Aging, Neuropsychology, and Cognition
1998, Vol. 5, No. 2, pp. 129-146
1382-5585/98/0502-129$12.00
© Swets & Zeitlinger
Differential Decline of Verbal and Visuospatial Processing
Speed Across the Adult Life Span*
Bonnie Lawrence, Joel Myerson, and Sandra Hale
Washington University
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ABSTRACT
The present study compared the age-related decline in verbal and visuospatial processing speed in 131
participants aged 18 to 90 years. Participants performed four verbal and four visuospatial tasks. Age differences in processing speed were compared at the group and individual levels. For the group-level analyses,
participants were divided into a young adult group and six older groups subdivided by decade. The mean
verbal and visuospatial response times (RTs) for each group were regressed on the corresponding RTs of
the young adult group. The slope of the visuospatial regression was greater than that for the verbal regression at all ages, and the difference between the visuospatial and verbal slopes increased with each decade.
For the individual-level analyses, a verbal and a visuospatial processing-time coefficient (Hale & Jansen,
1994) was obtained for each individual, and these values were then regressed on age. Verbal processing
time increased linearly by approximately 50% while visuospatial processing time increased exponentially
by approximately 500% from 18 to 90 years. Taken together, the results at both the group and the individual level demonstrate that aging affects visuospatial processing to a much greater extent than verbal processing.
The differential decline of scores on tests involving verbal and visuospatial information
processing is well documented. Most notably,
the age-related differential decline on the verbal
and visuospatial subtests of the Wechsler Adult
Intelligence Scale – Revised (WAIS-R) appears
to begin as early as age 40, with visuospatial
measures (e.g., block design) declining to a
much greater extent than verbal measures (e.g.,
vocabulary) thereafter (Wechsler, 1981). However, the proper interpretation of this pattern of
decline has long been a matter of dispute. This
is because a direct comparison may be confounded by the speeded nature and greater novelty of the more visuospatial subtests and the
nonspeeded nature and greater reliance on previously learned information of the more verbal
subtests.
More recently, several studies have compared
the effects of age on verbal and visuospatial pro*
cessing using experimental designs that do not
confound the nature of the task with the type of
information to be processed (Hale & Myerson,
1996; Lima, Hale, & Myerson, 1991; Myerson,
Hale, Rhee, & Jenkins,1998; Tubi & Calev,
1989). The results of these studies are consistent
with an interpretation that emphasizes the importance of the type of information (i.e., verbal
versus visuospatial) as a determinant of the
magnitude of the age difference. However, these
studies typically have compared verbal and
visuospatial processing in one younger and one
older group (e.g., 20-year-olds and 70-yearolds) and have not examined the development of
such age differences either longitudinally or
cross-sectionally. The goal of the present study
was to examine the development of differences
between verbal and visuospatial informationprocessing speed across the adult life span using
a cross-sectional design.
Address correspondence to: Bonnie Lawrence, Department of Psychology, Washington University, Campus Box
1125, St. Louis, MO 63130, USA. E-mail: [email protected].
Accepted for publication: May 20, 1998.
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130
BONNIE LAWRENCE ET AL.
Reports of age-related declines in processing
speed are ubiquitous in the literature (Birren,
1965; Cerella, 1990; Cerella, Poon, & Williams,
1980; Salthouse, 1985). Evidence suggesting
that aging is accompanied by some form of general slowing was provided originally by analyses
in which the mean response times (RTs) of older
adults were plotted as a function of the mean
RTs of young adults on the same tasks. This
method of inspection, known as the Brinley plot
(Brinley, 1965), typically yields slope coefficients greater than 1.0 with single linear functions accounting for over 90% of the variance in
older adults’ RTs. The slope coefficients have
been assumed to measure task-independent aspects of cognitive speed (Cerella, 1985; Hale &
Jansen, 1994; Lima et al., 1991) and the large
proportion of variance accounted for is often
interpreted as consistent with the general slowing hypothesis. According to this hypothesis,
age-related slowing is not localized within any
specific cognitive process but affects all processes to approximately the same degree.
Rather than being completely global, Hale
and Myerson and their colleagues (Hale &
Myerson, 1996; Lima et al., 1991) have suggested that slowing may be general within particular cognitive domains (e.g., verbal and
visuospatial). Based on analyses of both experimental and meta-analytic data, Hale and colleagues concluded that the relationships between
young and older adults’ RTs on lexical (i.e., verbal) and nonlexical (i.e., visuospatial) information-processing tasks were more accurately described by separate, domain-specific regression
lines than by a single, general regression line.
More specifically, according to the Lima et al.
meta-analysis, the slope of the function describing the verbal data was approximately 1.5,
whereas the slope of the function describing the
visuospatial data was approximately 2.0. Importantly, slowing within each domain appears to be
general as the RTs from each domain are well fit
by a single regression line. Slope coefficients
for verbal and visuospatial RTs similar to those
reported by Hale and colleagues have been reported elsewhere in the literature (e.g., Madden,
1992; Sliwinski & Hall, 1998), and evidence for
additional domains involving short-term or
working memory processes has also been described (Mayr & Kliegl, 1993; Sliwinski & Hall,
1998; Swearer & Kane, 1996).
