Satisfaction of Search

Generalized “Satisfaction of Search”:
Adverse Influences on Dual-Target Search Accuracy
Mathias S. Fleck, Ehsan Samei, and Stephen R. Mitroff
Duke University
The successful detection of a target in a radiological search can reduce the detectability of a second target,
a phenomenon termed satisfaction of search (SOS). Given the potential consequences, here we investigate the generality of SOS with the goal of simultaneously informing radiology, cognitive psychology,
and nonmedical searches such as airport luggage screening. Ten experiments utilizing nonmedical
searches and untrained searchers suggest that SOS is affected by a diverse array of factors, including (1)
the relative frequency of different target types, (2) external pressures (reward and time), and (3)
expectations about the number of targets present. Collectively, these experiments indicate that SOS arises
when searchers have a biased expectation about the low likelihood of specific targets or events, and when
they are under pressure to perform efficiently. This first demonstration of SOS outside of radiology
implicates a general heuristic applicable to many kinds of searches. In an example like airport luggage
screening, the current data suggest that the detection of an easy-to-spot target (e.g., a water bottle) might
reduce detection of a hard-to-spot target (e.g., a box cutter).
Keywords: visual search, satisfaction of search, luggage screening, radiological search
Given the potentially harmful consequences of missing an abnormality in a radiological scan, radiological research has scrutinized the circumstances of successful and failed target detection.
Among other findings, it has been shown that a specific target is
more likely to be missed when it is accompanied by an additional
abnormality than when it is the only target in a radiological
examination (e.g., Tuddenham, 1962). That is, an abnormality has
a lower rate of detection when presented in the same image as
another problem spot than when presented alone (see Berbaum,
Franken, Caldwell, & Schartz, 2009, for a recent review). This
phenomenon was originally characterized as a visual search that is
discontinued once the searcher finds a target and then becomes
“satisfied” with the “meaning” of an image (Tuddenham, 1962).
Such “satisfaction of search” (SOS) errors remain an acknowledged problem in radiologic examinations and have been demonstrated in chest radiography (e.g., Berbaum et al., 1998; Samuel,
Kundel, Nodine, & Toto, 1995), abdominal radiography (e.g.,
Franken et al., 1994), osteoradiology (e.g., Ashman, Yu, & Wolfman, 2000), and multiple trauma patients (e.g., Berbaum et al.,
1994), although the original “discontinued-search” account for
such effects has not seen strong support (Berbaum et al., 2009).
SOS generalizes across multiple radiological domains and extends, at a minimum, to certain cytological searches as well
(Bowditch, 1996). But do SOS errors arise in other visual searches
beyond the medical domain? The goal of the current article is to
explore the parameters in which SOS occurs in nonmedical visual
search in order to simultaneously inform multiple fields. First,
expanding the study of SOS into nonmedical searches will contribute to the discussion about the cause of SOS within radiology.
Recent radiography studies have suggested that SOS effects can
arise from scanning errors (Berbaum et al., 1996; Samuel et al.,
1995), recognition errors (Berbaum, Franken, Dorfman, Caldwell,
& Krupinski, 2000), or decision errors (Franken et al., 1994).
Understanding the parameters of SOS will be important in better
specifying the source of such errors and developing methods to
counteract them.
Second, establishing the scope of SOS errors in nonmedical
searches can inform the nature of the basic cognitive processes
broadly involved in visual search. SOS may reflect a general
heuristic of the decision-making process (e.g., “satisificing”; see
Simon, 1976) involved in any kind of visual search, and if so, such
errors should be observable in nonradiological contexts. Recent
studies incorporating searches with more than one type of target
(e.g., Menneer, Barrett, Phillips, Donnelly, & Cave, 2007; Menneer, Cave, & Donnelly, 2009) have complemented and expanded
general theories of visual search (e.g., Treisman & Gelade, 1980;
Wolfe, 2007), and the current study can contribute further. Past
research has examined various external factors on search such as
item familiarity (e.g., Wang, Cavanagh, & Green, 1994) and the
Mathias S. Fleck and Stephen R. Mitroff, Department of Psychology
and Neuroscience, Center for Cognitive Neuroscience, Duke University;
and Ehsan Samei, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center.
We thank Melissa Bulkin and Jordan Axt for help with data collection,
and we thank the Duke Visual Cognition Lab for helpful conversations. We
are very grateful to Ron Bowditch for extensive discussion of related issues
in cytology searches. We also thank Kevin Berbaum for sharing a prepublication draft of his review chapter in press. This research was partially
supported by National Institutes of Health/National Institute of Mental
Health (NIH/NIMH) grants F31 MH082582 (to Mathias S. Fleck) and R03
MH080849 (to Stephen R. Mitroff), Army Research Office Grant 54528LS
(to Stephen R. Mitroff), and a grant from the Institute of Homeland
Security Solutions (to Stephen R. Mitroff).
Correspondence concerning this article should be addressed to Stephen
R. Mitroff, Duke University, LSRC Building, Box 90999, Durham, NC
27708. E-mail: [email protected]
Journal of Experimental Psychology: Applied © 2010 American Psychological Association
2010, Vol. 16, No. 1, 60–71 1076-898X/10/$12.00 DOI: 10.1037/a0018629
60
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emotional salience of search items (e.g., Hanson & Hanson, 1988),
and here we explore the effects of multiple targets.
Third, finding that SOS effects exist outside the medical realm
would carry critical implications for tasks such as airport security
searches. For example, does the presence of a water bottle in a
luggage X-ray adversely affect the detectability of a pair of scissors also in the bag? While the commonalities between airport
baggage screening and medical image searches have only briefly
been considered together (e.g., Fleck & Mitroff, 2007; Gale,
Mugglestone, Purdy, & McClumpha, 2000; Wolfe, Horowitz, &
Kenner, 2005), given the dangerous implications, it is critical to
determine whether multiple target errors might occur in airport
security searches and to establish what properties of the search
might be predictive of SOS in these critical situations.
