Journal of Experimental Psychology: Applied
1996, Vol. 2, No. 4, 305-329
Copyright 1996 by the American Psychological Association, Inc.
1076-898X/96/$3.00
Determinants of Adult Age Differences
on Synthetic Work Performance
Timothy A. Salthouse, David Z. Hambrick, Kristen E. Lukas, and T. C. Dell
Georgia Institute of Technology
Synthetic work research is designed to simulate complex work activities by
requiring participants to perform several concurrent tasks. The current
project consisted of 2 studies in which adults of different ages performed 4
tasks during 25 sessions in a synthetic work situation in 5-min periods
across 3 days. Large age differences were evident in the total score in both
studies, and they were maintained across all stages of practice. Detailed
analyses revealed that with increased age adults in this time management
activity were less likely to perform self-paced tasks and to attempt difficult
auditory discrimination judgments. Very little independent age-related
influences were evident after the initial few sessions on the task. More than
70% of the age-related variance after nearly 2 hr of practice was shared with
measures of processing speed obtained before performing the tasks. These
results suggest that age-related differences in basic processing efficiency
may be responsible for a large proportion of the age-related influences on
the performance of moderately complex activities presumed to be similar to
those likely to be encountered in a variety of work situations. A potential
implication of the results of these studies is that increased age is likely to be
a disadvantage in at least the initial phases of performance in many jobs.'
As the average age of the population increases
there has been growing interest in the abilities
and capacities of older workers. Unfortunately,
relatively little is known about the actual work
performance of adults of different ages because it
is often difficult to obtain detailed assessments
while individuals are performing real jobs (however see Salthouse & Maurer, 1996, for a recent
review). Not only is the process of assessment
potentially intrusive and disruptive to normal
operations, and thus may not be well re-
ceived by management, but many workers could
be reluctant to participate if they cannot be
convinced that the project is not intended to
evaluate their individual level of productivity.
Attempts should still be made to examine the
relations between age and performance in actual
job situations, but other approaches to predicting
the functioning of people in the world outside the
laboratory should also be explored. Three quite
different approaches have been used to relate
research in cognitive psychology to functioning
outside of the laboratory.
By far the most common approach has been to
rely on psychometric ability measures to link
cognitive constructs to performance in natural
situations. That is, within this approach the
researcher concentrates on measures designed to
assess primary cognitive abilities, and then reference is made to the large literature in industrial
and personnel psychology in which scores on
psychometric tests have been found to be related
to criteria such as job performance and occupational level (e.g., J. E. Hunter & R. E Hunter,
1984; Schmidt & J. E. Hunter, 1992).
Timothy A. Salthouse, David Z. Hambrick, Kristen
E. Lukas, and T. C. Dell, School of Psychology,
Georgia Institute of Technology.
This research was supported by National Institute
on Aging Grant AG R376826. We thank Richard Sit
for collecting auditory discrimination data in the retest
session.
Correspondence concerning this article should be
addressed to Timothy A. Salthouse, School of Psychology, Georgia Institute of Technology, Atlanta, Georgia
30332-0170. Electronic mail may be sent via Internet
to tim. salthouse @psych.gatech.edu.
305
306
SALTHOUSE, HAMBRICK, LUKAS, AND DELL
The fundamental assumption underlying the
psychometric approach is that a small number of
abilities are sufficient to account for a large
proportion of the variance in performance across
a wide range of activities because those abilities
are presumably required for successful performance in many and possibly all moderately
complex tasks. The psychometric approach has
the advantage of providing very efficient assessment, and there is a large literature concerned
with the validity of psychometric measures. It
also provides a parsimonious set of predictors to
a wide range of occupational and academic
activities. A possible disadvantage of this approach is the concern that prediction may not be
optimal because the ability measures are usually
quite abstract and often have little face validity
for many activities.
A second approach to linking functioning in
the laboratory to that in life outside the laboratory
consists of detailed analyses of specific jobs or
activities. That is, one particular job or activity is
selected, and then an attempt is made to analyze
performance on it in terms of cognitive constructs. The target activity could be as broad and
complex as automobile driving, but is often more
limited, such as learning to use selected functions
of a word processing package.
The task analysis approach offers a potentially
rich set of data that could be informative about
the strategies, processes, and mechanisms used in
a specific activity. However, generalizability may
be limited to very similar types of situations. For
example, a task analysis of word processing skill
would probably not be very useful in understanding the on-the-job functioning of an airline pilot.
Furthermore, it is often very time consuming to
perform a task analysis and to obtain valid and
reliable measures of each of the hypothesized
components.
A third approach that can be used to relate
laboratory research to functioning in real-world
activities involves the use of simulations of the
characteristics postulated to be common to many
complex activities. In this approach, the goal is to
abstract what are assumed to be the critical
aspects of a wide range of activities and then to
measure them in a controlled setting. However, it
is important to note that the purpose is not just to
assess the component abilities in isolation but
also to evaluate the ability to time share among
several concurrent activities. That is, the synthetic work perspective emphasizes the coordination of two or more dynamically changing components with varying levels of difficulty and
importance, not simply the abilities required for
performance of the individual components (A1luisi, 1967; Morgan & Alluisi, 1972).
The usefulness of the synthetic work approach
depends on the extent to which relevant aspects
of actual jobs have been incorporated in the
simulation. If the simulation is successful, it may
incorporate the major advantages of both the
psychometric approach and the task analysis
approach. A disadvantage is that although synthetic work situations have been designed to
incorporate components of a wide variety of
work activities, little validity information is typically available to indicate that the simulations are
truly accurate or realistic.
Each approach has strengths and weaknesses,
and all three are probably needed to obtain a
complete picture of how cognition relates to
functioning outside the laboratory. In this project,
we relied primarily on the third approach by
using Elsmore's SYNWORK1 (1994) computer
program. The program is designed to incorporate
the dynamic aspects of complex work activities,
and consists of four tasks that are all quite simple
when performed in isolation. They include remembering items on demand, performing a self-paced
task requiring concentration, and monitoring and
reacting to both visual and auditory information.
In addition to the abilities required in the individual tasks, the synthetic work situation requires
time management skills to deal with multiple
concurrent demands. Although there are apparently no studies in which performance in synthetic work situations has been related to measures of work performance, synthetic work
activities were developed to assemble what were
assumed to be important components of many
different work situations together in a manner
that would allow sensitive measurement of all
relevant aspects (Alluisi, 1967; Elsmore, 1994).
Even if subsequent research were to find only
weak relations to actual measures of job performance, synthetic work activities should be of
interest because they are clearly more complex
than most laboratory tasks. Therefore, the motiva-
SYNTHETIC WORK
tion for the current project was that understanding the sources of individual differences in moderately complex synthetic work activities would
likely be informative about differences that might
occur in a variety of actual job situations.
At the end of Study 2, we asked participants
whether the synthetic work activity resembled
anything from their own experience. Only a few
individuals did not provide a response, and thus
most apparently perceived some similarity to
real-world activities. In addition to various specific work situations (e.g., working as a receptionist in a busy office, preparing and baking pizzas),
the activities mentioned by 2 or more respondents
included driving in traffic or on an interstate
highway, preparing a multicourse meal, and monitoring and interacting with small children, including a response by a woman who said that it
reminded her of having to babysit nine children.
Study 1
The primary goal in Study 1 was to determine
if there are adult age differences in the initial
level of performance and in the rate of improvement on this synthetic or artificial work situation.
If there is, we want to specify how the level of
performance and type of improvement differ
across age groups. Moreover, we seek to identify
the factors that predict the level of proficiency
reached after several hours of practice on the
task. We are also interested in determining whether
independent age-related influences are no longer
evident after a certain amount of practice on the
task. If this is true, then most of the age differences were attributable to factors operating in the
earliest periods of experience with the situation.
Method
Participants. There were 39 college students
and 33 older adults recruited from newspaper
advertisements who participated in the study (for
their descriptive characteristics, see Table 1).
There were no age differences in self-ratings of
health, but older adults reported more healthrelated limitations, more frequent use of antihypertension medications, and more incidents of
cardiovascular surgery. Older adults had higher
307
vocabulary scores but were slower in the perceptual comparison and reaction time tasks. The
older adults were also slower and less accurate on
the mouse control task. This pattern of differences is typical of that reported in many studies
comparing adults of different ages in measures of
cognitive functioning.