Some previous cross-sectional research suggests that aging is accompanied by linear increases in verbal RTs (Madden, 1992), whereas
other research suggests that both verbal and
visuospatial RTs increase in an exponential
manner (Cerella & Hale, 1994). Cerella and
Hale recently proposed a mathematical model of
changes in the speed of information processing
across the life span. According to their model, a
negative exponential function describes processing speed changes throughout childhood and a
positive exponential function describes processing speed changes throughout adulthood. Combining the two exponentials into one U-shaped
function, Cerella and Hale successfully characterized RT data taken from various studies that
examined changes in processing speed across
the life span.
There are limits to the conclusions that may
be drawn from directly comparing the results of
the Madden (1992) and Cerella and Hale (1994)
studies with respect to the effects of aging on
verbal and visuospatial processing speed. One
potential problem is that although Madden used
lexical decision tasks, a subset of the conditions
used visually degraded stimuli and thus may
have required visuospatial as well as verbal processing. Another potential problem is that although Cerella and Hale analyzed data from
both lexical and nonlexical tasks, they did not
test to see if the rates of change differed for the
two types of tasks. Moreover, an attempt to do
so based on published data would be complicated by the fact that these data sets are from
different samples, making it difficult to determine whether the results reflect differences in
the effects of aging on verbal and visuospatial
processing or sampling differences between the
studies. Therefore, the present study tested the
same participants on both verbal and visuospatial tasks in order to compare the form (i.e.,
linear or exponential) and the rate of the change
in verbal and visuospatial information processing across the adult life span.
131
VERBAL AND VISUOSPATIAL PROCESSING SPEED
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METHOD
Participants
The young adult control participants were 24 undergraduate students attending Washington University (M age = 19.7 years, SD = 0.95) who participated in the Hale and Myerson (1996) study.
Ninety-three adult participants were recruited from
a pool of volunteers maintained by the Aging and
Development Program at Washington University.
Seven of the 93 adults (M age = 66.6 years) were
excluded from analyses because of high error rates
(see Results) and an additional 3 participants were
excluded because of their health profiles (e.g.,
poor vision, childhood diabetes; M age = 66.7).
The data from 24 older adults (M age = 69.7 years,
SD = 3.5) who participated in the Hale and
Myerson (1996) study were combined with the
adult data collected for the current study, thereby
enabling us to obtain a large sample of older adults
(n = 107). A breakdown of participant characteristics is provided for each decade in Table 1. All
participants were screened for vision problems and
completed a health questionnaire. Overall, participants reported being in good to excellent health.
Verbal ability was assessed using the Shipley Vocabulary test from the Shipley Institute of Living
Scale. Raw scores on the Shipley Vocabulary were
regressed on age, revealing no significant age-related trend in performance, t(128) = 1.53, ns.
Materials
Stimuli were presented on a NEC MultiSynch 2A
monitor controlled by an IBM compatible 286
computer. Stimulus presentation was synchronized
with the refresh cycle for the monitor display, and
RTs were recorded with 1 ms accuracy. Participants used a response panel with three buttons arranged in an inverted triangle to indicate their decisions (the upper two buttons) and to initiate trials
(the lower button). Once a trial was initiated, the
stimulus remained on the screen until a response
occurred.
Design and Procedure
Four verbal tasks (single lexical decision, double
lexical decision, category judgment, and synonymantonym judgment) and four visuospatial tasks
(line-length discrimination, shape classification,
visual search, and abstract matching) were completed by all participants. These tasks were the
same as those completed by young adult and older
adult participants in the Hale and Myerson (1996)
study. For the purpose of completeness, an abbreviated description of the details of each task is provided below. Additional details are available in
Hale and Myerson.
Each participant received the same order of task
administration so that correlational analyses and
comparisons of subgroups of different ability levels would not be confounded by order differences.
The order of task administration (line-length discrimination, single lexical decision, visual search,
category judgment, shape classification, synonymantonym judgment, abstract matching, and double
lexical decision) distributed tasks from the two
domains evenly across the sessions. Additionally,
the order of administration of the tasks neither proceeded from easiest to most difficult nor vice
versa, so as not to confound either practice or fatigue with task complexity.
Each task began with six practice trials, followed by the experimental trials in which stimuli
from all of the conditions within a task were interleaved. The first two experimental trials were
‘‘buffer’’ trials (i.e., the RTs on these trials were
not included in the analyses). Not including the
buffer trials, there were 16 trials per condition,
with the exception of the different-shapes condition of the shape classification task which consisted of 32 trials (16 same-size, different-shape
trials and 16 different-size, different-shape trials).
The dominant hand was used to signal the following responses: word (vs. nonword), two words (vs.
one word + one nonword), same category (vs. different categories), synonym (vs. antonym), same
shapes (vs. different), target present (vs. absent).
Table 1. Participant Characteristics by Decade.
Decade
n
Sex
M age and SD
18–21
30–39
40–49
50–59
60–69
70–79
80–89
24
18
18
18
18
22
13
14 males
19 males
10 males
17 males
18 males
11 males
16 males
19.71 (0.95)
34.83 (2.43)
45.22 (2.82)
53.44 (2.41)
65.94 (2.73)
73.86 (2.90)
85.15 (2.64)
132
BONNIE LAWRENCE ET AL.
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For the line-length discrimination task, participants pressed the key corresponding to the location
of the longer line. For the abstract matching task,
participants pressed the key corresponding to the
location of the best match – upper left or upper
right.