In the present article, we explore the robustness of SOS in basic
visual search tasks in which we systematically manipulated several
diverse factors. Through 10 experiments we examine how accuracy in a dual-target search is affected by (1) the relative salience
and frequency of different target types, (2) time and reward pressures, and (3) expectations about number of targets in a given
display. We reveal that SOS errors (the reduced detection of a
target when another target has also been detected in the same
display) may reflect a default and generalized search heuristic that
is sensitive to the interplay between several factors. Broadly, SOS
appears to arise in time-pressured search tasks when the searchers
have an expectation about the low likelihood of a specific target
type appearing, in relation to a different target type.
Effect of Salience and Frequency on SOS
(Experiments 1–3)
SOS in radiology is typically defined as an increased detection
rate for a particular target (e.g., a lesion) when it is the only target
in a radiographic image in comparison with when it is accompanied by an additional target (e.g., a pulmonary nodule). Usually the
relative salience of the primary and secondary targets is not manipulated, and the targets are from different categories (e.g., a
lesion and a pulmonary nodule). However, evidence from osteoradiology found that SOS was stronger when the added secondary
target was more salient than the test target (Berbaum et al., 1994).
Cytology research has additionally suggested that the frequency
and conspicuity of particular targets may lead to missing subtle,
smaller targets (Bowditch, 1996; DeMay, 1996, 1997). Here we
are particularly interested in this situation with differing salience
between the primary and secondary target given that, in response
to the nature of recent terrorist threats to aircraft safety, airport
baggage security regulations were broadened in August 2006 by
adding new categories to the prohibited items list, including liquids
and gels (Transportation Security Administration, 2009). These
additions vastly increase both the frequency and salience of certain
targets, as well as the possibility of multiple targets co-occurring in
the same image. Here we ask how the addition of a highly salient
and frequent target (e.g., a water bottle) might impact the detection
of a less salient or infrequent target (e.g., a box cutter) that may be
present in the same image. Manipulating the frequency of a particular target type may bias the searcher’s expectation for that
target and contribute to producing an SOS effect.
Method
All 10 experiments in this study utilized a similar search paradigm, detailed here. Any differences from this paradigm are noted in
the Method subsection for each subsequent section. See Table 1 for a
summary of all experiments and parameters.
Participants. Thirty individuals (Experiment 1: mean age
18.9 years, SD 1.2 years; Experiment 2: mean age 18.4 years,
SD 1.0 years; Experiment 3: mean age 18.7 years, SD 0.5
years) from the Duke University community participated (10 in
each experiment with 3, 3, and 4 male participants in each experiment, respectively). Each participant in this article completed only
one experiment. All participants gave informed consent and received either course credit or $10. The experiments were conducted on a Dell Optiplex computer running Windows 2000 and
programmed in Matlab 7.0 using the Psychophysics toolbox version 3.0 (Brainard, 1997).
Stimuli. The stimuli comprised two perpendicular lines
slightly offset from each other (Ts and Ls, stroke width 0.3°,
subtending 1.3° 1.3° total), with target Ts having a crossbar
directly in the middle and with distractor Ls having the crossbar
slid at variable distances away from the center (see Figure 1).
Stimuli were presented on a rendered grayscale “cloud” background (brightness range 10%–50% black) that differed on each
trial (see Figure 1). The background served to increase overall
difficulty and to model background noise that is commonly present
in radiological searches. Distractor Ls were presented at varying
shades of gray (range 28%–66% black), and target Ts were
presented at one or both of two visibility levels: high salience
(range 66%–70% black) or low salience (range 28%–40%
black). In this fashion, high-salience Ts were relatively easy to
detect, and low-salience Ts were more difficult to detect. Each
stimulus was placed with a slight spatial jitter within randomly
selected cells of an invisible 8 7 grid subtending 25.4° 19.1°
at an approximate viewing distance of 60 cm.
Procedure and design. Each trial began with a cross appearing for 0.5 s at the center of the screen. The cross was replaced
with the search array, which consisted of 25 items. Participants
were informed that there were 0, 1, or 2 target Ts to find within
each display. Participants used the mouse to click on each detected
target item and then clicked a blue button at the bottom of the
screen labeled DONE to complete the trial. The DONE button
appeared 3 s after the onset of each trial, and the mouse cursor was
reset to the center of the screen after each trial. Participants could
correct an error or a mis-click by clicking a yellow button at the
bottom of the screen labeled CLEAR before completing the trial.
Each trial had a time limit of 15 s, after which no further clicks
were accepted and a message was displayed encouraging participants to try to finish searching and press the DONE button before
time elapsed on subsequent trials. Responses made prior to the
timeout were recorded and analyzed even if the DONE button was
not pressed.
Trials were classified as one of four types on the basis of the
number and the salience of the target Ts presented within the array
of distractor Ls, resulting in trial types of no target, single high
salience, single low salience, or dual target (both a high-salience
target and a low-salience target present). Experiments 1–3 only
varied in the relative proportion of trial types presented to each set
of participants to bias their expectation about how frequently a
DUAL-TARGET SEARCH ACCURACY 61
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high-salience or low-salience target might appear. High-salience
single targets were as equally frequent as were low-salience single
targets in Experiment 1, twice as frequent in Experiment 2, and
three times as frequent in Experiment 3.
Each experiment began with one block of 20 practice trials
that was matched to the trial-type frequency of the rest of the
experiment. Participants were not informed about how often
targets would be present, although the practice block gave some
indication as to what to expect in the rest of the experiment.
During practice, immediate feedback was provided on any false
positive identification or missed targets. Participants then completed 250 test trials with no accuracy feedback, divided into
50-trial blocks.
Planned analyses. To assess SOS errors, the critical
planned comparison for each experiment was how accurately
the low-salience targets were detected when they were the only
target in the display (low-salience single targets) versus how
accurately the low-salience targets were detected in a dualtarget trial, given that the high-salience target was also detected. Note that this calculation of SOS differs slightly from
that typically used in radiology. Radiology studies have often
used a small number of trials in which each specific image is
presented twice, once with a single target and once with that
same target accompanied by an additional target. Such studies
typically examine SOS by looking for changes in receiveroperating characteristic (ROC) curves, which incorporate a
reported confidence rating about each detected target. We did
not utilize identical displays with or without an added target and
instead compared a large number of single- and dual- target
trials, which provide the power to look for generalized effects.
We also did not include confidence ratings, given the reduced
role of decision-making required with our simpler stimuli.
Table 1
Experimental Parameters and Accuracy Data for Experiments 1–10
Experiment
Experimental parameters Accuracy (%)
SOS effect:
Time limit SOS?
