Attention and driving performance in Alzheimer's disease.

Abstract

The present study examined the relationship between visual attention measures and driving performance in healthy older adults and individuals with very mild and mild dementia of the Alzheimer type (DAT). Subjects were administered an on-road driving assessment and three visual attention tasks (visual search, visual monitoring, and useful field of view). The results indicated that error rate and reaction time during visual search were the best predictors of driving performance. Furthermore, visual search performance was predictive of driving performance above and beyond simple dementia severity and several traditional psychometric tests. The results suggest that general cognitive status may be useful for identifying individuals "at risk" for unsafe driving. However, measures of selective attention may serve to better differentiate safe versus unsafe drivers, especially in the DAT population.

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Journal of Gerontology: PSYCHOLOGICAL SCIENCES
1998, Vol. 53B, No. 2, P130-P141
Copyright 1998 by The Gerontological Society of America
Attention and Driving Performance
in Alzheimer's Disease
Janet M. Duchek,1 Linda Hunt,2 Karlene Ball,3 Virginia Buckles,14 and John C. Morris14
'Washington University School of Medicine, St. Louis, Missouri.
2Maryville University, St. Louis, Missouri.
3University of Alabama at Birmingham.
4Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, Missouri.
The present study examined the relationship between visual attention measures and driving performance in healthy
older adults and individuals with very mild and mild dementia of the Alzheimer type (DAT). Subjects were adminis-
tered an on-road driving assessment and three visual attention tasks (visual search, visual monitoring, and useful
field of view). The results indicated that error rate and reaction time during visual search were the best predictors of
driving performance. Furthermore, visual search performance was predictive of driving performance above and be-
yond simple dementia severity and several traditional psychometric tests. The results suggest that general cognitive
status may be useful for identifying individuals "at risk" for unsafe driving. However, measures of selective atten-
tion may serve to better differentiate safe versus unsafe drivers, especially in the DAT population.
T
tant functional activity which promotes independence in
older adults, there are concerns regarding driving safety in
this group. Taking into consideration that older drivers
travel fewer miles than younger drivers and tend to drive in
"low-risk" situations (e.g., daylight, good weather), the ac-
cident and fatality rates for older drivers are considerably
higher than those for other age groups (Evans, 1988). Fur-
thermore, older individuals with cognitive deficits are at a
greater risk for impaired driving (Drachman & Swearer,
1993). Thus, there is an increasing recognition of the need
for public policy to determine "driving fitness."
Clearly, age-associated illnesses that affect cognition,
such as Alzheimer's disease, would be expected to impair
driving ability. Retrospective studies that have examined
the relationship between dementia of the Alzheimer type
(DAT) and driving performance based upon informant re-
port suggest that dementia is associated with unsafe driving
(Coyne, Feins, Powell, & Joslin, 1990; Friedland et al.,
1988; Lucas-Blaustein, Filipp, Dungan, & Tune, 1988). For
example, Friedland et al. found that 77% of the informants
reported that the DAT individuals had shown a change in
driving performance over the past 5 years and that individ-
uals with DAT were five times more likely to be involved in
a motor vehicle accident than healthy age-matched con-
trols. Furthermore, 58% of the DAT individuals who had
stopped driving during this time did so only after having an
accident.
There have been a few prospective studies of actual on-
the-road driving performance in Alzheimer's disease. Fitten
et al. (1995) found that individuals with mild DAT per-
formed significantly worse on a road test than either a
HE older driver represents the fastest growing segment
of the driving population. While driving is an impor-
group of healthy control subjects or diabetic control sub-
jects. Specifically, the DAT group drove more slowly and
made more errors (e.g., driving into a street marked "do not
enter") particularly in the more complex stage of the driv-
ing assessment. The driving scores on the road assessment
in this study were correlated with the number of collisions
and moving violations during the preceding 2 years. In an-
other study of actual driving performance in DAT, Hunt,
Morris, Edwards, and Wilson (1993) found that driving
performance was related to dementia severity. In this study,
individuals with mild DAT were more likely to fail the
driving assessment than individuals with very mild DAT or
healthy controls. Likewise, in an expanded longitudinal
study, Hunt et al. (1997) reported that only 3% of the
healthy controls failed an on-the-road driving test, whereas
19% of the very mild DAT group failed the driving test,
and 41% of the mild DAT group failed the driving test.