Procedure. All participants came to the laboratory on 3 separate days within a 10-day period.
Most completed the project within the same week
that they started. Testing was conducted in groups
of 1 to 5 individuals but participants each had
their own microcomputer workstation. On Day 1,
a background questionnaire was administered
followed by two paper-and-pencil perceptual comparison speed tasks, 10-item multiple choice
synonym and antonym vocabulary tests, and two
computer-controlled reaction time tasks. These
tests were identical to those described in earlier
articles (e.g., Salthouse, 1995; Salthouse, Fristoe,
Lineweaver, & Coon, 1995); hence they are
described only briefly here. The two perceptual
comparison tests (Letter Comparison and Pattern
Comparison) each consisted of a page of instructions with examples, followed by two test pages.
The test pages contained pairs of 3 to 12 letters
(Letter Comparison) or line segments (Pattern
Comparison). Participants had to reply by writing
an S for same or a D for different on the line
between the members of the pair according to
whether the members of the pair were the same or
different. We allowed 30 s for each test page, and
the score was the average of the number of
correct responses minus the number of incorrect
responses across both pages. The two reaction
time tasks involved either physical identity decisions for a pair of digits (Digit Digit) or decisions
whether a digit and a symbol were equivalent
according to a code table displayed at the top of
the screen (Digit Symbol). Same decisions were
communicated by pressing the "slash" key, and
different decisions were communicated by pressing the z key on the keyboard. In both tasks, a
practice block of 18 trials was administered
followed by an experimental block of 90 trials.
Because average accuracy was greater than 95%
in both tasks, the median reaction time was used
as the primary measure of performance.
A mouse control training exercise was then
administered in which the participant used the
mouse to move a cursor through a maze shaped
308
SALTHOUSE, HAMBRICK, LUKAS, AND DELL
Table 1
Characteristics of Participants in Study I
Younger
(n = 39)a
Variable
Age (years)
Education (years)
Health rating
1
2
Health satisfaction
Health-related limitations
Cardiovascular surgery
Hypertension medications
Head injury
Neurological treatment
Comparison
Letter
Pattern
Vocabulary
Synonym
Antonym
Reaction time (ms)
Digit digit
Digit symbol
Mouse time (s)
First 3 trials
Last 3 trials
Errors
First 3 trials
Last 3 trials
M
19.7
13.5
2.1
2.3
2.1
1.4
0.0
0.0
0.05
0.05
Older
(n = 33)b
SD
1.5
1.3
0.9
0.8
0.8
0.7
0.2
0.2
M
68.7
15.3
2.3
2.3
2.4
1.9
0.2
0.3
0.2
0.06
SD
t(70)
4.9
1.7
--5.02*
1.1
0.9
0.7
1.0
0.4
0.5
0.4
0.2
--0.63
--0.13
-- 1.52
--2.94*
--2.60*
--3.77*
-- 1.77
--0.17
11.9
20.5
2.4
3.6
8.6
14.3
2.8
3.4
5.40*
7.48*
4.7
4.8
2.1
2.3
7.8
6.6
2.5
3.0
--5.99*
--2.88*
551
1,056
72
176
719
1,595
230
516
--4.33*
--6.13"
7.54
4.52
3.0
1.3
27.51
18.92
19.1
14.6
-- 6.44*
--6.14"
0.9
0.6
0.7
0.6
5.1
2.4
4.7
2.5
-- 5.40"
--4.32*
Note. Health rating, health satisfaction, and health-related limitations were determined by a 5-point scale where lower
numbers indicate better health, and responses to the cardiovascular surgery, hypertension medications, head injury, and
neurological treatment variables were yes or no questions; thus the means correspond to the proportion of individuals reporting a
positive response.
~66.7% were women, b54.5%were women.
*p < .01.
like the letter W. Eight trims were presented on
this task. The participant was able to see the time
in seconds and the number o f errors displayed
after each attempt.
The participants were then introduced to the
synthetic work program through written instruction and a short practice session on each o f the
component tasks. The written instructions explained the task, and an examiner was present to
answer questions about procedures. Each task
was then presented in isolation for 1 min and
followed by the four tasks presented together for
1 rain. At this point, the participants were reminded that the goal was to obtain the highest
possible number o f points in each session. The
remainder o f Day 1 was devoted to the four tasks
together for the 5 sessions, which were 5 min
each. On Days 2 and 3, participants performed 10
sessions of 5 min each with all four tasks
together.
Figure 1 illustrates the S Y N W O R K 1 display
screen when all four tasks are operative. Displays
of the tasks occupied a full screen on a color
display monitor. However, because the viewing
distance was not constrained, it was impossible to
specify the exact size o f the stimuli in terms of
visual angle. All responses within the synthetic
work program were executed with a mouse. In
SYNTHETIC WORK
I
309
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Illustration of computer screen with all four tasks in the SYNWORK1
program.
the upper left of the screen was a memory task
consisting of a set of five letters. This set was then
removed and followed by periodic displays of a
probe letter, which was to be classified as yes (for
a member of the set) or no (not a member of the
set). At any point during the session, the participant could retrieve the set of memory letters by
moving the cursor to the box that previously
contained the letters and pressing a button on the
mouse. The upper right of the display contained
an arithmetic task, where two 3-digit numbers
were to be added by adjusting plus and minus
signs to produce the correct sum in the row below
the addends. This task was completely selfpaced. The lower left quadrant of the display
contained a visual monitoring task, where the
participant monitored the position of a pointer
moving continuously along a horizontal scale and
attempted to reset it before it reached the end of
the scale. The lower right quadrant of the display
contained an auditory monitoring task. High and
low tones were presented periodically throughout
the session, and the task was to respond whenever
a high tone occurred.
The SYNWORK1 program (Elsmore, 1994)
allows considerable flexibility in terms of the
presentation of the tasks and manipulation of
relevant parameters. Values of the parameters
used in this study were as follows: In the memory
task, there were five letters in the memory set,
and they remained constant throughout each
5-rain session but were varied across sessions.
Probe stimuli occurred every 10 s; 10 points were
awarded for every correct response, and 10 points
were subtracted for every incorrect response and
for every retrieval of the memory list after the
initial display at the beginning of the session. In
this task, the highest possible score was 300
because 10 points could be acquired with a
correct response to each of the 30 probe stimuli
310
SALTHOUSE, HAMBRICK, LUKAS, AND DELL
interval was 15 s). Finally, parameters within the
auditory monitoring task specified that the events
occurred every 5 s, had a .2 probability of targets,
with a target frequency of 1319 Hz and a
nontarget frequency of 1046 Hz. We awarded 10
points for every hit (correct detection) but subtracted 10 points for every miss (false alarm). If
exactly 12 targets (i.e., .2 × 60 events with an
event every 5 s for 5 min) were presented, then
the maximum score would be 120. An index of
overall performance, corresponding to the sum of
the points accumulated in the four tasks, was
continuously updated and was visible in the
middle of the display.
(i.e., one probe every 10 s for 5 min). Parameters
in the arithmetic task specified that two 3-digit
numbers were to be added. Again, 10 points were
awarded for every correct response, and 10 points
were subtracted for every incorrect response.
Because this task was self-paced, the maximum
score depended on how quickly and accurately
the individual could perform the arithmetic operations.
In the visual monitoring task, the pointer line
required 15 s to move from the middle to the end
of the scale. We awarded 1 point for every 10
pixels in which the line was away from the
middle of the scale and deducted 10 points for
every second in which the line was on the end of
the scale. Therefore, the maximum possible score
in this task was 200, if the line was reset each
time at the very end of the scale (i.e., 10
points × 20 opportunities in 5 min if the reset
.!f
Results
Figure 2 displays the mean total points (sum of
points across the four tasks) by session for the
I
Day I
Day 2
Day 3
300
aa~
100
0
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we
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2 3 4 5 6 7 8 9 10111213141516171819202122232425
Young
Session
Old
Figure 2. Means and standard errors of the total number of points by session for the
young and old adults in Study 1.
i
SYNTHETIC WORK
311
Memory
Day1
Arithmetic
Day2
Day3
Day1
Day2
Day3
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Visual Monitoring
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Auditory Monitoring
Day1
Day3
Day3
Day2
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Session
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Session
Figure 3. Mean points in each task by session for the young and old adults in
Study 1.
two age groups. 1 Notice that there were large age
differences but that similar patterns of improvement were evident in both age groups. An Age x
Practice analysis of variance (ANOVA) was
conducted on these data after first grouping the
sessions into 5-session blocks in order to increase
reliability. The ANOVA revealed significant effects of age and practice (both Fs > 113), but no
Age x Practice interaction, that is, F(4, 280) =
2.62.