Prevalence
high:low
Trial type
counts Low single Low dual
Low-single vs.
Low-dual
1 15 s 1:1 High: 50 60.40 63.09 t(9) 1.28 ✘
Low: 50 (12.57) (10.72) p .23
High-Low: 50 d 0.23
None: 100
2 15 s 2:1 High: 100 66.00 59.72 t(9) 3.18
Low: 50 (16.22) (16.62) p .01
High-Low: 50 d 0.38
None: 50
3 15 s 3:1 High: 120 72.50 60.76 t(9) 3.56
Low: 40 (12.42) (12.23) p .01
High-Low: 40
None: 50
d 0.95
4 30 s 2:1 High: 100 60.60 58.50 t(9) 1.07 ✘
Low: 50 (17.26) (19.59) p .31
High-Low: 50 d 0.11
None: 50
5 30 s 3:1 High: 120 60.50 55.64 t(9) 2.08
Low: 40 (13.83) (16.67) p .07
High-Low: 40 d 0.32
None: 50
6 30 s 6:1 High: 150 68.00 57.90 t(9) 2.11
Low: 25 (12.51) (15.98) p .06
High-Low: 25 d 0.70
None: 50
7 Luggage line 3:1 High: 96 74.07 73.08 t(9) 0.93 ✘
(variable rate) Low: 32 (15.11) (11.84) p .38
High-Low: 32 d 0.07
None: 40
8 Luggage line 3:1 High: 96 71.89 63.92 t(9) 3.21
(constant rate) Low: 32 (12.77) (15.11) p .01
High-Low: 32 d 0.57
None: 40
9 30 s 1:1 Low: 100 78.17 76.75 t(9) 1.12 ✘
Low-single:dual Low-Low: 100 (7.80) (13.22) p .29
None: 50 d 0.13
10 30 s 4:1 Low: 160 74.91 71.63 t(9) 3.10
Low-single:dual Low-Low: 40 (8.70) (5.43) p .01
None: 50 d 0.45
Note. Accuracy reflects mean detection rates with standard deviations in parentheses; p-values represent 1-tailed t-tests of low-salience target accuracy
in single vs. dual target trials.
62 FLECK, SAMEI, AND MITROFF
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Results
Our primary measure of interest is detection accuracy for lowsalience targets (response time data can be seen in Appendix A,
and high-salience target accuracy data can be seen in Appendix B),
and mean rates are presented in Table 1. Single-target accuracy
was calculated as the number of single-target trial hits divided by
the total number of single-target trials. Dual-target accuracy was
calculated as the number of dual-target trials in which both targets
were detected divided by the total number of dual-target trials in
which the high-salience target was detected (and the low-salience
target missed). This calculation of dual-target accuracy was chosen
to focus specifically on the hypothesis that detection of a highsalience target interferes with detection of a low-salience target.
Additionally, it offers a conservative measure of SOS; inclusion of
dual-target trials in which both the low- and high-salience targets
were missed would potentially serve to inflate any potential SOS
effect by lowering the dual-target accuracy rate in relation to the
single target rate.
A repeated-measures analysis of variance (ANOVA) was run on
the low-salience target accuracy with the between-subjects factor
of frequency (1, 2, and 3) and the within-subject factor of
number of targets (1 and 2). There was a main effect of number of
targets, F(1, 27) 7.40, p .01, p 2 .22, indicating that
accuracy for low-salience targets was lower when coupled with a
high-salience target. There was also an interaction between number of targets and frequency, F(2, 27) 5.02, p .02, p 2 .27,
such that single-target accuracy was increasingly better than dualtarget accuracy as a function of increasing frequency.
Planned one-tailed t tests for low-salience targets revealed that
the SOS effect was not significant at the 1 frequency, t(9)
1.28, p .23, one-tailed, d .23, but was significant at the 2
frequency, t(9) 3.18, p .01, one-tailed, d .38, and at the 3
frequency, t(9) 3.56, p .01, one-tailed, d .95 (see Table 1).
We used one-tailed tests for this statistical comparison because our
a priori SOS prediction, based upon the radiology research, is that
detection should be better on single-target trials than on dual-target
trials. Additionally, because raw trial counts were chosen to balance single-target and dual-target trials for low-salience targets, we
did not focus on high-salience dual-target costs. However, inspection of that data revealed no cost on dual-target search for highsalience targets when compared with single-target high-salience
searches in any of these experiments (see Appendix B).
For all three experiments, the participants had a small proportion
of trials in which they did not click DONE before the 15-s time
limit (Experiment 1: M 1.4%, SD 1.2%; Experiment 2: M
1.1%, SD 0.6%; and Experiment 3: M 1.5%, SD 1.4%).
There were few false alarms, which were calculated as the percentage of all trials with one or more false positive responses
(Experiment 1: M 2.8%, SD 2.4%; Experiment 2: M 1.6%,
SD 1.9%; and Experiment 3: M 2.3%, SD 3.7%).
Discussion
The results of Experiment 1 indicated that salience differences
alone are not enough to trigger SOS errors. In a dual-target search
wherein one target is highly salient and another is less salient and
both are equally likely to be present in any given display, detection
of the high-salience target does not lead to a higher miss rate for
the low-salience target than when the low-salience target is presented by itself (Experiment 1). However, when the relative frequency of the high-salience targets increased (Experiments 2 and
3), participants missed low-salience targets more often on trials in
which a high-salience (and highly expected) target was detected
than on trials in which the low-salience target was presented alone.
The target prevalence differences appear to lead the participants to
shift their biases about the type of target to expect on any given
trial. Interestingly, biases about the expected number of targets
also changed accuracy: single-target low-salience trial accuracy
increased as the number of single-target high-salience trials increased. Thus, while salience differences alone were not enough to
induce SOS (Experiment 1), it appears that changing relative
frequency of target types, and subsequent expectations about those
target types, can indeed generate SOS errors. We will return to this
result in the General Discussion section after exploring additional
possible influences on SOS in Experiments 4–10.