Verbal battery
In the single lexical decision task, participants decided whether or not a consonant-vowel-consonant
letter string of high frequency (e.g., top), low frequency (e.g., rib), or a pronounceable nonword
(e.g., ged), is a word in English. In the double lexical decision task, participants decided whether or
not two strings of letters (presented one above the
other) were both words in English. This task consisted of four conditions: related word pairs (e.g.,
high and low), unrelated word pairs (e.g., bread
and queen), nonword-word pairs (e.g., jarton and
ink), and word-nonword pairs (e.g., flesh and
biper). In the category judgment task, participants
decided whether or not two words were from the
same semantic category. This task consisted of
four conditions: high-typicality word pairs from
the same category (e.g., ruby and emerald), lowtypicality word pairs from the same category (e.g.,
camel and buffalo), high-typicality word pairs
from different categories (e.g., dog and carrot),
and low-typicality word pairs from different categories (e.g., turnip and amethyst). In the antonymsynonym judgment task, participants decided
whether the meanings of two related words were
the same or opposite. The four conditions for this
task were as follows: synonym pairs made up of
high frequency words (e.g., loud and noisy), synonym pairs made up of lower frequency words
(e.g., grieve and mourn), antonym pairs made up
of high frequency words (e.g., hard and soft), and
antonym pairs made up of lower frequency words
(e.g., domestic and foreign).
Visuospatial battery
In the line-length discrimination task, participants
decided which of two lines was longer. This task
included two conditions: 10% and 20% difference
in line length. In the shape classification task, participants decided whether or not two objects were
the same shape, regardless of any difference in
size. This task included three conditions: same
shape and same size, same shape and different
sizes, and different shapes. In the visual search
task, participants decided whether a red square target was present in an array of red circles and green
squares. This task included six conditions: target
present and target absent conditions with three set
sizes of 9, 15, and 25 items. In the abstract matching task, participants selected one of two arrays
that was the best match to a third array which was
positioned below the two upper arrays. Each array
varied on four different dimensions (number,
shape, and color of the elements, and orientation of
the array) that could take on one of three values.
Specifically, each array consisted of either two,
three, or four shapes (circles, triangles, or squares)
that were colored either red, yellow, or blue, and
arranged either horizontally, vertically, or diagonally. On each trial, one and only one of the four
dimensions was relevant to determining the best
match. This task included four conditions: all three
irrelevant dimensions held constant, two irrelevant
dimensions held constant, one irrelevant dimension held constant, and no irrelevant dimensions
held constant.
RESULTS
The visuospatial tasks used in the present study
spanned a greater range of difficulty (as indexed
by mean RTs for the young adult group) than the
verbal tasks. For the young adult group, verbal
RTs ranged from 0.639 s on the easiest condition of the single lexical decision task to 1.279 s
on the most difficult condition of the synonymantonym judgment task (Table 2), whereas
visuospatial RTs ranged from 0.473 s on the easier condition of the line-length discrimination
task to 1.693 s on the most difficult condition of
the abstract matching task (Table 3). In order to
compare verbal and visuospatial processing
speed on tasks of equivalent difficulty (Fisk &
Fisher, 1994; Hale & Myerson, 1996), the following tasks and conditions were removed from
the visuospatial analyses: line-length discrimination (all conditions), shape classification (2
easiest conditions), and abstract matching (2
hardest conditions). As this left a much smaller
number of visuospatial than verbal conditions,
nonword conditions also were removed from
single and double lexical decision tasks to make
the number of visuospatial and verbal conditions
133
VERBAL AND VISUOSPATIAL PROCESSING SPEED
Table 2. Verbal Response Times (RTs), SDs, and Error Rates Across the Adult Life Span.
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Age group
Lexical decision
High frequency word
RT
SD
% Error
Low frequency word
RT
SD
% Error
Nonword*
RT
SD
% Error
Double lexical decision
Related words
RT
SD
% Error
Word-nonword*
RT
SD
% Error
Unrelated words
RT
SD
% Error
Nonword-word*
RT
SD
% Error
Category membership
High typicality – same
RT
SD
% Error
High typicality – different
RT
SD
% Error
Low typicality – same
RT
SD
% Error
Low typicality – different
RT
SD
% Error
Young
30s
40s
50s
60s
70s
80s
0.639
0.092
3.6
0.691
0.126
1.0
0.720
0.104
1.7
0.751
0.127
0.3
0.840
0.181
0.3
0.883
0.200
0.3
1.004
0.205
1.4
0.719
0.120
12.8
0.822
0.191
6.3
0.870
0.115
7.6
0.910
0.204
7.3
1.032
0.304
4.9
1.098
0.343
8.0
1.210
0.389
8.2
0.774
0.151
2.7
0.900
0.206
4.5
0.949
0.164
3.5
0.983
0.213
1.9
1.198
0.277
6.1
1.342
0.355
3.7
1.221
0.232
7.0
0.771
0.129
1.3
0.898
0.223
2.1
0.825
0.111
0.7
0.928
0.174
0.3
0.943
0.168
0.3
1.020
0.197
0.3
1.129
0.211
1.0
1.078
0.184
7.0
1.237
0.257
5.6
1.150
0.136
4.9
1.220
0.267
4.9
1.230
0.203
3.8
1.448
0.481
3.7
1.455
0.307
1.9
0.892
0.147
1.8
1.031
0.244
1.0
0.982
0.108
1.0
1.069
0.210
0.3
1.059
0.180
0.0
1.243
0.285
0.6
1.306
0.271
1.4
0.897
0.182
3.1
1.061
0.251
0.3
0.950
0.173
3.1
1.053
0.286
3.1
1.038
0.225
1.7
1.234
0.463
4.0
1.281
0.244
3.8
0.949
0.179
5.7
1.134
0.266
2.8
1.087
0.143
4.2
1.203
0.249
4.2
1.357
0.360
5.2
1.456
0.297
2.0
1.677
0.378
7.7
1.016
0.184
1.3
1.249
0.272
0.7
1.225
0.150
0.7
1.255
0.226
0.7
1.290
0.217
0.7
1.475
0.353
1.1
1.608
0.323
1.0
1.122
0.230
4.4
1.409
0.504
2.4
1.272
0.205
3.8
1.358
0.309
2.4
1.442
0.339
3.5
1.610
0.396
3.4
1.750
0.451
7.7
1.235
0.281
3.1
1.520
0.393
1.7
1.434
0.180
2.4
1.481
0.302
1.4
1.520
0.300
0.7
1.704
0.465
0.6
1.834
0.447
1.0
Table continues.