Figure 3 illustrates the mean points on each of
the four tasks for the two age groups. All main
effects of age and block were significant
(Fs > 14), and all Age x Block interactions were
significant (Fs > 4.5) except for that on the
memory task, that is, F(4, 280) = 2.69. The lack
of an interaction in the memory task likely
occurred because both groups were very close to
the maximum score (i.e., 300).
Figures 2 and 3 indicate that there were large
age differences in total score and in the scores for
every component. The Age X Block interaction
was not significant for total score because the
greater improvement for the younger adults in the
arithmetic task was apparently offset by greater
improvement for the older adults in the visual
monitoring and auditory monitoring tasks.
Predictors of performance. We conducted a
series of regression analyses to identify the
determinants of performance at each stage of
1 Because of the large number of statistical comparisons, an alpha level of .01 was used for all significance
tests.
312
SALTHOUSE, HAMBRICK, LUKAS, AND DELL
practice. First, composite measures were formed
for use as predictors in the regression equations.
We created a composite speed index by subtracting the average of the z scores for the Digit Digit
reaction time and Digit Symbol reaction time
measures (r = .74) from the average of the z
scores for the Letter Comparison and Pattern
Comparison (r = .68) measures. Because higher
reaction times represent slower performance and
higher comparison scores represent faster performance, the effect of this subtraction was such that
it created a composite speed index in which
higher scores indicated faster speed. A composite
vocabulary score was created from the average of
the synonym and antonym vocabulary scores
(correlation = .66), with higher scores representing higher vocabulary levels. A mouse control
measure was derived from the average time (in
seconds) needed to complete the last three trials
on the mouse maze. Higher scores on this measure reflect poorer or slower performance. We
then used simultaneous regression equations to
predict the number of points in each 5-session
block from the age, vocabulary, speed, and mouse
control measures (see Table 2).
The increase in the intercept across successive
blocks for many of the criterion measures indicates that factors other than the predictors in the
regression equations are responsible for some
practice-related improvement. That is, if only the
relation of the predictors to the scores were to
change, then the intercept would remain constant
and the regression coefficients would vary. However, there were relatively few systematic changes
across practice in the strength of the predictors.
Mouse control ability appeared somewhat less
important in later sessions (primarily in the visual
monitoring task), but there were few other shifts
in the predictive relations. Because significant
independent effects of age were evident only on
arithmetic and auditory monitoring, most of the
age effects on the memory and visual monitoring
measures can be inferred to have been mediated
through other variables.
Independent age-related influences. The next
series of analyses (see Table 3) examined where
in the sequence of successive blocks independent
age-related influences occurred. The standardized
regression coefficients are in the first two rows
followed by the total proportion of variance
accounted for by these two variables, the total
proportion of variance accounted for by age, and
then the proportion of variance uniquely accounted for by age (i.e., the increment in R 2
associated with age in a hierarchical regression
analysis after control of the prior variable in the
sequence). Finally, the percentage of the agerelated variance that was unique (i.e., the value in
row 5 divided by the value in row 4 × 100) is in
the last row.
Statistically significant unique age effects were
evident on several measures after the first in the
sequence (row 5), but in all cases it was a rather
small percentage of the total age-related variance
(row 6). Only in Block 1 of Day 2 for the memory
measure was the value greater than 15%, and
even then it was only 23.5%. Therefore, it can be
concluded that most age-related influences in
these synthetic work tasks are present within the
first 30 min of performing the tasks.
Detailed analyses of component task performance. Measures of each task were next examined to determine the source of the age and
practice effects in more detail. That is, which
aspects of the tasks contribute to improvement
with practice, and to the existence of age differences? Results of Age × Practice (five blocks
with five sessions each) ANOVAs of specific
measures from each component task, are summarized in Table 4.
In the memory task, significant age differences
were evident in the accuracy measure and in the
number of list retrievals. There was a significant
increase in accuracy with practice, but it was no
greater for young adults than for older adults. The
older adults were also much more variable than
the young adults in the later blocks, but this may
simply reflect the fact that the young adults were
near the measurement ceiling on the accuracy
measure and near the measurement floor with the
number of list retrievals measure.
There was a large increase with practice in the
number of arithmetic problems attempted for the
younger adults but much less of an increase for
the older adults. Some older adults did not
attempt the arithmetic task, but for those who did,
accuracy was significantly lower than for the
younger adults. Efficiency also improved with
practice and was reflected in the shift toward
fewer operations (i.e., sum of plus and minus
operations) per problem in later sessions. The
absolute shift in the number of operations per
SYNTHETIC WORK
313
Table 2
Regression Analyses of Total and Component Scores Across All Blocks
in Study I
Variable
1
2A
2B
3A
3B
Total
Inmrcept
Age
Vocabulary
Speed
Mouse
R2
632
-6.7*
7.0
55.5*
-4.0
793
-7.4*
10.0"
32.3*
-5.7*
873
-6.2*
5.1
31.7"
-3.9*
900
-7.4*
7.0*
34,0*
-2.2
880
-6.4*
5.9
38.1"
-.02
.67*
.85*
.84*
.88*
.81"
Memory
Intercept
Age
Vocabulary
Speed
Mouse
R2
245
-0.5
1.9
9.0
0.2
266
-0.5
2.6*
5.7
-0.5
282
-0.2
1.3
4.1
-0. I
277
-0.4
1.9
2.0
-0.4
279
-0.3
1.3
2.5
-0.1
.18"
.33*
.18"
.21"
.15
• Arithmetic
Intercept
Age
Vocabulary
Speed
Mouse
R2
156
-2.1"
0.9
11.8"
-0.2
234
-3.5*
3.2
10.3
-0.3
294
-3.7*
1.7
9.9
-0.1
325
-4.6*
3.9
11.4
-0.6
325
-4.3*
3.4
11.2
-0.1
.74*
.80*
.73*
.79*
.73*
Visual monitoring
Intercept
Age
Vocabulary
Speed
Mouse
R2
161
- 1.3
1.1
23.8
-4.4
155
-0.1
1.8
11.9
-4.8*
164
0.5
- 0.2
10.3
- 3.0*
170
-0.1
- 0.4
9.5
- 1.2
144
0.3
0.4
11.3
- 1.3
.35*
.53*
.47*
.34*
.26*
Auditory monitoring
Intercept
Age
Vocabulary
Speed
Mouse
R2
69
-2.8*
3.1
10.9
0.4
137
-3.3*
2.5
4.5
-0.1
133
-2.8*
2.4
7.5
-0.0
129
-2.4*
1.6
11.1
-0.0
132
-2.1"
0.7
13.1
0.1
.72*
.75*
.64*
.68*
.60*
Note. All blocks contained five sessions.
*p < .ol.
314
SALTHOUSE, HAMBRICK, LUKAS, AND DELL
problem was greater for the older adults. However, it is important to note that the average for
the older adults in the last block was nearly the
same as the average in Block 1 for the younger
adults. Younger adults appeared to have increased
their use of minus signs relative to plus signs with
practice, but this did not occur for the older
adults, and the overall practice effect on the
measure of the ratio of plus-to-minus operations
was not statistically significant.
In the visual monitoring task, there were more
lapses in the early blocks for the older adults.
However, there were no significant age or practice differences in the average reset distance.
Older adults were less accurate than young adults
across all blocks in the auditory monitoring task,
although the absolute amount of practice-related
improvement was greater for the older adults.
There was a large age difference in hit rate and a
smaller but still significant difference in the false
alarm rate.
