search is difficult if one cannot find the items.
This is the basis for camouflage and a possible source of difficulty in some radiological images [7].
4. If feature difference is small, search will be inefficient. Even searches for basic features can be difficuh. If the difference between target and distractor is small enough, a subject will need to scrutinize each item. Target-distractor differences must be surprisingly large to support efficient search. For example, with attention, subjects can discriminate lines that differ by 1° or 2 ° in orientation. Parallel visual search requires differences of 10-15 ° [8]. This can be understood as a complicated signal detection problem. In a standard signal detection task, subjects might be asked if the orientation of a line was different from a standard. Lines that differ have the "signal" of that orientation information plus the "noise" inherent in our perception of orientation. Lines that match the standard have only the noise. A visual search task can be seen as multiple signal detection tasks---one per item. Small differences between target and distractors are small signals for the visual system to try to detect. They,,can get lost in the background noise [9, 10].
6. Attention can be guided to a target by multiple cues. Truly efficient visual searches w e r e once thought to be only those with a target defined by a single basic feature. A target defined by a conjunction of two features was thought to require full serial search [3]. Subsequent research has shown that efficient conjunction search is possible with target slopes for m a n y conjunctions in the range of 5-10 msec/item [12-15]. My laboratory's Guided Search model [2] explains efficient conjunction search as an act of cooperation b e t w e e n parallel feature processors that can handle only one feature at a time and a serial, focal attention process that can handle only one item at a time. In Figure 7, the target is a black horizontal line. It is neither uniquely black nor uniquely horizontal. However, focal attention can be guided to likely target locations by parallel feature processors earlier in the visual pathway. A color processor can guide attention toward black
15/ III / / !/ I / /I/// I I
--II
",'
_.SS
S
_
, ,,;;;;" I I I
76
,
I
I t/
FIGURE 6. In both parts of the figure, target is vertical and the closest distractor orientations are 30 ° from vertical. Heterogeneous distractors on the left make search more difficult.
WOLFE
Vol. 2, No. 1, J a n u a r y 1995
!
I I
|
I
I
,
I
I
i
I
I,
''
I
i
I
I
i
I I
5
I= i
i i
i I
I
i
I
i
i
I
I I
I I
,
i i
I
i
i
i
I
!
I
!
II I ''I
F I G U R E 7. Conjunction of color and orientation. Find the black horizontal bar.
FIGURE 8. It is easier to find a white item with a black part (top half of figure) than to find an item with black and white parts (bottom).
items, and an orientation processor can guide attention toward horizontal (or shallow) items. The combination of these two sources of parallel guidance increases the chance that attention will be deployed efficiently to a black vertical item.
world problem such as search of radiological images? An obvious problem is that all of the preceding examples (except for the camouflage example) involve isolated items on a blank background--hardly a model of an Xray. However, we have shown that many of these rules apply to a class of continuous images [6], holding out the possibility that they apply more generally to real-world stimuli. Another difference is that laboratory search tasks are designed to minimize the impact of eye movements. Control of eye movements is greatly important when scanning radiologic images [18]. Searches in radiological images pose a particularly difficult problem because they seem to be searches for weak signals in noisy displays. If they were not, if potential tumors "popped out" in parallel search, radiologists could devote their full energies to interpretation without having to worry about detection. Thus, many of the rules for easy feature searches seem likely, if sadly, to be of little application. However, some of the other rules may be more relevant. For instance, the ability to segment an image into searchable "items" (compare Figs. 4 and 5) makes a large difference in search efficiency. Any steps that could promote a useful segmentation of the image would assumingly make the search task easier. Indeed, given an understanding of the constraints on human search performance, it may be possible to design imaging systems that make better use of human abilities. If a computer system could recode a complex property into a basic feature for display purposes (e.g., marking all items of some complex shape with a color), possibly a guided search collabora-
7. The parallel stage knows something about the structure of items, In studying conjunctions, we found that searching for conjunctions of two different types of features (color x orientation, size x curvature, etc.) was relatively easy but that searching for conjunctions of two instances of the same type of feature (color x color, size x size) was difficult [16]. An important exception is shown in Figure 8. In the top half of the figure, please search for the white square with a black part. Here, where the two regions are in a hierarchical, part-whole relationship, search is quite easy. Search is more difficult in the lower figure, where the target is an item with black and white parts. Apparently, parallel processing is capable of a crude division of items into components at different scales. A color version of this experiment produced average slopes of 15 msec/item for target trials and 30 msec/item for blank trials for the part-whole case and 30 and 65 msec/item, respectively, for the comparable part-part case [17].