Effect of Time Pressure on SOS (Experiments 4–6)
Radiological studies of SOS are typically self-paced paradigms
modeled after routine radiograph examinations (e.g., Berbaum et
al., 1998). Even though such studies have demonstrated significant
SOS effects, no study has yet to directly test the effects of a
specific time pressure that might be presented in radiographic
workflows. A radiologist may feel compelled to process a minimum number of radiographs in a day, a pressure that is absent in
a controlled radiology experiment. Relatedly, during high passenger flow, luggage screening typically occurs on the order of 3–5 s
per inspection (Schwaninger, 2005), indicating the possibility of a
strict time pressure (potentially even more so than that found in
radiology). Here we ask whether time pressure plays a role in the
SOS effect.
In Experiments 1–3, we utilized a 15-s time limit per trial.
Although search tasks such as luggage screening and radiology
may indeed be faced with time pressures, we wish to establish
Figure 1. Sample search display for Experiments 1–10. Each trial contained 0, 1, or 2 T-shaped targets among the L-shaped distractors. Experiments 7 and 8 additionally displayed a row of “luggage” icons across the
top of the display.
DUAL-TARGET SEARCH ACCURACY 63
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whether the SOS effects observed in Experiments 2 and 3 were
potentially driven by time pressure, instead of or in addition to
frequency. In Experiments 4–6, we double the amount of time
allowed to search on each trial. We again manipulate the frequency
of salient targets to explore the relationship between salience,
frequency, and time pressure.
Method
The search task was identical to that in Experiments 1–3. Thirty
individuals participated (Experiment 4: mean age 18.6 years,
SD 1.0 years; Experiment 5: mean age 19.1 years, SD 1.2
years; Experiment 6: mean age 18.8 years, SD 1.4 years).
There were 10 participants per experiment with 5, 4, and 4 male
participants, respectively. In Experiments 4–6, each participant
was allowed 30 s to search each display, which was twice as long
as allowed in Experiments 1–3. High-salience targets were either
2 (Experiment 4) or 3 (Experiment 5) more frequent than were
low-salience targets, just as in Experiments 2 and 3, respectively.
Because Experiment 1 revealed no evidence for SOS, we did not
repeat the 1 frequency distribution here. Likewise, to increase
our chances of obtaining SOS with 30 s to search, we included a
new condition with a more extreme salience distribution (Experiment 6; 6).
Results
Mean detection rates are presented in Table 1. Accuracy data
from Experiments 2–5 were submitted to a repeated-measures
ANOVA with the within-subject factor number of targets (1 or 2)
and between-subjects factors of frequency (2 or 3) and time
limit (15 s or 30 s), which revealed a significant main effect of
number of targets, F(1, 36) 13.78, p .001, p 2 .28, but no
effects of time limit or frequency. Planned, follow-up one-tailed t
tests for low-salience targets revealed no SOS effect for Experiment 4, t(9) 1.07, p .31, one-tailed, d .11, and a nonsignificant effect for Experiment 5, t(9) 2.08, p .07, one-tailed,
d .32. Experiment 6, with the greater frequency of high- to
low-salience targets, also generated a nonsignificant SOS effect,
t(9) 2.11, p .06, one-tailed, d .70. There were very few
trials in which participants did not click DONE before the 30-s
time limit (Experiment 4: M 0.2%, SD 0.3%; Experiment 5:
M 0.2%, SD 0.3%; and Experiment 6: M 0.1%, SD
0.3%). There were few false alarms (Experiment 4: M 1.4%,
SD 1.5%; Experiment 5: M 1.6%, SD 2.3%; and Experiment 6: M 3.1%, SD 3.7%).
Discussion
Although available time to search did not interact with frequency and number of targets, the notable absence of an interaction between frequency and number of targets, as observed in
Experiments 1–3, potentially indicates that time limit may play
some role in exacerbating the SOS effect. Comparing the onetailed planned comparisons and associated effect sizes of Experiments 4 through 6 reveals much weaker SOS effects when participants were allowed 30 s to search, in comparison with
Experiments 1 through 3 when participants were allowed 15 s to
search. One possible reason that the factor of time limit was not
significant was that the time limit may have pressured searchers
only on a select number of especially “difficult” trials, depending
on the vagaries of the randomized background noise and the
particular clustering of stimuli. Once a particular pace is set by a
participant, the actual time remaining in a trial may become less of
a focus and therefore not impactful on behavior. While these data
do not directly indicate that time limit plays a significant role in
driving SOS, the reduced effect sizes of Experiments 4–6 in
relation to Experiments 1–3 indeed suggest that time pressures
should be minimized in critical tasks with more than one target,
such as in radiology, cytology, or airport security. We more
directly explore the link between time pressure and accuracy in
Experiments 7 and 8, in which we examine SOS in searches that
rewarded participants for minimizing time on task.
Interaction Between Time and Reward Pressures on
SOS (Experiments 7 and 8)
While Experiments 1–6 collectively demonstrated that SOS
does indeed generalize beyond medical searches, the role of external pressures such as a time limit remains open. Beyond time
limit pressures, laboratory-based experiments with novice searchers typically do not address the host of other pressures that a
searcher may face. For example, a radiologist, a cytologist, and an
airport X-ray screener all have an immeasurably higher incentive
to avoid missed targets than do participants volunteering in a
laboratory study. Although it is extremely difficult to replicate in
the lab such pressure to perform accurately, in Experiments 7 and
8 our goal was to add pressure by motivating participants through
performance-based reward and to further explore the role of time
pressure by directly linking overall performance to time on task.
We also wished to establish that the generalized SOS effects found
in Experiments 1–6 are not simply driven by a low motivation to
find all targets. In many laboratory studies, there is a concern that
participants may wish to complete the experiment as quickly as
possible, and in a multiple-target paradigm this is a particularly
hazardous prospect. Participants may feel that they have “adequately participated” on each trial upon finding any target, thereby
manifesting the exact property we wish to explore. Although
arguably this prospect is inherent as well in any nonlaboratory
search task, a reward manipulation addresses whether the SOS
effect may be attenuated when motivation to find all possible
targets is increased.
Moreover, we wanted to allow participants the ability to “budget” their time across the length of the experiment to enable them
to selectively balance accuracy versus time. In many tasks, searchers are not subject to the “fixed” time limit pressure as we have
utilized it so far. Instead, they have the option of economically
allocating time appropriately—spending more time on the cases
that require additional attention while speeding through images
deemed “easier” upon initial inspection. In this manner, the time
pressure is less a per-image pressure as it is a per-session pressure
(e.g., to search a given number of images within a day). In
Experiments 7 and 8, we implement a performance-based reward
system simulating a luggage screening task to see how and when
SOS would arise when participants are motivated to perform
accurately and are facing a time pressure across the experiment
rather than within a trial.