Prediction of final level of performance. In
the last analysis, we examined how much of the
age-related variance in the final level of performance (i.e., total number of points) was independent of the index of speed assessed from the
reaction time and perceptual speed tasks administered at the beginning of the study. The speed
variable was selected because it was a significant
predictor across all blocks (cf. Table 2), and past
research has indicated that this variable shares a
large percentage of the age-related variance with
many cognitive measures (e.g., Salthouse, 1993,
1994, 1995; 1996; Salthouse et al., 1995). The R 2
associated with age in the regression equation for
Block 3B overall score with age as the only
predictor was .739, but the increment in R 2
associated with age after control of the speed
measure was .206. Therefore, it can be inferred
that only 27.9% (i.e., .206 - .739) of the total
age-related variance in the last block of trials was
independent of the speed with which very simple
cognitive operations could be performed before
beginning the synthetic work activity.
across approximately 2 hr of practice. Although
the age differences in overall score remained
fairly constant across practice, the nature of the
practice-related improvement differed across the
two groups, with the younger adults improving
by increasing the number of arithmetic problems
attempted and solved correctly and the older
adults improving by reducing the number of
lapses in visual monitoring and by increasing
detection of targets in auditory monitoring.
What was responsible for the age differences in
the initial session and for the differences in the
nature and magnitude of improvement across
sessions? The large speed relations suggest that
how quickly many processing operations can be
executed contributes to the age differences at all
stages of practice. In fact, the discovery that over
70% of the age-related variance in the total points
measure in the last block was shared with measures of perceptual comparison and reaction time
speed indicates that simple processing efficiency
may be a major determinant of the age-related
differences in complex activities. This has also
been found to be true in simpler activities (e.g.,
Salthouse, 1993, 1994, 1995, 1996; Salthouse et
al., 1995).
However, other factors could also have contributed to the observed age differences. For example, mouse experience, auditory or visual
sensitivity, or general experience with computers
and related activities may all have been important
factors affecting performance. There could also
be age-related declines in time-sharing ability
because several older participants reported that
they simply ignored some of the tasks, usually the
arithmetic and auditory monitoring tasks, because they felt that they could not cope with them
simultaneously. This interpretation is consistent
with earlier reports of age differences in divided
attention situations (e.g., Kramer, Larish, &
Strayer, 1995; Salthouse et al., 1995).
Study 2
Discussion
The results of Study 1 indicate that there are
sizable initial age differences in synthetic work
performance and that they are largely maintained
Two approaches can be used when a potential
productivity problem is identified in a work
situation. One is to introduce training to attempt
to improve the proficiency of the poorer performing individuals. The second is to try to redesign
SYNTHETIC WORK
315
Table 3
Standardized Regression Coefficients and Estimates of Proportions of Variance Across All Blocks
in Study I
Variable
1
2A
2B
3A
3B
Total
Age
Priorvariab~
R2
Total
Age
Unique age
Percentage o f a g e R2unique
-.76*
-.41"
.59*
-.26*
.71"
-.25*
.74*
.11
.98*
.58*
.58*
.58*
100.0
.89*
.74*
.07*
9.5
.89*
.76*
.02*
2.6
.94*
.81"
.02*
2.5
.96*
.74*
.00
0.0
Memory
Age
Prior variable
R2
Total
Age
Unique age
Percentage of age R 2 unique
-.31 *
- .22
.61"
- .02
.77*
-.07
.83*
-.02
.83*
.10"
.10"
.10"
100.0
.46*
.17"
.04
23.5
.58*
.09
.00
0.0
.72*
.10"
.00
0.0
.70*
.08
.00
0.0
Arithmetic
Age
Prior variable
R2
Total
Age
Unique age
Percentage o f a g e R2unique
-.84*
-.24*
.75*
-.09
.86*
-.24*
.75*
-.09
1.05"
.70*
.70*
.70*
100.0
.93*
.76*
.02*
2.6
.89*
.71"
.00
0.0
.92*
.76*
.02*
2.6
.96*
.69*
.00
0.0
Visual monitoring
Age
Prior variable
R2
Total
Age
Unique age
Percentage o f a g e R 2 unique
-.50*
-.15
.65*
-.02
.80*
-.20*
.73*
.14
.92*
.25*
.25*
.25*
100.0
.54*
.23*
.02
8.7
.62*
.13"
.00
0.0
.68*
.21"
.03*
14.3
.74*
.08
.01
12.5
Auditory monitoring
Age
Prior variable
R2
Total
Age
Unique age
Percentage of age R 2 unique
Note. All blocks contained five sessions.
*p < .01.
-.82"
-.38"
.58*
.10
1.03"
--.16"
.82*
.10
1.05"
.67*
.67*
.67*
100.0
.84*
.73*
.05*
6.8
.89*
.61"
.00
0.0
.91"
.65*
.01"
1.5
.95*
.56*
.00
0.0
316
SALTHOUSE, HAMBRICK, LUKAS, AND DELL
Table 4
Component Measures and Other Statistical Data Across All Blocks for Each Age Group in Study I
1
Variable
M
2A
SD
M
2B
SD
3A
M
SD
M
3B
SD
M
SD
Memory
Correct (%)
Age (A): F(1, 70) = 6.75
Younger
Older
t(70)
90.3
81.9
3.05"
Practice (P): F(4, 280) = 25.18
12.1
10.9
Number of list retrievals
Age (A): F(1, 70) = 8.44*
Younger
Older
t(70)
0.14
1.48
-2.79*
94.2
84.7
4.51"
6.1
11.3
A × P: F(4, 280) = 2.23
97.1
91.5
3.31"
3.1
9.9
Practice (P): F(4, 280) = 2.79
0.23
3.01
0.13
1.28
-2.41
0.23
2.96
95.9
89.2
3.91"
4.6
9.5
94.8
89.9
2.60
5.4
10.1
A x P: F(4, 280) = 188
0.04
0.78
-2.38
0.09
1.94
0.06
0.85
-2.76*
0.12
1.77
0.12
0.76
-2.45
0.20
1.63
Arithmetic
Number of problems
attempted
Age (A): F(1, 51) = 161.49"
Younger
Older
t(70)
18.65
3.28
11.26"
Correct (O~)a
Age (A): F(I, 51) = 57.83*
Younger
Older
t(df)
t(djO
11.36
15.49
-2.71"
Ratio of plus to negative
operations a
Age (A): F(1, 50) = 0.12
Younger
Older
t(djO
6.36
5.00
7.41
4.99
1.47
24.81
4.95
12.46"
7.30
6.00
30.35
6.88
11.61"
9.29
7.57
Practice (P): F(4, 204) = 15.50"
86.5
6.0
58. I
25. I
6.69* (53)
Number of operations per
problema
Age (A): F(I, 56) = 7.24*
Younger
Older
Practice (P): F(4, 204) = 159.70"
91.5
4.5
65.4
25.0
6.35* (60)
91.3
6.8
73.0
21.0
5.02* (59)
10.79
12.51
-1.74
3.10
4.78
(61)
10.42
11.38
-1.31
1.91
3.86
(59)
7.23
11.12
-0.87
9.40
24.68
(57)
6.61
16.48
- 1.59
8.46
7.22
33.06
7.49
12.21"
9.62
7.85
A × P: F(4, 204) = 3.40
93.5
77.0
6.97*
4.1
13.8
(58)
92.3
80.3
5.50*
4.9
12.0
(58)
9.46
10.65
-1.59
1.61
4.15
(59)
9.88
11.24
-2.10
1.74
3.28
(58)
A x P: F(4, 200) = 1.51
Practice (P): F(4, 200) = 2.72
5.72
5.01
(53)
32.30
7.08
13.47"
A × P: F(4, 244) = 4.20*
Practice (P): F(4, 244) = 18.17"
2.29
9.10
(60)
A x P: F(4, 204) = 47.89
6.29
37.91
(55)
4.30
10.84
-1.46
5.12
27.28
(54)
3.76
12.09
-2.08
4.29
24.39
(55)
Visual monitoring
Reset distance
Age (A): F(1, 70) = 0.04
Younger
Older
t(70)
69.9
70.6
-0.14
Practice (P): F(4, 280) = 2.32
21.3
17.2
66.9
69.0
-0.46
20.1
17.0
66.6
68.7
-0.46
A × P: F(4, 280) = 0.57
19.7
18.0
67.5
67.5
-0.01
19.5
18.9
67.6
66.9
0.13
20.5
21.3
SYNTHETIC WORK
Table 4
317
(continued)
1
Variable
M
2A
SD
M
2B
SD
M
3A
SD
M
3B
SD
M
SD
Visual monitoring (continued)
Number of lapses
Age (A): F(1, 70) = 6 . 3 8
Younger
Older
t(70)
0.18
0.99
-5.89*
0.21
0.83
Practice (P): F(4, 280) = 6.41"
0.23
0.64
-3.66*
0.36
0.58
0.29
0.41
-0.83
0.60
0.56
A × P: F(4, 280) = 18.71
0.28
0.39
-0.92
0.49
0.53
0.42
0.33
0.64
0.67
0.44
Auditory monitoring
Hit rate
Age (A): F(1, 70) = 122.59"
Younger
Older
t(70)
77.9
24.7
11.22"
16.4
23.7
False alarm rate
Age (A): F(1, 70) = 12.84"
Younger
Older
t(70)
2.2
5.7
- 2.74*
1.8
7.6
Practice (P): F(4, 280) = 32.70*
93.2
29.4
12.72"
8.2
30.1
94.8
39.4
8.97*
6.2
38.0
Practice (P): F(4, 280) = 14.34"
1.1
3.4
- 2.41
1.5
5.8
0.3
2.4
- 3.37 *
0.5
3.9
A × P: F(4, 280) = 3.42*
95.2
42.5
9.29*
5.4
35.0
96.7
46.4
8.23*
4.1
37.9
A × P: F(4, 280) = 2.55
0.6
2.1
- 3.35"
0.7
2.7
0.5
1.2
- 2.03
0.7
2.0
aThe df differs because some older adults did not complete any arithmeticproblems in this session; hence, the df appears in
parentheses to the right of the respective t value.