CONCLUSIONS Laboratory work with visual search tasks has revealed that attending to a desired target is a rule-governed behavior. Do these rules have any relevance to a real-
77
PERTINENCE OF VISUAL SEARCH RESEARCH
tion between human and machine could search images better than either could alone. ACKNOWLEDGMENTS
This research is supported by NIH-NEI grant RO1EY05087 and by AFOSR grant F49620-93-1-0407. I thank Patricia O'Neill, Alexander Bilsky, Greg Gancarz, and Sara Bennett for comments on various drafts of this article. REFERENCES 1. Giger ML, Doi K, Yin FF, et al. Computer-vision schemes for lung and breast cancer detection. In: Brogan, Gale, Carr, eds. Visual Search 2, London, UK: Taylor & Francis, 1993;225-230. 2. Wolfe JM. Guided Search 2.0: A revised model of visual search. Psychonomic Bull Rev 1994; 1:202-238. 3. Treieman A, Gelade G. A feature-integration theory of attention. Cognit Psychol 1980; 12:97-136. 4. Enns JT. Sensitivity of early human vision to 3-D orientation in line-drawings. Can J Psycho11992;46:143-169. 5. Kwak H, Dagenbach D, Egeth H. Further evidence for a time-independent shift of the focus of attention. PerceptPsychophys1991;49:473-480. 6. Wolfe JM. Visual search in continuous, naturalistic stimuli. Vision Res 1994;34:1187-1195.
vol. 2, No. 1, January 1995 7. Nodine CF, Krupinski EA, Kundel HL, Visual processing and decision making in search and recognition of targets embedded in pictorial scenes. In: Brogan, Gale, Carr, eds. VisualSearch2, London, UK: Taylor & Francis, 1993;239-249. 8. Foster DH, Ward PA. Asymmetries in oriented-line detection indicate two orthogonal filters in early vision. Proc R Soc Lond B Biol Sci 1991 ;243:75-81. 9. Swensson RG, Judy PIE Detection of noisy visual targets: Models for the effects of spatial uncertainty and signal-to-noise ratio. Percept Psychophys 1981 ;29:521-534. 10. Swensson RG. A two-stage detection model applied to skilled visual search by radiologists. Percept Psychophys 1980;27:11-16. 11. Wolfe JM, Friedman-Hill SR, Stewart MI, O'Connell KM. The role of categorization in visual search for orientation. J Exp Psychol Hum Percept Perform 1992;18: 34-49. 12. Nakayama K, Silverman GH. Serial and parallel processing of visual feature conjunctions. Nature 1986;320:264-265. 13. McLeod P, Driver J, Crisp J. Visual search for conjunctions of movement and form is parallel. Nature 1988;332:154-155. 14. Wolfe JM, Cave KR, Franzel SL. Guided Search: An alternative to the Feature Integration model for visual search. J Exp Psychol Hum Percept Perform 1989; 15:419-433. 15. Treisman A, Sato S. Conjunction search revisited. J Exp Psychol Hum Percept Perform 1990;16:459-478. 16. Wolfe JM, Yu KP, Pruszenski AD, Treue F, Cave KR. Limits on guidance of visual attention by parallel feature processes. Invest Ophthalmol Vis Sci 1989;30(suppl):159. 17. Wolfe JM, Friedman-Hill SR, BilskyAB. Parallel processing of part/whole information in visual search tasks. Percept Psychophys 1994;55:537-550. 18. Kundel HL. Search for lung nodules: The guidance of visual scanning. Invest Radio11991;266:777-787.