134
BONNIE LAWRENCE ET AL.
Table 2. (continued).
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Age group
Synonym-antonym
High-frequency synonyms
RT
SD
% Error
High-frequency antonyms
RT
SD
% Error
Low-frequency synonyms
RT
SD
% Error
Low-frequency antonyms
RT
SD
% Error
Young
30s
40s
50s
60s
70s
80s
1.076
0.207
5.2
1.270
0.308
2.4
1.275
0.214
4.2
1.258
0.253
3.8
1.380
0.266
4.2
1.603
0.425
4.3
1.773
0.347
6.3
1.057
0.198
4.2
1.308
0.291
1.4
1.288
0.245
2.4
1.311
0.350
2.1
1.455
0.294
1.7
1.540
0.394
2.3
1.667
0.390
3.4
1.237
0.261
8.3
1.551
0.500
5.9
1.507
0.340
9.4
1.503
0.387
3.1
1.537
0.393
2.4
1.767
0.471
7.4
1.824
0.405
5.3
1.279
0.269
4.4
1.501
0.307
3.1
1.485
0.273
5.2
1.570
0.384
1.7
1.622
0.345
2.8
1.823
0.580
1.4
1.925
0.475
1.4
Note. Tasks and conditions that have been omitted from the regression analyses are marked with an asterisk.
more comparable.1 Thus, the data included in
the regression analyses consisted of 12 verbal
conditions (RTs ranging from 0.639 to 1.279 in
the young adult group) and 9 visuospatial conditions (RTs ranging from 0.621 to 1.298 in the
young adult group).
To assess whether there were age differences
in speed-accuracy trade-off in either domain,
mean verbal and visuospatial error rates were
calculated for each participant by collapsing
across conditions within a domain. When these
error rates were regressed on age, only the error
rate for the visuospatial tasks increased significantly, t(129) = 1.988, p < .05; the error rate for
the verbal tasks actually declined, t(129) =
1
The nonword conditions were selected for removal
because it has been suggested that RTs in such conditions reflect qualitatively different processes from
those in word conditions (e.g., Coltheart, Davelaar,
Jonasson, & Besner, 1977; Madden, Pierce, & Allen,
1993). That is, nonword RTs may represent the time at
which participants give up waiting to retrieve a word
(i.e., a strategic decision) as opposed to the time required for a specific cognitive process (i.e., lexical
access) to occur.
–2.725, p < .01. However, in both cases the
changes were extremely small: 0.16% per decade for the visuospatial and – 0.23% per decade
for the verbal. The increase in visuospatial errors was entirely attributable to lower accuracy
on the part of participants in their 80s. With this
group deleted from the analysis, the change in
visuospatial errors decreased with age (– 0.03%
per decade, p > .1). Verbal and visuospatial error rates for each condition are presented in Tables 2 and 3.
To test for the presence of differential slowing at the group level, the participants were divided into a young adult group (ages 18–21) and
six older groups, subdivided by decade (e.g.,
30–39 years, 40–49 years, etc.). For each decade, the mean RT was calculated for each experimental condition. The verbal and visuospatial RTs for each decade were then regressed
on the mean RTs of the young adult group from
the corresponding tasks and conditions (Fig. 1).
For all groups, linear functions fit to the verbal
and visuospatial RTs separately provided excellent descriptions of the data; all r2s > .92. Tests
for separate regressions revealed that the slopes
135
VERBAL AND VISUOSPATIAL PROCESSING SPEED
Table 3. Visuospatial Response Times (RTs), SDs, and Error Rates Across the Adult Life Span.