*p < .01.
the job situation to minimize sources of difficulty.
A version of the second approach was adopted in
this study in part because there was little evidence of a shift in the size of the age differences
in overall score across over 2 hr of task performance time in Study 1. Suitable training may
eventually eliminate the age differences, but
differential training may not be needed if the
differences can be eliminated with job redesign.
Furthermore, if the source of the age differences
could be identified, then eventually it might be
possible to design interventions that eliminate the
initial differences and again make training unnecessary.
Job redesigns typically begin by identifying
aspects of the situation creating problems followed by attempts to modify those aspects to
reduce the problems. In the previous study, many
older adults performed at low levels on the
arithmetic and auditory monitoring tasks. Therefore, we attempted to redesign parameters of the
synthetic work situation to make these tasks more
compatible with the capabilities of older adults.
This was accomplished by deemphasizing the
arithmetic task and increasing the distinctiveness
of the tones in the auditory monitoring task.
Major changes from Study 1 were as follows:
First, the participants were recruited to form a
continuous age distribution, but to the best of our
knowledge, none was currently enrolled as a
full-time student. Study 1 age comparisons are
open to question because all of the younger adults
in Study 1 were students at a technological
university, and most had considerable previous
experience with computers and mouse control
devices. Second, we transferred participants to
different priorities at the middle of Day 3 (Block
3B) to examine adaptability to new conditions.
Although there was little independent age-related
variance from Block 3A to Block 3B in Study 1,
unique age-related variance may have emerged if
there was a change in the conditions. To assess
awareness of the altered task conditions, we did
not inform the participants of this change, but
instead we asked them whether they had noticed
it when they had completed the final session.
Third, different initial task conditions (payoffs
318
SALTHOUSE, HAMBRICK, LUKAS, AND DELL
and event frequencies) were used to examine
generalizability of the practice-related improvements. Fourth, the auditory discrimination was
made easier by increasing the difference between
the signal and nonsignal tones. Fifth, a test of
near-vision acuity was administered at the end of
Day 2. Finally a postexperimental questionnaire
was administered at the end of Day 3. Items in the
questionnaire referred to the amount of prior
experience with computers, a computer mouse,
and video games, as well as self-appraisals of
level of motivation and of satisfaction with the
amount of improvement in performance. We also
requested information about how performance
improved and any factors that might have limited
further improvement.
Method
Participants. Descriptive characteristics of
the participants are summarized in Table 5. The
young adults in this study were slower than the
college-age adults in Study 1 in the perceptual
comparison, reaction time, and mouse control
tasks, but they had higher scores in the vocabulary tests. The older adults in the two studies were
fairly similar. It is particularly noteworthy that
there were strong negative age relations on the
visual acuity and experience measures. This is
consistent with the interpretation that factors
related to sensory ability and to relevant experience contribute to the age differences in synthetic
work performance.
Procedure. Most aspects of the procedure
were identical to those in Study 1, and thus only
the differences between the two studies are
described below. The vision test was conducted
with a card 2 containing columns of 2-digit numbers and Landolt C stimuli at 10 different font
sizes. Participants read numbers or stated the
orientation of the gap in the C while holding the
card at a distance of approximately 30.0 cm and
wearing necessary corrective lenses if needed.
The fonts at this distance correspond to Snellen
visual acuity ratios of 0.1 to 1.0 in steps of 0.1.
Participants began with the largest font and
continued to the smaller fonts until they were
unsuccessful on two or more items in a set of a
given size. The smallest font size at which they
made fewer than two errors served as the measure
of visual acuity. We made separate assessments
with the number and Landolt C material with the
left eye while the fight eye was covered and with
the right eye while the left eye was covered.
The questionnaire administered at the end of
Day 3 contained nine items. The first three were
ratings of the amount of experience on a scale of
1 = very much to 5 = none with computers, use
of a computer mouse and playing video games.
The level of interest or motivation in the study
and the degree of satisfaction with the amount of
improvement were also rated on a scale with 1 =
high and 5 = low. Finally, there were open-ended
questions that asked how individuals felt they had
improved across practice, what factors limited his
or her performance, whether any difference was
noticed in the conditions in the last day, and
whether the synthetic work activity reminded
him or her of any particular situation.
The parameters in the synthetic work program
were selected to emphasize the memory task
more than the arithmetic task. This was accomplished by reducing the costs and payoffs in the
arithmetic task to 5 points for a correct response
and - 5 points for an incorrect response, increasing the frequency of the memory probes to one
every 5 s, and changing the payoffs in the
memory task to 20 points for correct responses
and - 2 0 points for an error or a miss, with the
cost for a list retrieval remaining at - 1 0 points.
Tones in the auditory monitoring task were
changed to 523 Hz for the low (nontarget) tone
and 2092 Hz for the high (target) tone, from the
values of 1046 and 1319 Hz, respectively, in
Study 1. For the last five sessions on Day 3, the
following changes were introduced: the memory
probes occurred every 10 s instead of every 5 s;
the payoffs in the memory task were changed
from 20 and - 2 0 to 10 and - 1 0 for correct and
incorrect responses, respectively; and the payoffs
for correct and incorrect responses in the arithmetic task were changed from 5 and - 5 to 10 and
- 10, respectively. Therefore, the maximum score
in the memory task for Blocks 1 to 3A was 1,200
(i.e., 20 points for each of 60 probes), and for
Block 3B it was 300 (i.e., 10 points for each of 30
probes).
The initial instructions to the participants were
also modified slightly to enhance comprehension,
and additional practice was provided on the
2 The vision chart was Scalae Typographicae
Birkhausen (Birkhauser Verlag; Basel, Switzerland).