64 FLECK, SAMEI, AND MITROFF
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Method
Twenty individuals (Experiment 7: mean age 18.7 years,
SD 0.9 years, 4 men; Experiment 8: mean age 19.1 years,
SD 1.1 year, 8 men) were paid $10 for their participation and
had the opportunity to earn an extra $10 for high performance (see
below).
The search task was identical to that in the previous experiments, except that we implemented an accuracy- and time-based
reward system for performance on the task rather than a fixed time
limit. Participants were instructed to manage a “line of luggage,”
represented by a row of luggage icons at the top of the screen
above the searched area, which increased and decreased in the
number of icons throughout the experiment. As participants completed each trial, regardless of accuracy on the trial, one icon was
eliminated from the row. Icons were added to the row as a function
of time spent on each trial. In Experiment 7, this time window was
set individually for each participant on the basis of their average
time spent on no-target trials in the practice block. Slower participants therefore accrued “luggage icons” at a slower rate than did
faster participants, and this manipulation reduced the time pressure
factor overall while still demanding that participants budget accuracy versus time spent searching. In Experiment 8, this time
window for luggage accrual was fixed for all participants at 9 s per
icon to establish a much greater time pressure and to determine
whether participants would adapt to the relatively fast rate of
luggage accrual.
The luggage line was tied to a point-based reward system.
Participants were told that successfully responding to any trial
(either with zero, one, or two targets) would always result in a gain
of points, but that these points would be greater if the luggage line
at the top of the screen was kept short. Participants were warned
that missed targets would result in a significant loss of points, and
that even when the luggage line reached its longest length (16
icons along the top of the screen), participants would continue to
gain points for each correct trial. Participants were told that accuracy was the primary goal in the task, and that line-length management was a secondary goal. The computer tracked each participant’s cumulative point total offscreen, and participants were
informed that the top points scorer out of every 3 participants
would receive an extra $10. After each block, participants were
informed of their current point total, but they were never told about
the total points of any previous participant.
Participants completed 50 practice trials with feedback, followed by 200 test trials with no feedback, divided into four blocks
of 50 trials each. Each display contained 30 items. Similar to
Experiments 3 and 5, we utilized a high-salience to low-salience
single target trial ratio of 3:1 to replicate known SOS conditions.
Results
Mean detection rates are presented in Table 1. A repeatedmeasures ANOVA with within-subjects factor of number of targets (1 or 2) and a between-subjects factor of time pressure
(variable or fixed) revealed a significant main effect of number of
targets, F(1, 18) 4.50, p .05, p 2 .20. There was a nonsignificant interaction between number of targets and time pressure,
F(1, 18) 2.73, p .12, p 2 .13. There was a small proportion
of false alarms in each experiment (Experiment 7: M 0.7%,
SD 1.0%; Experiment 8: M 2.1%, SD 1.8%). Experiment
7, with the rate of luggage accrual customized for each participant
as a function of their practice block speeds, mirrored the general
accuracy rate of Experiment 3, but did not exhibit an SOS effect,
t(9) .93, p .38, one-tailed, d .07. Alternately, Experiment
8, with the rate of luggage accrual fixed at 9 s across all participants, produced lower accuracies overall and a significant SOS
effect, t(9) 3.21, p .01, one-tailed, d .57 (see Table 1 for
data and planned t test).
Discussion
Experiment 7 did not itself yield an SOS effect with its individually titrated luggage accrual (i.e., lower pressure), but Experiment 8, in which luggage icons appeared in a relatively rapid and
steady stream, exhibited a strong SOS effect. Examining effect
sizes across the four experiments in which frequency and salience
combinations were held constant (Experiments 3, 5, 7, and 8), we
found the greatest SOS effects in the two experiments in which
time pressure was highest: Experiment 3 (15-s time limit, d .95)
and Experiment 8 (rapid luggage accrual rate, d .57), in contrast
to Experiment 5 (30-s time limit, d .32) and Experiment 7
(individualized luggage accrual rate, d .07). Collectively, these
data suggest that external pressures of reward and time increase
SOS-type errors. Implications for the role of external pressures are
further explored in the General Discussion.
Effect of Expectation About Number of Targets on
SOS (Experiments 9–10)
Experiments 1–8 explored the influences of salience, frequency,
and external pressure on multiple-target accuracy. To examine
these issues, each of the experiments so far has utilized two types
of targets: high-salience targets and low-salience targets. This was
motivated by salience-related SOS effects in medical research
(Berbaum et al., 1994; Bowditch, 1996) and an attempt to map
onto the saliency and frequency differences found in current airport security screening. However, we also wished to determine the
extent to which SOS may arise when two present targets are
identical to one another, because many searches involve looking
for multiple items that may be very similar.
Recent work has demonstrated that searches for multiple categories yield decreased accuracy when compared with singlecategory searches (Menneer et al., 2007, 2009). This multiplecategory deficit has also been discussed in radiology with respect
to SOS errors; radiologists may adopt a readiness to seek out a
specific pattern in the image, at the expense of detecting other
target types (Berbaum et al., 2009). In such a “perceptual set”
explanation, the detection of one target type, say a tumor of a
particular contrast, may bias searchers to selectively search for
additional targets with the same perceptual features as that tumor
and to discount the features of targets from different categories
(Berbaum et al., 1990). Because the first eight experiments have all
utilized two classes of targets, some of the SOS effects demonstrated so far may be related to multiple-category effects.
In Experiment 9, we remove all expectation biases about both
the number of targets and the target salience by making secondary
targets identical to the primary target and by making dual-target
trials as equally likely as single-target trials. Thus, on any given
DUAL-TARGET SEARCH ACCURACY 65
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trial, it is equally likely that there may be one or two targets in the
display, and if there are two targets, they are identical in salience
and shape. In Experiment 10, we maintain the identical-target
manipulation while reintroducing the element of frequency and
expectation by altering the ratio of single-target to dual-target
trials. The goal is to determine whether SOS is sensitive to expectations about the number of targets likely to be present even when
all targets are identical.