The Pertinence of Research on Visual Search to Radiologic Practice Jeremy M. Wolfe, PhD
isual search is a topic of interest to academic vision scientists and to radiologists. However, it is a long w a y from the vision research lab to the visual tasks faced daily by radiologists. Visual search tasks are performed in both venues, but stimuli are different. In the lab, subjects generally view an image of a set of discrete items placed on a h o m o g e n e o u s background. Radiological images are continuous. In the lab, feedback about performance is usually presented after each trial, and subjects k n o w that targets will a p p e a r on, say, 50% of trials. Radiology provides no such assurance. Finally, in the lab, subjects are asked to respond as quickly and as accurately as possible" with error rates of 5-10% considered tolerable. There is no preference for false-positive errors over misses. The constraints in radiology are different [1]. With that preamble, is there any reason b e y o n d simple curiosity for radiologists to be interested in the experiments and conclusions of scientists w h o study this artificial form of visual search? In this article, I will describe a few of the conclusions about visual search that have b e e n reached in the laboratory. It is for others to decide if they have application to the specific problems facing radiologists. I will illustrate with sample stimuli rather than with an extensive presentation of the data. The data backing these various assertions have b e e n published elsewhere as noted in the references [reviewed in 2]. Most of the experiments to be discussed have the general form s h o w n in Figure 1. Subjects are s h o w n a set of items on a computer screen and are asked to determine if some target is or is
V
A Stimulus
IIii
II
j
Target Present? Yes or no? FIGURE 1, The basic laboratoryvisual search task
not present. Their accuracy and response times (RT) are measured. Feedback about accuracy is given to the subject, and the trial sequence is repeated several hundred times, with the locations of items and the presence or absence of a target randomly determined for each trial. The n u m b e r of items (set size) is the independent variable. In our experiments, data from about 10 subjects is then averaged and RT is examined as a function of set size. If RT does not vary with set size (the fiat, lower functions in Fig. 2), we can conclude the subject could process all items at once ("in parallel"). If RT increases linearly with set size and the "no" slope is twice the "yes" slope, w e suspect a "serial," item-by-item search that ends w h e n the subject finds a target or w h e n all
From the Center for Ophthalmic Research, Brigham & Women's Hospital and Harvard Medical School, Boston, MA. Address reprint requests to J. M. Wolfe, PhD, Center for Ophthalmic Research, Brigham & Women's Hospital, 221 Longwood Ave., Boston, MA 02115. Received April 22, 1994, and accepted for publication after revision July 14, 1994.
Acad Radio11995;2:74-78 © 1994, Association of University Radiologists
74
Vol. 2, No. 1, January 1995
WOLFE
Results Calculate .., ~To- serial RT x Set Size ..°." slopes Yes - serial OOO 00
...."
The set of basic features includes most of the obvious properties, such as motion and stereoscopic depth. However, some more complex properties work, including shininess and pictorial depth cues such as shading and linear perspective [4]. The set of features that produce target trial slopes near 0 msec/item is extensively reviewed in Wolfe [2].
°0O0
No - parallel Yes - parallel Set Size (# of items) FIGURE 2. RT x set size functions
candidate targets have b e e n rejected. A serial search generally has a slope of 20-30 msec/item for yes trials and 40-60 for no trials. Since only half of the items must be examined on average for yes trials, this suggests that 20-25 simple items can be examined each second [3]. Most, but not all, laboratory search tasks produce results on the continuum b e t w e e n these parallel and serial endpoints. Using experiments of this sort, a n u m b e r of conclusions have b e e n reached: 1. A set of basic f e a t u r e s c a n be p r o c e s s e d in parallel.
When one item differs from a h o m o g e n e o u s set of distractors, it is easy to find if it differs in a basic property such as color, orientation, or size (Fig. 3).
' I I 'S"i l'l " I I ' II I
This parallel processing of multiple items does not work w h e n targets and distractors share all their basic features. In Figure 4, the search is for a 2 a m o n g 5s (S a m o n g mirror-reversed Ss). The identification of any one item is trivial, but items must be identified one after another until the target is found. The RT × set size functions for a search of this sort would be in the serial range of 20-30 msec/item for target-present trials and twice that for target-absent [3]. 3. If t h e items are hard to find, s e a r c h is very inefficient.
Even though none of the parallel processes can differentiate between targets and distractors in the 2 versus 5 example, at least they can locate all the items. This parallel preprocessing produces a set of locations that can be checked in series. If the items are embedded in the right sort of continuous background, even that source of parallel help is lost and search becomes a slow hunt for the items. In Figure 5, making the Ss part of a network of "rivers" slows search to 102 msec/item for target trials and 206 msec/item for blank trials [6].
5s5 #ss
I
||
IOIll
2. If there is no basic feature i n f o r m a t i o n , search is serial.
"!
FIGURE 3. In a feature search, number of items makes little difference in time needed to find a target.
5 5 # 5 5 5 5 5 5 55 S 5 5 5 55 5 555555 5 5 5 5 5 5 55 FIGURE 4. In a serial, self-terminating search, each additional distracting item adds to average time needed to find a target.