These studies indicate that there is a relationship be-
tween impaired driving abilities and DAT based upon both
informant reports of motor vehicle accidents and moving
violations, and actual on-road driving performance. How-
ever, it is important to note that not all DAT individuals ex-
hibit poor driving performance. That is, 59% of the mild
DAT and 81% of the very mild DAT individuals in the
Hunt et al. (1997) study did not fail the driving test and
hence appear to retain relatively safe driving skills, at least
in the early stages of the disease. Given that driving repre-
sents a highly automatized skill, it is possible that driving
behaviors would not show a decline in the early stages of
the disease, because some relatively automatic tasks appear
to be well preserved in DAT (e.g., see Balota & Duchek,
1991). Thus, diagnosis of DAT alone may not serve as the
best predictor of driving (see Drachman, 1988).
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ATTENTION AND DRIVING IN DAT
P131
It has been suggested that cognitive screening measures
should be developed to predict driving ability and identify
individuals with DAT who are at risk for unsafe driving
(e.g., Cushman, 1996; Hunt et al., 1993; Kaszniak, Keyl, &
Albert, 1991). In this light, many of the above-mentioned
studies also have attempted to examine performance on var-
ious cognitive and neuropsychological measures in relation
to driving. For example, in the Fitten et al. (1995) study,
Sternberg memory search (Sternberg, 1975), visual track-
ing, and Mini-Mental State Examination (Folstein, Folstein,
& McHugh, 1975) scores were best correlated with driving
scores relative to other cognitive measures (together ac-
counting for 68% of the variance in drive scores). Because
driving is a highly complex skill that involves visual, motor,
and cognitive functioning, it would seem paramount to un-
derstand how specific skills and processes influence driving
performance to better identify screening measures, which
would be predictive of unsafe driving in the older adult and
DAT population.
Aspects of attentional processing have been identified in
the literature as related to driving. For example, Shinar
(1978) suggested that 25-50% of accidents are due to driver
"inattention." In an earlier study, Kahneman, Ben-Ishai, and
Lotan (1973) found that errors of shifting attention in a di-
chotic listening task were the best predictor of accidents in a
group of commercial bus drivers (also see Mihal & Barret,
1976). In reference to older drivers, studies by Ball and her
colleagues have found that a measure of visual attention
(i.e., the size of the useful field of view; UFOV) is a rela-
tively strong predictor of driving ability and vehicle acci-
dents (Ball & Owsley, 1991; Ball, Owsley, Sloane, Roenker,
& Bruni, 1993; also see Cushman, 1996). The UFOV refers
to the visual spatial area functionally available in a single
glance (without head or eye movements) for a given visual
task, such as target identification or localization. Owsley,
Ball, Sloane, Roenker, and Bruni (1991) found that the
UFOV followed by general mental status were the best pre-
dictors of accident frequency in a sample of older adults (to-
gether accounting for 20% of the variance). Furthermore,
older adults who failed the UFOV task in this study had ?>-A
times more accidents overall and-15 times more intersection
accidents than older adults who passed the UFOV task. In a
prospective study utilizing a structural equation (LISREL)
model, only the UFOV directly predicted crash frequency
(Owsley, Ball, Sloane, Overley, & White, 1994). Measures
of visual sensory function (e.g., acuity, contrast sensitivity,
glare sensitivity) were only indirectly related to crash fre-
quency and were mediated by the UFOV.
Based upon the above-mentioned studies, it appears that
measures of attention, especially visual attention, are related
to driving in the older adult population. Clearly, attentional
deficits are present in DAT, even in the earlier stages of the
disease when individuals are more likely to be still driving
(Parasuraman & Nestor, 1991). Deficits in selective atten-
tion have been reported in DAT across various tasks, such as
Stroop (Spieler, Balota, & Faust, 1996), dichotic listening
(Grady et al., 1989), and visual search (Nebes & Brady,
1989). Given that individuals with DAT exhibit deficits in
attentional processing, and performance in some attentional
tasks has been shown to be predictive of accidents in older
adults, it is plausible that attentional deficits may be linked
to impaired driving performance in DAT. In fact, in the Hunt
et al. (1993) study of driving in DAT, a simple paper/pencil
test of switching attention was highly related to driving per-
formance relative to other clinical measures.