Age group
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Line-length discrimination*
10% difference*
RT
SD
% Error
20% difference*
RT
SD
% Error
Shape classification
Same shape – same size*
RT
SD
% Error
Same shape – different size*
RT
SD
% Error
Different shapes
RT
SD
% Error
Visual search
9 items – target present
RT
SD
% Error
9 items – target absent
RT
SD
% Error
15 items – target present
RT
SD
% Error
15 items – target absent
RT
SD
% Error
25 items – target present
RT
SD
% Error
25 items – target absent
RT
SD
% Error
Young
30s
40s
50s
60s
70s
80s
0.473
0.147
0.0
0.606
0.190
0.0
0.592
0.136
0.0
0.608
0.127
0.0
0.684
0.126
0.7
0.693
0.161
0.0
0.886
0.298
1.4
0.458
0.127
0.3
0.577
0.161
0.3
0.572
0.123
0.0
0.590
0.113
1.0
0.676
0.115
0.7
0.725
0.266
0.0
0.863
0.301
1.4
0.549
0.112
1.6
0.644
0.137
0.3
0.717
0.151
1.4
0.759
0.129
0.7
0.864
0.143
0.3
0.965
0.294
1.1
1.033
0.236
1.0
0.583
0.108
3.6
0.715
0.186
1.7
0.810
0.137
3.8
0.862
0.166
2.8
0.912
0.161
0.3
0.996
0.263
2.6
1.070
0.209
3.8
0.621
0.142
0.5
0.777
0.247
1.0
0.823
0.132
0.7
0.849
0.174
1.4
0.881
0.129
0.7
1.020
0.262
0.9
1.072
0.236
0.0
0.638
0.120
4.4
0.732
0.132
1.4
0.771
0.123
3.1
0.802
0.146
2.4
0.893
0.193
1.7
0.887
0.166
2.3
1.138
0.250
5.3
0.698
0.148
1.0
0.879
0.188
1.0
0.945
0.156
1.0
0.975
0.140
1.0
1.116
0.247
0.7
1.190
0.244
0.9
1.422
0.261
0.5
0.671
0.136
2.9
0.809
0.150
1.7
0.890
0.139
3.8
0.874
0.173
3.5
0.992
0.196
3.5
1.072
0.212
2.0
1.297
0.291
4.3
0.865
0.209
2.3
1.160
0.263
1.0
1.226
0.221
0.7
1.251
0.220
1.4
1.391
0.377
0.3
1.607
0.420
0.0
1.845
0.400
1.9
0.750
0.171
3.9
0.960
0.204
2.4
1.016
0.168
5.9
0.986
0.171
6.6
1.158
0.300
7.6
1.351
0.368
8.2
1.469
0.257
14.4
1.019
0.230
0.8
1.434
0.401
0.7
1.617
0.345
0.3
1.537
0.356
1.0
1.698
0.531
0.3
2.037
0.576
0.6
2.352
0.543
3.4
Table continues.
136
BONNIE LAWRENCE ET AL.
Table 3. (continued).
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Age group
Abstract matching
Level 1
RT
SD
% Error
Level 2
RT
SD
% Error
Level 3*
RT
SD
% Error
Level 4*
RT
SD
% Error
Young
30s
40s
50s
60s
70s
80s
0.920
0.175
1.7
1.182
0.223
2.9
1.193
0.176
1.6
1.400
0.308
1.3
1.712
0.474
1.6
2.013
0.606
1.9
2.801
0.860
8.1
1.298
0.334
7.8
1.774
0.518
6.3
1.847
0.333
6.3
2.280
0.741
5.2
2.888
1.028
4.1
3.225
1.051
7.6
4.875
1.867
9.2
1.471
0.389
5.2
2.055
0.500
3.1
2.081
0.472
2.1
2.430
0.840
8.3
3.239
1.342
6.9
3.662
1.188
7.7
5.643
1.460
17.3
1.693
0.477
3.6
2.411
0.736
2.4
2.421
0.620
3.5
2.717
0.696
8.3
4.085
1.534
5.2
4.443
1.321
8.2
6.458
1.918
15.4
Note. Tasks and conditions that have been omitted from the regression analyses are marked with an asterisk.
for the verbal and visuospatial regressions were
significantly different within each decade; all
ts(17) > 2.63, all ps <.05.
It should be noted that although the visuospatial slope coefficients for each decade increased systematically with increasing age, the
increase in verbal slope coefficients was less
systematic. That is, although the two oldest
groups (age 70 and above) showed the greatest
slowing on verbal tasks, the 30–39 group was
slower than either the 40–49, 50–59 or the
60–69 groups. Nevertheless, as may be seen in
Figure 2, the difference between the visuospatial
and verbal slope coefficients did increase systematically with each decade.
To test for the presence of differential slowing of verbal and visuospatial processing at the
individual level, the mean visuospatial and verbal RTs for each individual were regressed separately on the RTs of the young adult group from
the corresponding tasks and conditions. The median percentage of variance explained by the
regression of individual RTs on those of the
young adult group was 91% in the visuospatial
domain and 84% in the verbal domain (the semi-
interquartile ranges were 5.8% and 7.0%, respectively). The resulting visuospatial and verbal slopes may be termed processing-time coefficients (Hale & Jansen, 1994) and provide a
general processing speed index for each domain.
Consistent with the tests for separate regressions
on the group means, comparisons of individuals’
verbal and visuospatial processing-time coefficients revealed a systematic increase in the percentage of individuals with greater visuospatial
than verbal slowing (see Fig. 3); 52% (22/42) of
the participants from 19–39 years of age showed
greater visuospatial than verbal slowing as compared with 89% (79/89) of the participants from
40–90 years of age.
The age-related trend in processing-time coefficients was examined by plotting the verbal
and visuospatial processing-time coefficients for
each individual as a function of age. Polynomial
regressions were conducted on the verbal and
visuospatial coefficients separately to test for
the presence of a nonlinear age-related trend. A
significant quadratic trend was revealed for the
visuospatial processing-time coefficients (incremental F = 19.12, p < .001). The quadratic trend
137
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VERBAL AND VISUOSPATIAL PROCESSING SPEED
Fig. 1.
The mean verbal (Vrb) and visuospatial (Vsp) response times (RTs) for each decade plotted as a function of the mean RTs of the young adult group from the corresponding tasks and conditions. Filled
circles represent the visuospatial data and unfilled circles represent the verbal data. Solid lines represent
the verbal and visuospatial regression lines. In each panel, if performance of the older and young adult
groups were equal, the points would fall along the dashed line.