Table 5
Characteristics of Participants in Study 2
Age group
18-39
(n = 21) a
Variable
M
SD
40-59
(n = 30) b
M
60-80
(n = 26) c
SD
M
SD
F(2, 76)
5.3
2.9
69.2
14.8
5.2
2.1
2.00
0.9
0.9
0.9
0.8
0.3
0.5
0.2
0.2
0.95
1.06
1.50
3.10
1.05
19.94"
1.32
0.91
.17
.19
.17
.24
.16
.53*
.01
-.10
Demographicd~a
Age
Education (years)
28.9
15.9
6.3
2.0
49.4
16.1
-.18
Health
Health rating
1
2
Health satisfaction
Health-related limitations
Cardiovascular surgery
Hypertension medications
Head injury
Neurological treatment
1.9
2.2
2.0
1.3
0.0
0.0
0.0
0.1
0.7
0.9
0.7
0.6
0.4
2.1
2.4
2.4
1.8
0.1
0.1
0.1
0.1
1.0
0.9
0.8
0.9
0.3
0.3
0.3
0.3
2.3
2.6
2.4
1.8
0.1
0.6
0.04
0.04
Other comparison measures
Comparison
Letter
Pattern
Vocabulary
Synonym
Antonym
Reaction time (ms)
Digit Digit
Digit Symbol
Mouse time (s)
First 3 trials
Last 3 trials
Errors
First 3 trials
Last 3 trials
Vision (Snellen ratio) d
Left Eye
Numbers
Landolt C
Right Eye
Numbers
Landolt C
Experience level
Computers
Mouse
Video games
Level of interest
Satisfaction with improvement
Noticed Block 3B change
10.7
18.3
2.2
2.8
9.8
16.6
2.4
3.4
8.8
14.1
2.3
2.7
3.68
11.74"
-.36*
-.54*
6.4
6.1
2.5
3.4
7.2
6.4
2.6
3.2
7.7
6.8
2.1
2.7
1.66
0.35
.22
.07
2.28
16.56"
.35*
.61"
681
1,311
239
281
730
1,467
162
246
804
1,844
204
439
13.65
8.07
9.16
4.76
16.03
9.95
13.21
8.39
33.82
18.99
26.17
12.60
9.51"
9.74*
.49*
.47*
2.3
0.8
1.8
0.9
2.1
1.5
2.3
2.0
6.4
2.1
7.0
3.3
7.78*
1.87
.43*
.29
0.65
0.84
0.23
0.21
0.44
0.44
0.23
0.20
0.37
0.39
0.18
0.18
10.39"
36.59*
-.50*
-.64*
0.60
0.78
0.22
0.22
0.42
0.45
0.18
0.23
0.36
0.40
0.18
0.18
9.53*
22.44*
-.47*
-.55"
2.3
2.5
3.1
1.7
2.4
0.91
1.3
1.5
1.3
1.1
1.1
0.30
2.4
3.0
3.9
1.5
2.6
0.86
1.2
1.3
1.2
0.8
1.2
0.35
3.8
4.2
4.5
1.4
2.7
0.54
1.1
1.2
1.2
0.8
1.4
0.51
11.36"
9.90*
7.08*
0.80
0.29
6.28*
.45*
.49*
.45*
-.19
.08
-.32*
Note. Health rating, health satisfaction, and health-related limitations were determined by a 5-point scale where lower
numbers indicate better health, and responses to the cardiovascular surgery, hypertension medications, head injury, neurological
treatment, and noticed Block 3B change were yes or no; thus the means correspond to the proportion of individuals reporting a
positive response.
a52.4% were women, b60.0% were women, c46.2% were women, dHigher scores indicate better vision.
*p < .01.
320
SALTHOUSE, HAMBRICK, LUKAS, AND DELL
component tasks in isolation (i.e., two blocks of 1
min each instead of one single block). The probability of the target signals in the initial instruction sessions on the auditory monitoring task was
also increased to .5 from .2 in order to maximize
the opportunity to experience both tone types.
and practice (five-session blocks) main effects
(Fs > 21.67) and the Age X Practice interaction,
F(8, 296) = 5.12, were all significant in an
ANOVA on the total points.
The mean points in each component task as a
function of practice are illustrated in Figure 5.
Age X Practice (5-session blocks) ANOVAs
revealed that all main effects (Fs > 7.00) and
interactions (Fs > 3.29) were significant for all
of the component scores.
Age X Practice ANOVAs were also conducted
Results and Discussion
Figure 4 displays the mean total points in the
three age groups across the 25 sessions. The age
18-39
.....,...0.---
40-59
60-80
,'g ,~"
1200
~2"
e
i o..o°
9OO
I
. u
0
ii/-".........."""
6OO
n
i
m
i
I
I
I"
300
:
-i
0
Day 2
Day I
12345
Day 3
6 7 S 9 10111213141516171819202122232425
Session
Figure 4. Means and standard errors of the total number of points by session for the
three age groups in Study 2.
SYNTHETIC W O R K
with only Blocks 3A and 3B, before and after the
switch in the payoffs in the memory and arithmetic tasks and in the frequency of the memory
probes. Interactions of age and block were
significant on the arithmetic; F(2, 74) = 15.45,
and visual monitoring, F(2, 74) = 9.71, scores.
Young adults increased their scores more than
older adults after the shift in the arithmetic task,
but older adults had more of an increase than
young adults in the scores on the visual monitoring task. This latter effect was surprising and may
have occurred because older adults had more
time to attend to visual monitoring when the
frequency of memory events was reduced. Young
and middle-aged adults did not improve in visual
monitoring because they were already performing near the maximum level.
Responses to the questionnaire item on whether
any differences were noticed on Day 3 were
coded as yes or no (see Table 5). Table 5 shows
that although 91% of the younger adults and 86%
of the middle-aged adults reported noticing a
change in the conditions, only 54% of the older
adults reported a change. Therefore, the smaller
increase in the number of attempted arithmetic
problems on the part of the older adults may have
been partially attributable to many of them not
having noticed the change in payoffs.
Predictors of performance. Composite measures of vocabulary, speed, and mouse control
ability were formed as in Study 1. We also
created composite measures of vision because the
vision measures were all moderately correlated
(r = .52 to .80) with one another. The experience
measures were also moderately correlated with
one another (r = .32 to .68). Thus an experience
composite was created by averaging the z scores
on the computer experience, mouse experience,
and video game experience items.
Table 6 shows results from the regression
Arithmetic
Memory
Day2
!Day1
321
200
Day3
Day 3
Day 2
Day I
150
~
900
'
.:
100
~!..o o
."
.O
30O
E
:3
7
50
4D. B~B'43
0
.~...~.~
k.,~. ,ok....,.~. ~ •
kL
7"
-3OO
|
~
1
a
i
|
3
i
|
5
I
i
7
*
9
*
i
*
|
|
i
J
|
|
|
|
=
|
|
i,
|
1
11 13 15 17 19 21 23 25
Visual Monitoring
3
5
i
|
|
7
|
|
9
|
|
i
|
i
m |
|
,
11 13 15 17 19 21 23 25
Auditory Monitoring
Day1
Day 2
Day3
2OO
0 ~d'
100
~....,'~'*'*' "~
.
"5-200
~." .."
50
0
'~'~" B'P"m'~''u"~ t~
f
#.'
",k
.~. .~...y"
Z
."
,~.~¢.-
~,00 -/
;
I
-1000
1
!
I
3
I
I
5
*
|
*
7
*
9
*
|
=
j
*
*
*
*
*
*
*
i
*
|
*
|
|
11 13 15 17 19 21 23 25
Session
Figure 5.
-100
~3
Day2
'~.'
-150 1
,
,
|
3
i
,ll
5
,
|
|
|
,
=
|
i
|
|
i
|
t
i
|
t
|
a
,
7 9 11 13 15 17 19 21 23 25
Session
Mean points in each task by session for the three age groups in Study 2.