The purpose of the present study was to examine the rela-
tionship between specific aspects of visual attention perfor-
mance and actual on-road driving performance in healthy
aging and various stages of DAT. The inclusion of two levels
of DAT (i.e., very mild and mild) is important in understand-
ing how aspects of attention may be related to driving per-
formance as the disease progresses, particularly because
driving represents a relatively automatized skill that may not
decline in the earliest stages of the disease (e.g., Hunt et al.,
1997). This study was conducted as part of a larger longitu-
dinal research project examining driving in DAT.
In this study, healthy older adults, individuals with very
mild DAT, and individuals with mild DAT were adminis-
tered three visual attention tasks (visual search, visual mon-
itoring, UFOV) and an on-road driving test. The visual at-
tention tasks were chosen to reflect various aspects of
attentional processing which are important to the driving
situation. The visual search task was employed as a mea-
sure of selective attention and the ability to select a target
from an array of distractors. In this task, subjects make a
speeded decision as to whether or not a target letter is pre-
sent in an array of 2, 4, or 6 letters. Clearly, the typical driv-
ing situation involves constant monitoring of the environ-
ment and the selection of relevant information from a
myriad of distracting information. The visual monitoring
task was used to examine the ability to sustain and divide
attention and detect infrequent changes in a visual display.
In this task, subjects make a response whenever they detect
an "X" either in 1 or 2 scrolling columns of "Os." Clearly,
efficient vigilance is necessary during prolonged periods of
driving. The UFOV task, as described above, was utilized
because it represents attentional processing at an early pre-
attentive level and has been linked to accident frequency in
older adults (e.g., Owsley et al., 1991).
Two basic issues were addressed in this study. First, to
determine the impact of DAT on visual attention processing
and driving, we examined performance on the three atten-
tion tasks and the driving task as a function of dementia
severity in a series of ANOVAs. Second, to examine the re-
lationship between attentional performance and driving per-
formance in these three groups of subjects, regression anal-
yses were performed utilizing the various attentional
measures as predictors of on-road driving performance. In
addition, various psychometric tests that are routinely ad-
ministered to all the study participants also were included
in the regression analyses to determine whether the specific
measures of visual attention were better predictors of driv-
ing performance than other more general measures of cog-
nitive processing.
METHOD
Subjects
Three groups of participants were included in this study:
healthy controls, individuals with very mild DAT, and indi-

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DUCHEKETAL.
viduals with mild DAT. All participants were still driving at
the time of the study, had a valid driver's license, had at
least 10 years of driving experience, and had corrected vi-
sual acuity of at least 20/50. Participants were recruited
from the Alzheimer's Disease Research Center (ADRC) at
Washington University School of Medicine and were origi-
nally screened for depression, hypertension, reversible de-
mentias, and other disorders that could potentially produce
cognitive impairment. The inclusionary and exclusionary
criteria for DAT are consistent with the criteria of the Na-
tional Institute of Neurological and Communicative Disor-
ders and Stroke and the Alzheimer's Disease and Related
Disorders Association (NINCDS-ADRDA; McKhann,
Drachman, Folstein, Katzman, Price, & Stadlan, 1984). The
severity of dementia was staged according to the Washing-
ton University Clinical Dementia Rating (CDR) Scale
(Hughes, Berg, Danziger, Coben, & Martin, 1982; Morris,
1993). According to this scale, CDR 0, 0.5, and 1 represent
no dementia, very mild dementia, and mild dementia, re-
spectively. The CDR is based on a 90-minute interview
with both the subject and a collateral source. This interview
assesses the subject's cognitive abilities in the areas of
memory, orientation, judgment and problem solving, com-
munity affairs, home and hobbies, and personal care. Both
the reliability of the CDR and validation of the diagnosis
with this research team have been excellent and well-
documented (Berg et al., 1990; Burke et al., 1988; Morris,
McKeel, Fulling, Torack, & Berg, 1988; Morris et al.,
1991). Presently, 97% of the individuals diagnosed with
DAT have had Alzheimer's disease confirmed upon autopsy
(Berg & Morris, 1994).