Method
Twenty individuals (Experiment 9: mean age 19.9 years,
SD 1.0 years, 5 men; Experiment 10: mean age 21.3 years,
SD 5.0 years, 3 men) participated. To minimize the contribution
of the time pressure effect to SOS implicated by the first eight
experiments, we again utilized a 30-s time limit rather than the
reward-based paradigm. The other difference from the previous
search parameters was that all targets were presented at the lowsalience values of Experiments 1–8, resulting in three trial types:
no target, single low salience, and dual low salience. Because there
were no salience differences between targets, dual-target accuracy
was calculated differently from previous experiments: Here the
accuracy was calculated by dividing the number of dual-target
trials in which both were detected by the sum of those trials plus
all trials in which only one target was detected. Dual-target trials
in which both targets were missed were again excluded to focus on
errors that arise as a consequence of detecting one target.
Results
Mean detection rates are presented in Table 1. A repeatedmeasures ANOVA with a within-subject factor of number of
targets (1 or 2) and a between-subjects factor of single-to-dual
frequency (1 or 4) revealed little effect of number of targets,
F(1, 18) 2.86, p .11, p 2 .14, and no significant interaction
between number of targets and trial type frequency, F(1, 18) 1,
p .51, p 2 .02. Planned, follow-up one-tailed t tests for
low-salience targets revealed no SOS effect for Experiment 9,
t(9) 1.12, p .29, one-tailed, d .13, as was expected, but in
Experiment 10, when single-target trials were four times more
likely to occur than were dual-target trials, participants demonstrated a significant SOS effect, t(9) 3.10, p .01, one-tailed,
d .45. There was a small proportion of trials in which participants did not click DONE before the 30-s time limit (Experiment
9: M 0.5%, SD 0.9%; Experiment 10: M 0.2%, SD
0.3%). There were few false alarms (Experiment 9: M 5.6%,
SD 5.9%; Experiment 10: M 1.5%, SD 1.1%).
Discussion
The lack of an SOS effect in Experiment 9 is unsurprising,
particularly considering the absence of an effect in Experiments 1
and 4 in which differing salience levels may have biased expectation about target types and yet still yielded no SOS effect.
Keeping all else the same and equating salience here thus predictably generated no SOS effect. However, when the relative frequency of single- and dual-target trials was manipulated in Experiment 10, participants missed more targets in dual-target
conditions than in single-target conditions. It should be noted that
the between-experiments manipulation of relative frequency of
target types showed no interaction with number of targets, indicating that additional work may be necessary to more fully explore
the relationship. However, comparison of the a priori one-tailed t
tests and associated effect sizes offer some preliminary evidence
that increased frequency of single-target trials biased participants’
expectations and led to a greater relative proportion of missed
dual-target trials. Experiment 10 indicates that a “perceptual set”
or “multiple-category effect” account cannot entirely drive the
SOS effects observed in the previous experiments, and that SOS is
indeed sensitive to expectations about number of targets.
General Discussion
The 10 experiments presented here focused on the phenomenon
of “satisfaction of search” (SOS), whereby the detection of one
target is hindered by the successful detection of another target.
This research was conducted with the goal of establishing the
commonalities between visual search as it is studied in radiology
and how it is studied in cognitive psychology. Because SOS may
not be a problem exclusive to radiology, we have investigated here
the generality of the effect and explored the parameters to which it
is sensitive. By manipulating a set of search parameters, we have
revealed several contexts in which SOS errors can be observed,
even in a nonmedical search task, and with novice searchers.
Importantly, this general finding of SOS outside of radiology
suggests that the factors that modulate accuracy in multiple- target
detection may be broadly applicable to many other critical search
tasks such as airport security screening. These factors include (1)
the relative salience and frequency of different target types, (2)
external pressures of reward and time, and (3) expectation about
the number of targets present.
Summary
Experiments 1–3 established that SOS is sensitive to the interaction
between salience and the expectancy about certain target events: As
the frequency of easy-to-detect high-salience targets increased in
relation to difficult-to-detect low-salience targets, participants
missed more of those low-salience targets in a dual-target condition than when the low-salience target was presented by itself.
Interestingly, salience differences alone (Experiment 1) were not
sufficient to induce SOS. Rather, it is the interaction between those
salience differences and biased expectation about the differing
target types that leads to SOS; in Experiments 2 and 3, we shifted
the frequency of targets such that participants expected highsalience targets more often than low-salience targets, and consequently dual-target low-salience performance fell.
In Experiments 4–6, we examined the role of time pressure in
SOS. A potential concern with the significant SOS effects in
Experiments 2 and 3 is that a 15-s time limit may not be enough
time to adequately search the display and find two targets. In
Experiments 4–6, we doubled the time limit to 30 s, resulting in
much weaker SOS effects.
In Experiments 7 and 8, we implemented a performance-based
reward system to further explore the impact of time pressure and
to determine whether increased motivation can offset the SOS
effect. That is, if participants are given an incentive to be both
accurate and fast, will they no longer reveal SOS? Experiment 8
66 FLECK, SAMEI, AND MITROFF
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revealed a strong effect of the number of targets on accuracy,
suggesting that SOS arises even in motivated participants when
external pressure is increased (here, in the form of optimizing
accuracy and time on task).
Lastly, in Experiments 9 and 10 we focused on whether expectation about number of targets reduces detection of extra targets,
even in the absence of multiple categories. We wished to determine whether SOS can arise even when all targets are perceptually
identical. A failure to find an effect when targets are perceptually
similar would suggest that SOS arises as a function of a “perceptual set” that biases the decision to terminate a search after the
“preferred target” has been detected. However, even when targets
were perceptually identical, we still observed SOS, indicating that
SOS represents an effect distinct from the multiple- category effect
previously demonstrated (Menneer et al., 2007, 2009).
Implications for SOS in Radiology
SOS has been extensively studied in radiology, and the present
experiments attempt to establish the generality of the effect to
determine whether and how SOS errors arise in nonmedical domains. However, these generalized results may in turn inform the
attempts to minimize SOS within radiology. We discuss here a few
possible implications, but their ultimate impact on radiology remains an open question given that the current search arrays differ
from radiological scans and that our novice searchers do not have
the benefit of extensive training and experience.