322
SALTHOUSE, HAMBRICK, LUKAS, AND DELL
Table 6
Regression Analyses of Total and Component Scores Across All Blocks
in Study 2
Variable
1
2A
2B
3A
3B
Total
Intercept
Age
Vocabulary
Speed
Mouse
Vision
Experience
R2
1,493
-9.6
3.4
75.5
-9.5
- 1.6
-99.1
1,459
-6.1
3.0
100.6"
-11.4
243.3
-51.5
1,183
-0.5
3.2
118.4"
-11.1
330.5
-21.5
1,273
- 1.2
3.4
90.9*
-11.4"
290.2
-23.9
681
-3.9
7.5
43.8*
-7.5*
66.5
-6.8
.46*
.47*
.48*
.52*
.68*
Memory
Intercept
Age
Vocabulary
Speed
Mouse
Vision
Experience
R2
1,087
-3.6
2.7
28.1
-2.0
10.2
-22.0
1,063
-0.9
0.9
37.6
-3.9
111.5
-3.2
902
3.1
- 2.7
64.0*
-3.3
207.1
-0.2
969
1.4
0.4
36.3*
-2.9
162.8
-0.1
251
-0.0
0.6
5.2
-0.3
20.2
5.4
.27*
.26*
.29*
.25*
.06
Arithmetic
Intercept
Age
Vocabulary
Speed
Mouse
Vision
Experience
9
-0.3
0.7
1.6
-0.1
16.5
-0.4
30
-0.5
1.2"
3.5
-0.3
9.5
-2.1
46
-0.7
1.5"
3.4
-0.3
7.5
-2.5
49
-0.7
1.5
5.4
-0.3
3.7
-2.9
165
-2.1"
4.2*
11.9
-0.7
-3.5
-9.1
R2
.41"
.47*
.45*
.46*
.53*
Visual monitoring
Intercept
Age
Vocabulary
Speed
Mouse
Vision
Experience
R2
381
-4.7
-5.9
36.9
-6.5
- 32.0
- 56.2
241
-2.3
-4.1
57.2
-4.8
123.0
- 30.7
53
-0.3
-0.1
49.9*
-5.9
142.8
3.1
95
0.8
-3.2
46.0*
-5.3*
130.5
- 11.5
105
0.8
-1.1
21.0"
-3.5*
56.8
1.7
.44*
.39*
.38*
.41"
~40"
Auditory monitoring
Intercept
Age
Vocabulary
Speed
17
- 1.1
5.9"
8.8
125
-2.4*
5.0*
2.2
182
-2.6*
4.6*
1.2
160
-2.7*
4.5*
3.3
161
-2.5*
4.0*
5.7
SYNTHETIC WORK
Table 6
323
(continued)
Variable
1
2A
2B
3A
3B
Auditory monitoring (continued)
Mouse
Vision
Experience
RE
-0.9
3.7
-20.6
-2.3
-2.8
-15.5
- 1.6
-26.9
-22.0
-2.9*
-6.7
-9.5
-3.0*
-6.9
-4.9
.41"
.49*
.51"
.54*
.62*
Note. All blocks containedfive sessions.
*p < .01.
analyses with the set of six predictors. It is
noteworthy that there were no significant independent effects of the composite experience or vision
measures on any session. As in Study 1, there
were significant speed and mouse effects on later
sessions of the memory, visual monitoring, and
auditory monitoring tasks. The significant relation of vocabulary on auditory monitoring performance was surprising, but it may reflect an
influence of general intellectual ability on timesharing capabilities.
There were no significant independent age
effects except in the posttransfer block of arithmetic and in all blocks except Block 1 of auditory
monitoring. There was a decrease from Blocks 1
to 3A in the intercepts for some of the measures,
but there was relatively little systematic increase
in the regression coefficients for any of the
predictors. One exception was in the transfer
(Block 3A vs. Block 3B) comparison, where
speed seems to have been less important in the
posttransfer block, particularly in the memory
task where performance was at ceiling for all
groups (cf. Figure 5).
Independent age-related influences. Table 7
contains the results of the regression analyses
designed to identify independent age-related influences on the total scores and the scores in each
component task. As in Study 1, most of the
unique age-related effects were evident early in
practice, with the highest value prior to the
transfer block of only 15.2%. Significant independent age-related effects occurred in the transfer
block, particularly for the arithmetic and auditory
monitoring tasks. We expected an increase in
emphasis on the arithmetic task when payoffs
were shifted, but it is noteworthy that the shift
was smaller with increased age. The reason for
the emergence of independent age-related vari-
ance in later blocks of the auditory monitoring
task was not obvious.
Detailed analyses of component task performance. Table 8 shows that significant practice
effects occurred on memory percentage correct,
arithmetic number of problems attempted, percentage correct, and number of operations per problem, visual monitoring number of lapses, and
auditory monitoring hit rate and false alarm rate.
Significant age effects were evident in memory
percentage correct, arithmetic number of problems attempted, and hit rate in the auditory
monitoring task. Finally, significant interactions
of age and practice were evident only on the
number of problems attempted in the arithmetic
task, and this occurred because the increases
were smaller with greater age. The only interaction of Age x Practice in ANOVAs contrasting
performance in Block 3A (before transfer) and
Block 3B (after transfer) was on the number of
arithmetic problems attempted, F(2, 74) = 5.50.
Prediction of final level of performance. The
total points on Block 3A (pretransfer) and Block
3B (posttransfer) were predicted from age and the
composite speed index. The R 2 for age in the
prediction of total points on Block 3A was .292,
and after control of the speed measure it was
.054, indicating that only 18.5% (.054 + .292) of
the age-related variance in the final pretransfer
performance was independent of speed. The R 2
for age in the prediction of total points in Block
3B was .430, and after control of speed it was
.117, indicating that 27.2% (.117 + .430) of the
age-related variance in the posttransfer performance was independent of speed. Although there
is clearly much more than speed involved in the
age differences in these tasks, it is nevertheless
noteworthy that speed accounts for such a large
324
SALTHOUSE, HAMBRICK, LUKAS, AND DELL
Table 7
Standardized Regression Coefficients and Estimates of Proportions of VarianceAcross All Blocks
in Study 2
Variable
1
2A
2B
3A
3B
Tot~
Age
Prior variable
R2
Total
Age
Unique age
Percentage ofage R2unique
-.60*
-.15
.71"
.06
.98*
-.10
.87*
-.29*
.68*
.35*
.35*
.35*
100.0
.66*
.33*
.02
6.1
.89*
.26*
.00
0.0
.85*
.29*
.01
3.4
.76*
.43*
.06*
14.0
Memory
Age
Prior variable
R2
Total
Age
Unique age
Percentage o f a g e R 2 unique
-.46*
- .01
.21"
.21"
.21"
100.0
.08
- .07
.10
.83*
.86*
.82*
.75*
.70*
.16"
.00
0.0
.69*
.07
.01
14.3
.71"
.08
.01
12.5
.53*
.01
.01
100.0
Arithmetic
Age
Prior variable
R2
Total
Age
Unique age
Percentage o f a g e R 2 unique
- .53"
- . 11
.85"
.28*
.28*
.28*
100.0
.83*
.31"
.01
3.2
.90*
.90*
- . 12"
.89*
.88*
.33*
.00
0.0
.87*
.32*
.00
0.0
.92*
.38*
.01"
2.6
-.07
-.05
Visu~monitofing
Age
Prior variable
R2
Tot~
Age
Unique age
Percentage o f a g e R 2 unique
-.58*
-.19
.58*
.07
.97*
-.08
.83*
.03
.86*
.34*
.34*
.34*
100.0
.50*
.28*
.03
10.7
.88*
.20*
.00
0.0
.76*
.20*
.00
0.0
.71"
.13"
.00
0.0
-.08
.90*
-.18"
.78*
Auditory monitofing
Age
Prior variable
R2
Tot~
Age
Unique age
Percentage ofage R2unique
-.43*
-.23*
.79*
-.09
.88*
.18"
.18"
.18"
100.0
.84*
.33*
.05*
15.2
.88*
.90*
.8 I*
.36*
.01
2.8
.38*
.00
0.0
.43*
.02*
4.7
Note. All blocks contained five sessions.
*p < .01.
proportion o f the age-related variance after nearly
2 hr o f practice.
For purposes o f comparison, we carried out
similar analyses with the exPerience and vision
composites as predictors. The percentages o f
unique age-related variance after control o f the
experience composite variable were 42.8% in
Block 3A and 47.9% in Block 3B. The values
SYNTHETIC WORK
after control of the vision measure were 38.7% in
Block 3A and 51.4% in Block 3B. Both sets of
residual age-related variance were considerably
higher than those after control of the speed index.
This indicates that there was greater overlap of
the synthetic work score with the age and speed
variance than with either the age and experience
or the age and vision variance.
Self-reports of improvement. Responses to
the items in the questionnaire concerning how
improvement occurred and what factors limited
improvement were quite heterogeneous and did
not lend themselves to formal quantitative analyses. Nevertheless, it is interesting that there were
several reports that performance improved because of better control of the mouse, faster
solutions to the arithmetic problems, better
memory of the letters, and easier detection of the
high tone in the auditory monitoring task. A
number of respondents also mentioned that they
improved because of greater concentration and
because they developed priorities for dealing
with the tasks. The primary factor mentioned as
limiting further improvement was the speed of
doing arithmetic. Distinguishing the tones, controlling the mouse, and generally trying to perform several activities simultaneously were also
each mentioned by 2 or more respondents.