As part of the larger longitudinal study, 58 CDR 0s, 49
CDR 0.5s, and 29 CDR Is performed the on-road driving
test. However, a different number of subjects from each CDR
group participated in the three visual attention tasks for vari-
ous reasons, such as the difficulty of the particular task, time
constraints, and when the task was initiated in the larger
study. The UFOV was initiated later in the study and results
are available only from 28 CDR 0s, 21 CDR 0.5s, and 6
CDR Is for the UFOV task. It was clear that the present ver-
sion of the UFOV task utilized in this study was very difficult
for the CDR Is to perform. Specifically, the CDR Is had dif-
ficulty performing both the central and the peripheral tasks
and utilizing the touch screen to make their responses. There
were 36 CDR 0s, 34 CDR 0.5s, and 18 CDR Is for the visual
monitoring task, and there were 47 CDR 0s, 37 CDR 0.5s,
and 18 CDR Is for the visual search task. Neither the visual
monitoring nor the visual search task appeared to be too dif-
ficult for the DAT individuals to perform.
Given that all subjects who participated in the on-road
driving test did not perform all the visual attention tasks, it
is important to determine whether the missing subjects'
driving performance was different from those subjects who
participated in the various attention tasks. There were no
apparent differences in mean scores on the driving test for
those CDR 0 subjects who did versus did not perform the
UFOV task (103 vs 101, respectively, p = .25), the visual
monitoring task (102 vs 102, p = .83), and the visual search
task (102 vs 101, p = .34). It appears there was no differ-
ence in mean drive scores for those CDR 0.5 subjects who
did versus did not perform the UFOV task (102 vs 99, re-
spectively, p = .16), but there were differences in the visual
monitoring task (101 vs 95, p < .03) and the visual search
task (101 vs 95, p < .03). Also, there was no significant dif-
ference in mean drive scores for those CDR 1 subjects who
did versus did not perform the UFOV task (98 vs 91, re-
spectively, p = .20), but there were differences in the visual
monitoring task (96 vs 86, p < .03), and the visual search
task (96 vs 86, p < .03). Thus it appears that both CDR 0.5
and CDR 1 subjects who did not perform the visual moni-
toring and visual search tasks had overall lower drive scores,
suggesting that these subjects had a slightly poorer cognitive
status than those who performed these attention tasks.
Visual Attention Tasks
All three visual attention tasks were presented on a com-
puter monitor, which both controlled stimulus presentation
and recorded subjects' responses. The UFOV task was con-
trolled by a Model 2000 Visual Attention Analyzer. Both
the visual monitoring and visual search tasks were con-
trolled by an IBM compatible computer that measures re-
sponse latency to the nearest millisecond.
Visual search task.—The visual search task examined the
ability to select a target in a visual array. In this task, a fixa-
tion mark (+) was presented in the center of the screen for 1
second followed by a target letter which was presented for
1 second. After the target letter was presented, a visual
array of either 2, 4, or 6 letters was presented immediately
in the center of the screen. The letters in the array could ap-
pear in 6 potential positions (2 rows of 3 positions). The 3
positions in a row were spaced 1.28 cm apart and the 2
rows were double-spaced. All letters were presented in 12
point font (approximately 0.3 degrees of visual angle) and
the width of the visual array subtended approximately 3 de-
grees of visual angle. The letters in the visual array oc-
curred across the 6 potential positions as equally often as
possible. Half of the time the target letter was present in the
array and half of the time the target was not present in the
array. The subject responded yes or no as to whether the
target was present in the array by pressing the designated
key on the computer. The visual array stayed on the screen
for 5 seconds or until the subject made a response. There
were 20 yes trials and 20 no trials for each visual display
(2, 4, or 6), yielding a total of 120 trials. Trial types were
randomly presented. Both response latency and accuracy
were measured. Practice trials were given before the test
trials to ensure that subjects understood the task.
Visual monitoring task.—The visual monitoring task ex-
amined the ability to sustain and divide attention and detect a
change in a visual display. In the first half of this task, a col-
umn of "O's" scrolled up the screen at a constant rate for ap-
proximately 7 minutes. At varying intervals an "X" appeared
in the column and the subjects' task was to press a key on the
computer whenever they detected an 'X" The onset of the
"X" was randomly varied to occur within a 3-10 second time
frame. The response latency to the "X" was measured from
onset up to 2 seconds after the "X" occurred. When the sub-
ject did not make a response within the 2-second time frame,

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ATTENTION AND DRIVING IN DAT
P133
this was scored as a "miss." A "false alarm" was scored if the
subject made a response outside the 2-second time frame. In
the first half of the task, an "X" appeared 70 times (approxi-
mately 8% of the stimulus events). In the second half of the
task, the subject monitored two columns of scrolling "Os"
for approximately 7 minutes. The two columns were spaced
approximately 15 cm apart and required eye movements
from one column to another to monitor the events. At differ-
ent varying intervals, an "X" appeared in either column and
the subject pressed a different designated key on the com-
puter for each column. In the second half of the task, an "X"
appeared 35 times in each column (approximately 4% of the
stimulus events per column). Throughout the entire task, the
letters (Xs and 0s) were presented in 12-point font at a rate
of 2 letters/second. Each letter subtended approximately 0.3
degrees of visual angle. Both response latency and accuracy
were measured. Practice trials were given before the test tri-
als to ensure that subjects understood the task.