The theory that a “perceptual set” (Berbaum et al., 2009) may
act to increase SOS errors is in accordance with the data from
Experiments 1 through 8, which illustrate that when participants
expected a particular visual pattern for the target (in the present
studies, a high-salience T shape), the result was that successfully
finding such a target interfered with detection of a perceptually
different target (here, a low-salience T shape). However, Experiments 9 and 10 indicate that this explanation does not fully account
for SOS errors, because perceptually similar targets continued to
yield SOS effects when expectations about the number of targets
were biased.
Additionally, although radiologists may be well trained in efficiently optimizing the trade-off between accuracy and the need to
process at least a certain number of cases in a particular session,
our data linking time pressure with SOS, both within a particular
case (e.g., Experiment 2 vs. Experiment 4) and across a longer
session (Experiment 7 vs. Experiment 8), should be taken into
consideration. The influence of external pressures on the SOS
effect (here, in a performance-based paradigm rewarding efficient
responding) specifically emphasizes that radiological searches
should minimize pressure to process a particular number of cases
in a day.
Implications for Cognitive Psychology
Visual search is a well-studied cognitive task, and this is not the
first comparison of searches for one versus multiple targets (e.g.,
Gibson, Li, Skow, Brown, & Cooke, 2000; Ko¨rner & Gilchrist,
2008; Menneer et al., 2007, 2009; Metlay, Sokoloff, & Kaplan,
1970; Takeda, 2004). However, the influence of multiple possible
targets in cognitive studies of search has typically explored the
impact on search efficiency, or the added cost of extra targets on
time to search, rather than on search accuracy, as we examined
here. In two notable exceptions, Menneer and colleagues (2007,
2009) have demonstrated an accuracy effect for search for multiple
targets. In these studies, participants searched for multiple categories of targets, but there was never more than one individual target
in any given trial. A performance decline was found in this
multiple-category search design, but unlike here, these effects do
not reflect the cost of having detected an added target within the
same search. Rather, the performance decline was a function of
increasing the possible search space to include more categories.
This accuracy cost maps well onto the multiple target types utilized in the present article in the form of differing salience levels,
and further indicates that expectation for a single type of target can
affect search performance.
The search parameters we explore in this article (relative frequency of target types, time and reward pressure, and number of
targets) are not novel in visual search studies, but the current
experiments offer some of the first evidence about how these
factors interact in a multiple-target paradigm to modulate accuracy. These results raise possible extensions to established theories
of visual search. For example, Chun and Wolfe (1996) explored
how an individual ends a search when a target is not present. The
most conservative strategy is to search every possible item before
declaring no target is present, but searchers do not do this in
practice. Instead, they make assumptions and end their search on
the basis of gathered information about the nature of the targets
and the distractors. Additionally, they adjust their search time on
the basis of misses and hits and also guess from time to time (Chun
& Wolfe, 1996). These same principles likely come into play for
accuracy in a multitarget search. Once one target is detected and a
second might be present, how will searchers adjust their search
behavior? Future research using the current paradigm and additional variables (e.g., changes in set size) should be fruitful.
More generally, the finding of SOS in a nonmedical context and
in an untrained, naı ¨ve group of participants suggests that this type
of error reflects a global search heuristic. Indeed, it may be
adaptive to exhibit SOS as a means to maximize efficiency of time,
just as the phenomenon of “inhibition of return” (Posner & Cohen,
1984) is thought to maximize the efficiency of search and foraging
behavior by deprioritizing previously attended locations. Regardless of the mechanism, that SOS is a general effect, sensitive to
pressures nonspecific to radiology, suggests that any visual search
incorporating multiple simultaneously present targets must take
into account the interactions we have demonstrated here, adding to
the previously known data on visual search including effects of
item frequency (e.g., Fleck & Mitroff, 2007; Rich et al., 2008; Van
Wert, Horowitz, & Wolfe, 2009; Wolfe et al., 2005), familiarity
(e.g., Wang et al., 1994), set size (e.g., Carter, 1982; Wolfe, 1998),
target and distractor heterogeneity (e.g., Nagy & Thomas, 2003;
Palmer, Verghese, & Pavel, 2000), emotionality of stimuli (e.g.,
Gerritsen, Frischen, Blake, Smilek, & Eastwood, 2008), and memory (e.g., Horowitz & Wolfe, 1998; Ko¨rner & Gilchrist, 2008), to
name a few.
Implications for Luggage Screening and Other
Related Searches
Given the generality of SOS, these results motivate a careful
analysis of search tasks to determine whether similar multipleDUAL-TARGET SEARCH ACCURACY 67
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target effects may be contributing to overall error rates in other
socially critical searches. In the particular case of airport security
X-ray screening, it is obviously a vital safety issue to ensure maximal
detection rates in luggage screening, yet there has been relatively little
cross-talk between radiology, cognitive psychology, and transportation security, despite the many factors common to visual search tasks
in all three domains (for some examples, see Fiore, Scielzo, &
Jentsch, 2004; Fleck & Mitroff, 2007; Gale et al., 2000; McCarley
& Carruth, 2004; McCarley, Kramer, Wickens, Vidoni, & Boot,
2004; Menneer et al., 2007; Smith, Redford, Washburn, & Taglialatela, 2005; Wiegmann, McCarley, Kramer, & Wickens, 2006;
Wolfe et al., 2005).
Most of the factors explored in this article can directly inform
search tasks such as airport security. Notably, the interaction
between salience and expectancy is directly relevant to current
security searches. As of this writing, current security protocols
mandate that airport screeners search for very salient and common
targets such as water bottles, hair gel, soft drinks, and toothpaste,
potentially at the expense of finding additional targets that may be
better concealed and less frequent, such as scissors, box cutters, or
pocketknives. Although extensive training assuredly emphasizes
an exhaustive search no matter the search results, the finding of
salience effects in osteoradiology (Berbaum et al., 1994) and
cytology (Bowditch, 1996) indicates that training may not override
this basic search heuristic.
We also found that time pressure likely exacerbates the SOS
effect. Critically, airport security searches are much shorter and
more numerous in a session than are radiograph examinations.