75
PERTINENCE OF VISUAL SEARCH RESEARCH
Target=
5
Distractors=
~
Vol. 2, No. 1, January 1995
5. If distractors differ from each other, search can be inefficient.
etc
If w e introduce noise by varying the b a c k g r o u n d items, the signal detection task b e c o m e s more onerous, and it can be difficult to distinguish even large targetdistractor differences. In both stimuli in Figure 6, the vertical target is separated from the closest distractor by 30 ° . Search is harder in the left stimulus because distractors are heterogeneous. Data for a similar experiment p r o d u c e d serial slopes of about 20 msec/item for target trials and 40 msec/item for blank trials [11]. A general rule seems to be that search can be efficient only if the target is categorically unique. In orientation, categories s e e m to be words such as "steep," "shallow," "left," and "right" [11]. In the left stimulus of Figure 6, the vertical target is difficult to find because it is not uniquely steep but merely steeper than the other steep lines.
FIGURE 5. Search is difficult if one cannot find the items.
This is the basis for camouflage and a possible source of difficulty in some radiological images [7].
4. If feature difference is small, search will be inefficient. Even searches for basic features can be difficuh. If the difference between target and distractor is small enough, a subject will need to scrutinize each item. Target-distractor differences must be surprisingly large to support efficient search. For example, with attention, subjects can discriminate lines that differ by 1° or 2 ° in orientation. Parallel visual search requires differences of 10-15 ° [8]. This can be understood as a complicated signal detection problem. In a standard signal detection task, subjects might be asked if the orientation of a line was different from a standard. Lines that differ have the "signal" of that orientation information plus the "noise" inherent in our perception of orientation. Lines that match the standard have only the noise. A visual search task can be seen as multiple signal detection tasks---one per item. Small differences between target and distractors are small signals for the visual system to try to detect. They,,can get lost in the background noise [9, 10].
6. Attention can be guided to a target by multiple cues. Truly efficient visual searches w e r e once thought to be only those with a target defined by a single basic feature. A target defined by a conjunction of two features was thought to require full serial search [3]. Subsequent research has shown that efficient conjunction search is possible with target slopes for m a n y conjunctions in the range of 5-10 msec/item [12-15]. My laboratory's Guided Search model [2] explains efficient conjunction search as an act of cooperation b e t w e e n parallel feature processors that can handle only one feature at a time and a serial, focal attention process that can handle only one item at a time. In Figure 7, the target is a black horizontal line. It is neither uniquely black nor uniquely horizontal. However, focal attention can be guided to likely target locations by parallel feature processors earlier in the visual pathway. A color processor can guide attention toward black
15/ III / / !/ I / /I/// I I
--II
",'
_.SS
S
_
, ,,;;;;" I I I
76
,
I
I t/
FIGURE 6. In both parts of the figure, target is vertical and the closest distractor orientations are 30 ° from vertical. Heterogeneous distractors on the left make search more difficult.
WOLFE
Vol. 2, No. 1, J a n u a r y 1995
!
I I
|
I
I
,
I
I
i
I
I,
''
I
i
I
I
i
I I
5
I= i
i i
i I
I
i
I
i
i
I
I I
I I
,
i i
I
i
i
i
I
!
I
!
II I ''I
F I G U R E 7. Conjunction of color and orientation. Find the black horizontal bar.
FIGURE 8. It is easier to find a white item with a black part (top half of figure) than to find an item with black and white parts (bottom).
items, and an orientation processor can guide attention toward horizontal (or shallow) items. The combination of these two sources of parallel guidance increases the chance that attention will be deployed efficiently to a black vertical item.
world problem such as search of radiological images? An obvious problem is that all of the preceding examples (except for the camouflage example) involve isolated items on a blank background--hardly a model of an Xray. However, we have shown that many of these rules apply to a class of continuous images [6], holding out the possibility that they apply more generally to real-world stimuli. Another difference is that laboratory search tasks are designed to minimize the impact of eye movements. Control of eye movements is greatly important when scanning radiologic images [18]. Searches in radiological images pose a particularly difficult problem because they seem to be searches for weak signals in noisy displays. If they were not, if potential tumors "popped out" in parallel search, radiologists could devote their full energies to interpretation without having to worry about detection. Thus, many of the rules for easy feature searches seem likely, if sadly, to be of little application. However, some of the other rules may be more relevant. For instance, the ability to segment an image into searchable "items" (compare Figs. 4 and 5) makes a large difference in search efficiency. Any steps that could promote a useful segmentation of the image would assumingly make the search task easier. Indeed, given an understanding of the constraints on human search performance, it may be possible to design imaging systems that make better use of human abilities. If a computer system could recode a complex property into a basic feature for display purposes (e.g., marking all items of some complex shape with a color), possibly a guided search collabora-
7. The parallel stage knows something about the structure of items, In studying conjunctions, we found that searching for conjunctions of two different types of features (color x orientation, size x curvature, etc.) was relatively easy but that searching for conjunctions of two instances of the same type of feature (color x color, size x size) was difficult [16]. An important exception is shown in Figure 8. In the top half of the figure, please search for the white square with a black part. Here, where the two regions are in a hierarchical, part-whole relationship, search is quite easy. Search is more difficult in the lower figure, where the target is an item with black and white parts. Apparently, parallel processing is capable of a crude division of items into components at different scales. A color version of this experiment produced average slopes of 15 msec/item for target trials and 30 msec/item for blank trials for the part-whole case and 30 and 65 msec/item, respectively, for the comparable part-part case [17].