Useful field of view.—The UFOV task consisted of a cen-
tral identification task and a peripheral localization task.
The UFOV task was presented as three subtests. In the first
subtest, a central stimulus (a graphic representation of ei-
ther a car or truck) was briefly flashed at varying durations
in the center of the screen and the subject had to make a
correct identification by pointing to either a car or truck on
the touch screen. This comprised the central task. In the
second subtest, the subject performed the same central task
and also had to localize a peripheral target. The peripheral
target was presented unpredictably at one of eight different
meridians at either 10, 20, or 30 degrees eccentricity at the
same time the central stimulus was presented. The duration
of the display was varied to measure the speed of visual
processing for this divided attention task. When the central
and peripheral stimuli went off the screen, the subject iden-
tified the central stimulus on the touch screen and then lo-
calized the peripheral target by touching the correct posi-
tion on a spoke that appeared on the screen. In the third
subtest, the same central and peripheral stimuli were pre-
sented, but the peripheral target was embedded in a series
of distractors (selective attention).
For subtest 1, the minimum duration at which subjects
could perform the task with 75% accuracy was measured.
For subtests 2 and 3, the best fitting line reflecting the rela-
tionship between eccentricity and localization errors was first
computed for each test duration, and the size of the UFOV
was defined for that stimulus duration as that eccentricity at
which the participant could localize the peripheral target cor-
rectly 50% of the time. Performance in each of the three sub-
tests was then scaled, in each case along a duration contin-
uum, to arrive at three scores representing the extent of
difficulty with respect to speed of processing (subtest 1), di-
vided attention (subtest 2), and selective attention (subtest 3).
These scores ranged from 0 (no problem) to 30 (great diffi-
culty). For example, within subtest 1, if a participant was un-
able to identify the central target correctly 75% of the time at
the longest stimulus duration (240 ms), that participant re-
ceived a score of 30 on subtest 1. If, however, this 75% crite-
rion was reached at an average of less than 40 ms duration,
then the scaled value was 0. Similarly, in subtests 2 and 3, if
the computed size of the UFOV was less than 5 degrees at
240 ms, then the maximum score of 30 was assigned. How-
ever, if the computed size of the UFOV was 30 degrees at a
40 ms duration, then a score of 0 was assigned. To summa-
rize performance, the three scaled scores are summed to
yield a composite score between 0 and 90, which represents
the total percentage reduction of the UFOV.
Driving Test
All subjects were administered the Washington University
Road Test (WURT) (see Hunt et al, 1997). The WURT is a
45-minute in-traffic road test along a predetermined route.
The test was given to all participants in a standard car with
dual brakes at approximately the same time of day in good
road conditions. A driving instructor was seated in the front
and an investigator was seated in the back of the car. Both
the driving instructor and investigator were unaware of the
CDR of the subject and the performance of the individual on
the visual attention tasks. The WURT consisted of two test-
ing components: a closed course and an open course test.
The closed course test was given in a large parking lot. Sub-
jects were familiarized with the controls of the car and asked
to perform various basic maneuvers, such as starting the car,
driving forward, stopping, and making a left turn. Each of
these behaviors was scored as either "pass" or "fail." If both
the driving instructor and investigator agreed that the subject
passed the items on the closed course, they then proceeded
to the open course test. All subjects in the study passed the
closed course test. The open course test was conducted in
traffic and assessed several typical driving skills such as
maintaining speed, obeying traffic signs, signaling, turning,
changing lanes, and negotiating intersections. The investiga-
tor scored these skills and maneuvers along the route. Each
skill was scored oh a 2- or 3-point scale, thus yielding a total
quantitative driving score, ranging from 0-108 (perfect per-
formance). The test-retest reliability for the WURT is r = .76
(see Hunt et al., 1997).