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138
Fig. 2.
BONNIE LAWRENCE ET AL.
The difference between visuospatial and verbal slope coefficients as a function of the mean age of each
decade group. The solid line represents the best-fitting polynomial function.
was not significant for the verbal processingtime coefficients (incremental F = < 1.0, ns), but
there was a significant linear trend (incremental
F = 6.51, p < .05). The quadratic equation that
best fit the visuospatial data was y = 1.668 –
0.047 x + 0.001x2. The linear equation that best
fit the verbal data was y = 0.992 + 0.005x.
Although the quadratic trend was not significant for the verbal processing-time coefficients,
both verbal and visuospatial processing coefficients were fit with an exponential function in
order to compare the rates of age-related change
as measured by the same mathematical model.
The exponential model was
slope = exp [d * (age – 20)]
(1)
in which d is the parameter governing the rate of
increase in processing time. This equation is
modified from Equation 2 of Cerella and Hale
(1994) so that slowing begins at age 20, the
mean age of the young adult group. (This modification was motivated by the following logic.
Because an individual’s RTs are regressed on
those of the 20-year-old group, the slope for an
average 20-year-old is 1.0 by definition. In
Equation 1, therefore, 20 is subtracted from a
participant’s age so that, because exp (0) = 1.0,
the predicted slope for an individual of age 20 is
1.0.) The rate parameter for the visuospatial
function (d = 0.024) differed significantly from
the rate parameter for the verbal function (d =
0.006), t(260) = 11.77, p < .0001. Based on
these parameter estimates, the predicted increase
in verbal processing time is nearly 50% from 20
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VERBAL AND VISUOSPATIAL PROCESSING SPEED
Fig. 3.
139
The percentage of individuals with visuospatial slope coefficients greater than their verbal slope coefficients slowing for each age group.
to 90 years. In contrast, the predicted increase in
visuospatial processing time is more than 500%
over the same period (Fig. 4).
DISCUSSION
In the present study, 131 adults from 18 to 90
years of age were tested on both verbal and
visuospatial information-processing tasks, and
age differences in processing speed were compared at both the group and individual levels. At
both levels of analysis, the data within a specific
domain (i.e., verbal or visuospatial) were well
described by a single regression line. At the
group level, for each decade in each domain, the
regression of the older group’s mean RTs on
those of the young adults accounted for at least
92% of the variance. At the individual level, the
median percentage of variance explained by the
regression of individual RTs on the mean RTs of
the young adult group was 91% in the visuospatial domain and 84% in the verbal domain.
Because the regression line typically accounted
for most of the variance in RTs, the slope of this
line was used as an index of processing speed in
both the visuospatial and verbal domains and at
both the group and individual levels (Hale &
Jansen, 1994).
Consistent results were obtained at both levels of analysis and revealed that although both
visuospatial and verbal processing time increased with age, the increase was far greater in
the visuospatial domain than in the verbal domain. At the group level, tests for separate regressions indicated that beginning with the
group of participants in their 40s, and for each
group thereafter, the slope coefficients for the
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140
Fig. 4.
BONNIE LAWRENCE ET AL.
Individual verbal and visuospatial slope coefficients plotted as a function of age. The solid lines represent the best-fitting exponential functions.
visuospatial regressions were significantly
greater than those for the verbal regressions. At
the individual level within each group, the percentage of participants whose visuospatial
slopes were greater than their verbal slopes increased as a function of age, beginning at 50%
for the young adult group and increasing to more
than 98% for all participants 60 years of age and
older.
Although the visuospatial slope coefficients
for each decade increased systematically with
increasing age, the increase in verbal slope coefficients was less systematic. That is, although
the two oldest groups (age 70 and above)
showed the greatest slowing on verbal tasks, the
next slowest group consisted of participants in
their 30s. It is possible that this pattern in the
verbal data reflects sampling differences between age groups, as those in their 30s tended to
be nearly as slow as those in their 40s on the
visuospatial tasks. However, because age differences in RTs on verbal tasks appear to be relatively small compared with individual differences, verbal speed measures may be particularly sensitive to sampling differences. One solution to the problem posed by such sampling
differences is to examine how the difference
between the visuospatial and verbal slope coefficients changes as a function of age, so that each
group’s visuospatial speed is assessed relative to
its own verbal speed. The results of such an
analysis revealed a systematic, nonlinear in-
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VERBAL AND VISUOSPATIAL PROCESSING SPEED
crease in the difference between visuospatial
and verbal slope coefficients across the life
span.
When individual visuospatial and verbal
slope coefficients were regressed on age, a similar pattern was observed. The verbal slopes increased in an approximately linear fashion with
age whereas the visuospatial slopes increased in
a positively accelerated fashion, so that the difference between the two increased nonlinearly.
In order to compare the age-related increases in
verbal and visuospatial processing time, the individual verbal and visuospatial slope coefficients were fit with an exponential model (Cerella & Hale, 1994), and the results of nonlinear
regression analyses revealed that visuospatial
processing time increased at a faster rate than
verbal processing time. More specifically, the
exponential rate parameter for visuospatial processing time was 0.024 whereas the exponential
rate parameter for verbal processing time was
0.006.