Although searchers have no “fixed time limit” after which they can
no longer search, there is likely to be a global pressure to keep
security lines moving efficiently, thus making these effects of time
pressure particularly relevant. It has been suggested that screener
performance is not linked to salary (Filipczak, 1996; Guzzo, Jette,
& Katzell, 1985), but we did observe in the present study that
reward-based pressure to perform accurately and quickly leads to
increased SOS. The potentially significant factor of time pressure
should therefore be an important consideration in the training of
airport security screeners and in constructing the environment in
which searches are conducted (i.e., obscuring screener awareness
of passenger line length). Future SOS research that more closely
parallels the search conditions of airport security screening might
reveal optimal strategies for efficient staffing and operations to
reduce both error and cost.
Conclusion
Our primary goal in this article has been to interrogate the
phenomenon of SOS in a series of controlled laboratory studies to
simultaneously inform radiology, cognitive psychology, and nonmedical searches such as airport baggage screening. One of the
most important implications from this work is that we reveal the
first evidence for SOS in a nonmedical search. By demonstrating
that SOS can arise for nonexperts and in standard cognitive psychology search paradigms, we illustrate some of the factors critical
to studying and understanding errors in multitarget search. SOS is
a complex, generalized effect, and the only way to reduce its
impact is to carefully delineate its diverse variety of underlying
causes.
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(Appendices follow)
DUAL-TARGET SEARCH ACCURACY 69
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Appendix A
Response Time Data
Table A1
Response Time Data for Experiments 1–10
Experiment
Experimental parameters Response time in seconds (with SD)
Time limit
Prevalence
high:low
Trial type
counts No target High single Low single Dual target
1 15 s 1:1 High: 50 9.02 9.43 9.51 8.52
Low: 50 (1.49) (1.35) (1.38) (1.23)
High-Low: 50
None: 100
2 15 s 2:1 High: 100 9.65 9.42 9.58 8.10
Low: 50 (0.95) (1.20) (0.86) (0.72)
High-Low: 50
None: 50
3 15 s 3:1 High: 120 9.93 9.53 10.01 8.55
Low: 40 (0.89) (0.64) (0.70) (0.66)
High-Low: 40
None: 50
4 30 s 2:1 High: 100 10.45 9.39 9.83 8.05
Low: 50 (2.64) (2.03) (1.96) (1.40)
High-Low: 50
None: 50
5 30 s 3:1 High: 120 10.97 10.23 10.72 8.99
Low: 40 (2.87) (2.09) (2.26) (1.58)
High-Low: 40
None: 50
6 30 s 6:1 High: 150 11.05 10.38 10.88 8.83
Low: 25 (1.65) (1.32) (1.65) (0.78)
High-Low: 25
None: 50
7 Luggage line 3:1 High: 96 10.15 9.46 10.04 7.02
(variable rate) Low: 32 (3.57) (2.63) (3.23) (1.45)
High-Low: 32
None: 40
8 Luggage line 3:1 High: 96 11.77 10.95 11.12 8.65
(constant rate) Low: 32 (4.22) (3.08) (2.94) (1.40)
High-Low: 32
None: 40
9 30 s 1:1 Low: 100 12.47 — 12.19 10.05
Low-single:dual Low-Low: 100 (3.36) (3.13) (1.73)
None: 50
10 30 s 4:1 Low: 160 11.99 — 12.02 10.61
Low-single:dual Low-Low: 40 (2.06) (1.35) (0.83)
None: 50
Note. One mechanistic explanation for SOS is a “truncated search,” in which participants prematurely end their search
after finding a target (Samuel et al., 1995). In the current experiments, participants click on each target they find and then
click a button labeled “DONE” once they have decided to terminate their search. Thus, in principle we can ask whether or
not participants exhibit a truncated search by comparing the time taken to click “DONE” for high-salience single target trials
in which the target was correctly detected and the no-target trials without false alarms. After successfully finding a
high-salience target, are participants ’satisfied’ and quicker to terminate their search? We are hesitant to make any strong
conclusions from these data since they are complicated by the fact that one trial-type involves an extra mouse-click and that
mouse-clicks in the fashion used here are not very sensitive. Only Experiment 4 (which did not reveal SOS) and Experiment
6 (which revealed a weak effect) had significantly faster responses for high-salience single target trials than no-target trials.
Thus, these data reveal little evidence of a truncated search but likely reveal little reliable evidence in general. As a result,
we focus on accuracy as our primary dependent measure.
70 FLECK, SAMEI, AND MITROFF
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Appendix B
Accuracy Data for High-Salience Targets
Table B1
Accuracy Data for High-Salience Targets on Single- and Dual-Target Trials in Experiments 1–8
(Experiments 9 and 10 Did Not Include High-Salience Targets)
Experiment
Experimental parameters Accuracy (%)
Time limit
Prevalence
high:low
Trial type
counts High single High dual
1 15 s 1:1 High: 50 88.40 90.19
Low: 50 (8.42) (7.63)
High-Low: 50
None: 100
2 15 s 2:1 High: 100 94.80 90.02
Low: 50 (5.57) (9.72)
High-Low: 50
None: 50
3 15 s 3:1 High: 120 95.66 96.07
Low: 40 (3.42) (3.83)
High-Low: 40
None: 50
4 30 s 2:1 High: 100 94.20 92.64
Low: 50 (4.10) (6.55)
High-Low: 50
None: 50
5 30 s 3:1 High: 120 95.74 92.13
Low: 40 (2.35) (8.25)
High-Low: 40
None: 50
6 30 s 6:1 High: 150 93.26 96.08
Low: 25 (5.77) (5.48)
High-Low: 25
None: 50
7 Luggage line 3:1 High: 96 97.10 95.32
(variable rate) Low: 32 (4.60) (6.12)
High-Low: 32
None: 40
8 Luggage line 3:1 High: 96 84.91 76.22
(constant rate) Low: 32 (7.81) (16.15)
High-Low: 32
None: 40
Note. Experiments 1–8 included high-salience targets to calibrate the low-salience targets. However, since the dual-target
trial counts were matched to the number of low-salience single-target trials, we did not analyze the two-target effects of
salience, nor explore interactions between salience and other factors such as frequency or time limit. We provide the data
here for completeness.
Received April 17, 2009
Revision received October 13, 2009
Accepted November 2, 2009
DUAL-TARGET SEARCH ACCURACY 71
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

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