Psychometric Testing
Each participant from the ADRC was administered a 2-hr
comprehensive psychometric battery that assesses various
aspects of memory, psychomotor performance, and lan-
guage. Memory and language were assessed with the
Boston Naming Test and the Wechsler Memory Scale (Log-
ical Memory, forward and backward digit span, Paired As-
sociate Learning, Mental Control; Wechsler & Stone,
1973), the Benton Visual Retention Test (BVRT) picture
memory (Benton, 1963), and the Word Fluency Test (Thur-
stone & Thurstone, 1949). Intelligence was assessed with
the following subtests of the Wechsler Adult Intelligence
Scale (WAIS): Information, Block Design, and Digit Sym-
bol (Wechsler, 1955). Perceptual motor performance was
assessed with the BVRT copy test and Part A of the Trail
Making Test (Armitage, 1946).
RESULTS
Data Analysis
The data analysis for this study was organized to address
two basic issues. First, we examined the effect of dementia

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DUCHEKETAL.
severity on visual attention, psychometric, and driving per-
formance. Separate analyses of variance (ANOVAs) were
performed for each of the visual attention and psychometric
tasks and the driving test with subject group as a between-
subject factor. These analyses provide data on the range of
performance of the different subject groups on the targeted
tasks. Second and more important, we examined whether
aspects of visual attention and psychometric test perfor-
mance were predictive of on-road driving performance. A
series of regression analyses were performed with perfor-
mance on the attention tasks and psychometric tests as pre-
dictor variables and driving performance as the dependent
measure.
ANOVAs on the Attention, Psychometric, and Driving Tasks
Visual search task performance and dementia severity.—
Figure 1 displays response latencies in the visual search
task as a function of CDR, set size, and response type. The
results of a 3 (CDR) X 3 (set size) X 2 (response type)
ANOVA yielded three significant main effects of CDR,
F(2,99) = 18.52, MSe = 10968239, p < .0001; set size,
F(2,198) = 246.62, MSe = 7213960, p < .0001; and re-
sponse type, F(l,99) = 220.59, MSe = 10378117, p <
.0001. These main effects were qualified by three signifi-
cant interactions. First, there was a set size X response type
interaction typically found in visual search tasks indicating
that the slope of the no response trials, as a function of set
size, was greater than the slope of the yes response trials,
F(2,198) = 92.77, MSe = 1492196, p < .0001. This pattern
of data is consistent across CDR groups suggesting that all
groups were performing a self-terminating search (Stern-
berg, 1975). Second, there was a significant CDR X set size
interaction, F(4,198) = 3.31, MSe = 96725, p < .02, which
indicated that the CDR 1 group was particularly slower
with the larger set size 6 relative to the CDR 0.5 and CDR 0
groups. Third, there was a significant CDR X response type
interaction, F(2,99) = 7.11, MSe = 334579.3, p < .002,
which indicated that the CDR 1 group was particularly
slower when making a no response than a yes response rel-
ative to the CDR 0.5 and CDR 0 groups.
Error rates for the visual search task are presented in Fig-
ure 2 as a function of CDR, set size, and response type. A 3
(CDR) X 3 (set size) X 2 (response type) ANOVA yielded
significant main effects of CDR, F( 2,99) = 9.47, MSe =
4625.9, p = .0002; set size, F(2,198) = 7.10, MSe = 182.3,
p < .002; and response type, F(l,99) = 3.93, MSe = 282.2,
p = .05. The analysis also yielded two significant interac-
tions: CDR X set size, F(4,198) = 2.96, MSe = 75.9, p <
.03; and CDR X response type, F(2,99) = 3.45, MSe =
247.8, p < .04. These interactions were qualified by a sig-
nificant CDR X set size X response type interaction,
F(4,198) = 2.72, MSe = 74.0, p < .04, which indicated that
the CDR 0 group made more errors on yes response trials
Visual Search
2500 T
2300 -•
2100-
1900 -•
1700-
1500-
1300-
1100 -
900-
700-
500
o-
O
•
CDR 0 - yes
CDR 0 - no
- • 0 - CDR0.5-yes
- • • - CDR 0.5-no
--O--CDR1 -yes
• ••CDR1 -no
6
Set Size
Figure 1. Visual search response latencies (ms) as a function of set size, response type, and CDR (Clinical Dementia Rating).