Although the difference between the two values may seem small, the process of exponential
increase is analogous to compound interest, and
this apparently small rate difference results in
large differences in the degree of age-related
slowing by the latter portion of the life span. For
the current data set, the predicted increase in
visuospatial processing time over the period
from 18 to 90 years of age is nearly 500%
whereas the predicted increase in verbal processing time over the same period is not quite
50%. Because the relation between the visuospatial RTs of older and young adults is positively accelerated (e.g., Hale, Myerson, Faust, &
Fristoe, 1995; Hale, Myerson, & Wagstaff,
1987), the RTs from the most difficult task (i.e.,
abstract matching) strongly influenced this estimate of the degree of visuospatial slowing.
However, even when the abstract matching data
were not included in the analysis, the oldest
group (i.e., those in their 80s) was still more
than 300% slower than the young adults in the
visuospatial domain.
The present results are consistent with the
experimental and meta-analytic findings of Hale
and colleagues suggesting that age-related declines in visuospatial processing speed are
141
greater than age-related declines in verbal processing speed (Hale & Myerson, 1996; Lima et
al., 1991). Importantly, the present results extend these previous findings with older adults
aged 65-75 years by demonstrating that the pattern of differential decline is evident perhaps as
early as the fourth decade and that the difference continues to increase through the ninth
decade. The present results are also consistent
with previous studies suggesting that the decline in visuospatial processing speed follows
an exponential time course (Cerella & Hale,
1994) whereas the decline in verbal processing
speed is more nearly linear (Madden, 1992).
However, these studies did not compare
changes in verbal and visuospatial processing,
and the present study is the first to examine the
time course of age-related slowing of verbal
and visuospatial processing in the same participants.
Although the function describing the decline
in visuospatial processing speed is clearly exponential in form (Cerella & Hale, 1994; the present study), the form of the function describing
life span changes in verbal processing speed is
not as easily characterized. Although a test for
the presence of a quadratic trend in the verbal
processing speed data from the present study
was not significant, to conclude that the decline
is linear would be to accept the null hypothesis.
Such conclusions are always unwarranted and
would be especially so in the present case for
two reasons: first, because of the obvious variability in verbal processing speed coefficients;
and second, because of the difficulty distinguishing linear and exponential trends when the
rate of change is low, as it is in the verbal domain.
For purposes of describing or predicting
changes in verbal processing speed, the present
data suggest that a linear model is as good as an
exponential one. For purposes of comparing
changes in the verbal and visuospatial domains,
however, an exponential model may be preferable as it appears to be appropriate in both domains whereas a linear model is only appropriate in the verbal domain. Ultimately, determination of the mechanism(s) underlying the differential decline in verbal and visuospatial process-
142
BONNIE LAWRENCE ET AL.
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ing speed may be required in order to decide
between alternative mathematical models of the
time course of age-related slowing.
Possible Mechanisms
The results of the present experiment appear to
parallel the well-documented differential decline
in scores on the more verbal and more visuospatial subtests of the WAIS-R (Wechsler,
1981), and thus the possible mechanisms that
have been proposed to explain age-related
changes in such test scores should also be considered as potential explanations for the present
findings. For example, it has been suggested that
from the standpoint of cognitive aging, the Performance-Verbal distinction may actually be a
speeded-nonspeeded distinction because many
of the Performance measures have a speed component whereas the Verbal measures are primarily nonspeeded (e.g., Mittenberg, Seidenberg,
O’Leary, & DiGiulio, 1989). However, the present results argue against such an interpretation
because there was clear evidence of greater declines in the visuospatial domain despite the fact
that both verbal and visuospatial processing
were assessed using speeded tasks.
It has also been suggested that the differential
decline on the Verbal and Performance subscales of the WAIS-R may reflect age-related
differences in the efficiency of processing familiar and unfamiliar information (Mittenberg et
al., 1989). Evidence against this interpretation of
differential decline is provided by Halpern
(1984), who studied age differences in the speed
of processing information regarding familiar
traffic signs. Halpern found that whereas young
adults took approximately the same amount of
time to process familiar verbal and visuospatial
information from such signs, older adults were
much slower to process familiar information
when it was nonverbal than when it was verbal.
The results of the Halpern (1984) study suggest that it is the nature of the stimuli (i.e., verbal vs.visuospatial), and not whether people are
processing familiar or unfamiliar information,
that is responsible for the greater age differences
observed on visuospatial tasks. Consistent with
this interpretation, greater age-related differences have been reported on spatial working
memory tasks than on verbal working memory
tasks (Myerson et al., 1998; Tubi & Calev,
1989; Wechsler, 1997). Because working memory tasks necessarily involve either novel information or novel combinations of information,
the data from such tasks reinforce the suggestion
that there is a differential decline in the efficiency of processing verbal and visuospatial information distinct from any differential effect of
age on tasks involving familiar versus unfamiliar information or on speeded versus unspeeded
tasks.
The present findings and those just discussed
(Halpern, 1984; Myerson et al., 1998; Tubi &
Calev, 1989; Wechsler, 1997) also argue against
interpreting the differential decline of verbal and
visuospatial abilities in terms of the fluid/
crystallized distinction (Horn & Catell, 1967).
Horn (1985, 1987) has clearly distinguished
speed and working memory abilities from both
crystallized intelligence (reflecting acculturation
and knowledge) and fluid intelligence (reflecting broad reasoning abilities). Thus, the finding
of differential age sensitivity of verbal and
visuospatial speed and working memory cannot
be adequately explained in terms of the fluid/
crystallized distinction.