CONCLUSIONS Laboratory work with visual search tasks has revealed that attending to a desired target is a rule-governed behavior. Do these rules have any relevance to a real-
77
PERTINENCE OF VISUAL SEARCH RESEARCH
tion between human and machine could search images better than either could alone. ACKNOWLEDGMENTS
This research is supported by NIH-NEI grant RO1EY05087 and by AFOSR grant F49620-93-1-0407. I thank Patricia O'Neill, Alexander Bilsky, Greg Gancarz, and Sara Bennett for comments on various drafts of this article. REFERENCES 1. Giger ML, Doi K, Yin FF, et al. Computer-vision schemes for lung and breast cancer detection. In: Brogan, Gale, Carr, eds. Visual Search 2, London, UK: Taylor & Francis, 1993;225-230. 2. Wolfe JM. Guided Search 2.0: A revised model of visual search. Psychonomic Bull Rev 1994; 1:202-238. 3. Treieman A, Gelade G. A feature-integration theory of attention. Cognit Psychol 1980; 12:97-136. 4. Enns JT. Sensitivity of early human vision to 3-D orientation in line-drawings. Can J Psycho11992;46:143-169. 5. Kwak H, Dagenbach D, Egeth H. Further evidence for a time-independent shift of the focus of attention. PerceptPsychophys1991;49:473-480. 6. Wolfe JM. Visual search in continuous, naturalistic stimuli. Vision Res 1994;34:1187-1195.
vol. 2, No. 1, January 1995 7. Nodine CF, Krupinski EA, Kundel HL, Visual processing and decision making in search and recognition of targets embedded in pictorial scenes. In: Brogan, Gale, Carr, eds. VisualSearch2, London, UK: Taylor & Francis, 1993;239-249. 8. Foster DH, Ward PA. Asymmetries in oriented-line detection indicate two orthogonal filters in early vision. Proc R Soc Lond B Biol Sci 1991 ;243:75-81. 9. Swensson RG, Judy PIE Detection of noisy visual targets: Models for the effects of spatial uncertainty and signal-to-noise ratio. Percept Psychophys 1981 ;29:521-534. 10. Swensson RG. A two-stage detection model applied to skilled visual search by radiologists. Percept Psychophys 1980;27:11-16. 11. Wolfe JM, Friedman-Hill SR, Stewart MI, O'Connell KM. The role of categorization in visual search for orientation. J Exp Psychol Hum Percept Perform 1992;18: 34-49. 12. Nakayama K, Silverman GH. Serial and parallel processing of visual feature conjunctions. Nature 1986;320:264-265. 13. McLeod P, Driver J, Crisp J. Visual search for conjunctions of movement and form is parallel. Nature 1988;332:154-155. 14. Wolfe JM, Cave KR, Franzel SL. Guided Search: An alternative to the Feature Integration model for visual search. J Exp Psychol Hum Percept Perform 1989; 15:419-433. 15. Treisman A, Sato S. Conjunction search revisited. J Exp Psychol Hum Percept Perform 1990;16:459-478. 16. Wolfe JM, Yu KP, Pruszenski AD, Treue F, Cave KR. Limits on guidance of visual attention by parallel feature processes. Invest Ophthalmol Vis Sci 1989;30(suppl):159. 17. Wolfe JM, Friedman-Hill SR, BilskyAB. Parallel processing of part/whole information in visual search tasks. Percept Psychophys 1994;55:537-550. 18. Kundel HL. Search for lung nodules: The guidance of visual scanning. Invest Radio11991;266:777-787.