In addition to the preceding psychological
hypotheses, two types of possible neurological
mechanisms for the pattern of differential decline must also be considered. First, it is possible that age-related brain changes are specific,
resulting in greater structural changes in
visuospatial regions than in verbal regions (e.g.,
greater loss of neurons or connections in
visuospatial cortical areas than in verbal areas).
Alternatively, it is possible that age-related
brain changes are global but that they have different functional consequences depending on the
nature of the neural architecture (e.g., equivalent
loss of neurons or of connections in visuospatial
and verbal regions might affect the former to a
greater extent than the latter because of intrinsic
differences in connectivity).
With respect to the first of these hypotheses
(i.e., that age-related neurobiological changes
are specific to certain brain regions) as applied
to visuospatial and verbal processing, it has been
suggested based on behavioral evidence that the
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VERBAL AND VISUOSPATIAL PROCESSING SPEED
right cerebral hemisphere may be subject to
greater age-related deterioration than the left
hemisphere (e.g., Ellis & Oscar-Berman, 1989;
Klisz, 1978; Schaie & Schaie, 1977). However,
neurobiological studies have failed to reveal significant differences in the age-related deterioration of the hemispheres or of the verbal and
visuospatial cortical areas (e.g., Raz et al., 1997;
Terry, DeTeresa, & Hansen, 1987). Although
the frontal lobes and hippocampal regions appear to be especially age-sensitive (Raz et al.),
there is at present no reason to suspect that
changes in these structures would differentially
affect verbal and visuospatial processing.
With respect to the second hypothesis (i.e.,
that equivalent changes differentially affect specific brain regions), it is possible that preexisting aspects of the neural architecture determine
the magnitude of the effects of biological aging
on specific aspects of cognition and behavior.
For example, Cerella and Hale (1994) used a
simplified version of a neural network model to
illustrate how connectivity differences might
determine the age sensitivity of a neural structure. In their example, age-related breaks in a
network could be bypassed through either one
intermediate node (in the case of what was
termed a unimedial network) or two intermediate nodes (in the case of what was termed a
bimedial network). The unimedial network exemplifies a structure with high original connectivity density (i.e., prior to aging) and the
bimedial network exemplifies a structure of
lower connectivity density. It should be noted
that the hypothesized differences in connectivity
involve differences in the pattern of connections
between neurons, rather than differences in the
number of neurons. As a result, these differences might be difficult to ascertain anatomically, despite their potentially profound behavioral consequences (Cerella & Hale, 1994).
For the simple cases considered by Cerella
and Hale (1994) (i.e., unimedial and bimedial
networks), it is possible to calculate the effects
of breaks in the connectivity on the amount of
time such networks would take to process information. Cerella and Hale demonstrated that the
lower connectivity density (bimedial) network is
particularly sensitive to age-related breaks. The
143
differential sensitivity of lower and higher connectivity networks is depicted in Figure 5
(adapted from Figure 18, Cerella & Hale, 1994),
which plots the relative processing time in a
unimedial and a bimedial network as a function
of the probability of a connection being cut. As
the probability of a cut increases, the expected
processing time for the bimedial network increases to a far greater extent than that for the
unimedial network. It should be noted that although the x axis in Figure 5 is labeled probability (cut) it might equally correctly be labeled age
because the proportion of cuts (and hence the
probability of a specific connection being cut) is
assumed to increase with advancing age.
Thus, the present findings raise the possibility
that the architecture of visuospatial cortical areas may be of lower connectivity density compared with that of verbal cortical areas. However, this account is purely speculative at the
present time, and other interpretations are worth
pursuing. For example, differences in the cognitive architecture of verbal and visuospatial tasks
(Fisher & Glaser, 1996) could also make performance in one domain decline more than the
other even though the biological substrates for
the two task domains were equally affected by
age. Discriminating between these alternatives
will require both the development of more refined neurobiological techniques and more explicit formulations of the kinds of differences in
cognitive architecture that might be responsible
for the greater declines in visuospatial abilities
observed in the present study.
Conclusions
Regardless of the mechanisms underlying the
differential decline in information-processing
speed, the differential decline of verbal and
visuospatial information-processing speed is
likely to have profound consequences for other
aspects of cognition. For example, age-related
changes in processing speed have been strongly
implicated in age-related changes in working
memory (e.g., Fry & Hale, 1996; Kail & Salthouse, 1994; Salthouse, 1996). If age-related
changes in working memory are the result of
age-related changes in processing speed, then
visuospatial working memory should decline to
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144
Fig. 5.
BONNIE LAWRENCE ET AL.
The differential sensitivity to damage of lower (unimedial) and higher (bimedial) connectivity networks.
As the probability of a cut increases, the expected processing time for the bimedial network increases
to a far greater extent than that for the unimedial network. Adapted, with permission of the authors,
from Figure 18, Cerella & Hale, 1994.
a greater extent than verbal working memory.
Consistent with this hypothesis, greater age-related declines are observed in spatial working
memory than in verbal working memory (Myerson et al., 1998; Tubi & Calev, 1989; Wechsler,
1997). Age-related changes in processing speed
have also been implicated in age-related changes
in higher cognitive functions, where these latter
changes appear to be mediated through processing speed’s impact on working memory (e.g.,
Fry & Hale, 1996; Kail & Salthouse, 1994;
Salthouse, 1996). Thus, the differential decline
in verbal and visuospatial processing speed observed in the present experiment may be part of
a much broader pattern of differential decline in
working memory and higher cognitive abilities
across the adult life span.
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