General Discussion
The results of the two studies in the current
project clearly indicate that there are large age
differences in this synthetic work time management activity. With increased age adults are less
able to deal successfully with several concurrent
tasks and often neglect the most difficult or the
self-paced tasks. The age differences persist
through at least 2 hr of on-task time and were also
evident when a new payoff scheme and different
event frequencies were introduced. To the extent
that the synthetic work activity is a reasonable
simulation of actual work situations, these results
suggest that moderately large age differences can
probably be expected in the initial stages of
performing a variety of different jobs.
Probably it is easier to specify factors that are
not responsible for the large and persistent age
differences than to specify those that are responsible. For example, the age differences in performance do not appear to be attributable to systematic shifts in level of interest or motivation
325
because the ratings (see Table 5) suggest that if
anything increased age was associated with higher
motivation. The age differences are also unlikely
to be completely attributable to sensory factors
because few unique effects of visual acuity were
found in Table 6, and the auditory discrimination
in Study 2 should have been very easy for all
participants. Furthermore, auditory monitoring
accuracy increased with practice for the older
adults, particularly when the frequency of memory
events decreased in Block 3B of Study 2, and this
is unlikely to have occurred if performance was
limited only by sensory abilities. Finally, although there were large preexperimental differences in amount of relevant experience, there
were few unique effects of experience when other
predictors were considered (cf. Table 6). In
addition, the age differences on the overall score
were nearly constant across practice instead of
being reduced with experience as one would
expect if the initial differences were simply a
reflection of lack of experience on the part of the
older adults.
The Study 2 attempt at job redesign could be
considered unsuccessful in the sense that there
were still age-related differences in many of the
measures of performance. This finding, together
with the inability to isolate the age-related effects
to a single specific factor, suggests that there may
be a genuine age-related impairment in the ability
to perform several simultaneous tasks successfully. In the current situation, this was manifest in
the almost total neglect of the self-paced arithmetic task and of the difficult auditory discrimination task until late in practice.
There were also strong unique effects of speed
across nearly all practice blocks, and less than
30% of the age-related variance in the final
25-min block was independent of speed in both
studies. This suggests that fairly basic limitations
in processing efficiency contributed to the initial
age differences and to the amount of practicerelated improvement. This in turn implies that the
contribution of simple processing efficiency needs
to be considered before attributing age differences to more specific factors. For example, the
apparent age differences in time-sharing ability
may be largely attributable to age differences in
processing speed, as has been found in other
studies (Salthouse et al., 1995).
Three factors could have contributed to the
sizable age differences found in the auditory
326
SALTHOUSE, HAMBRICK, LUKAS, AND DELL
Table 8
Component Measures and Other Statistical Data Across All Blocks for Each Age Group in Study 1
1
M
Variable
2A
SD
M
2B
SD
M
3A
SD
M
3B
SD
M
SD
Memo~
Co~ect(%)
A g e ( A ) : F ( 2 , 7 4 ) = 8.30*
Younger
Middle-age
Older
F(2, 74)
Practice (P): F(4, 296) = 28.09
11.1
9.4
13.4
88.4
84.3
79.0
4.12
Number of list retrievals
Age (A): F(2, 74) = 0.34
Younger
Middle-age
Older
F(2, 74)
95.3
93.2
84.8
10.17"
4.0
5.1
13.3
96.4
96.0
87.4
10.47"
Practice (P): F(4, 296) = 2.39
2.17
5.01
5.90
0.80
1.89
1.58
0.33
1.00
1.03
1.12
0.01
4.22
2.97
3.67
0.21
0.50
1.34
0.75
A × P: F(8, 296) = 0.62
3.4
3.0
12.9
95.5
94.0
87.1
4.97*
6.4
5.3
16.0
93.8
93.6
86.0
4.15
5.1
5.8
17.5
A × P: F(8, 296) = 0.55
0.78
1.32
5.54
0.13
0.81
0.92
0.51
0.37
2.89
3.78
0.16
0.86
0.41
1.43
0.17
2.29
0.74
Arithmetic
Number of problems attempted
Age (A): F(2, 74) = 9.97*
Younger
Middle-age
Older
F(2, 74)
Practice (P): F(4, 296) = 57.85*
7.72
4.64
2.10
8.44*
Correct (%)"
Age (A): F(2, 43) = 3.28
Younger
Middle-age
Older
F
74.3
70.1
58.4
2.94
12.91
11.28
11.91
0.51
Ratio of plus to negative operations"
Age (A): F(2, 47) = 0.61
Younger
Middle-age
Older
F
9.78
5.89
2.19
9.36*
7.56 11.93
6.71 7.18
2.83 2.72
9.57*
Practice (P): F(4, 172) = 20.00*
Number of operations per problem a
Age (A): F(2, 50) = 1.40
Younger
Middle-age
Older
F
5.89
5.13
2.50
10.24
4.77
3.21
2.60
14.2 81.9
17.6 79.6
26.7 62.9
(2, 53) 3.71
17.8 78.7
13.1 80.0
31.4 63.7
(2, 50) 2.69
Practice (P): F(4, 200) = 6.40*
3.94 11.52
2.53 10.73
8.28 15.46
(2, 64) 1.80
4.07 10.49
3.09 13.65
14.21 13.40
(2, 58) 0.46
Practice (P): F(4, 188) = 1.46
16.17
5.93
2.93
(2, 62)
7.96
5.02
3.55
1.15
11.76
8.63
4.27
(2, 59)
7.23
5.45
3.86
0.43
A x P: F(8, 296) = 3.86*
9.39 11.84
7.67 7.63
3.77 2.99
8.05*
9.95 16.85
7.89 11.42
4.17 5.28
10.00"
10.42
9.44
6.50
A x P: F(8, 172) = 0.79
23.4 86.7
13.0 80.4
25.4 67.4
(2, 48) 4.53
10.1 84.2
21.8
14.2 82.8
10.4
28.0 66.0
24.8
(2, 51) 5.06* (2, 59)
A x P: F(8, 200) = 2.17
3.29 11.40
16.73 11.15
9.23 12.50
(2, 57) 0.46
3.10 11.71
3.56 10.11
6.47 12.05
(2, 60) 1.49
4.16
2.04
5.81
(2, 66)
A x P: F(8, 188) = 1.22
13.20
10.31
3.31
(2, 53)
9.17
4.08
4.74
1.34
16.19 14.10
4.24 2.72
8.39 3.83
(2, 57) 1.62
39.35
3.95
7.80
(2, 64)
SYNTHETIC WORK
Table 8
327
(continued)
1
Variable
M
2A
SD
M
2B
SD
M
3A
SD
M
3B
SD
M
SD
Visual monitoring
Reset distance
Age (A): F(2, 74) = 0.09
Younger
Middle-age
Older
F(2, 74)
Practice (P): F(4, 296) = 1.23
73.8
76.9
73.7
0.27
Number of lapses
Age (A): F(2, 74) = 4.06
18.4 74.7
18.5 73.7
18.0 71.1
0.20
19.7 74.2
21.0 74.0
20.6 71.7
0.10
A × P: F(8, 296) = 0.59
20.5 71.1
22.9 73.8
21.8 71.2
0.13
Practice (P): F(4, 296) = 4.95*
0.53
0.88
1.63
4.98*
Younger
Middle-age
Older
F(2, 74)
0.71 0.23
1.22 0.73
1.55 1.26
4.71
0.39 0.56
1.15 0.54
1.51 1.02
1.62
24.1
20.6
21.1
72.3
73.9
73.8
0.05
21.9
18.6
18.8
A × P: F(8, 296) = 2.22
1.00 0.40
0.80 0.43
1.39 0.65
0.83
0.82 0.74
0.61 0.47
0.83 1.03
2.42
0.97
0.72
1.15
Auditory monitoring
Hit rate
Practice (P): F(4, 296) = 27.52*
Age (A): F(2, 74) = 18.88"
Younger
Middle-age
Older
F(2, 74)
65.6
26.2 82.0
22.8 85.2
22.8 89.3
18.4 92.1
9.7
46.0
35.4 55.1
37.1 61.5
39.8 65.2
39.2 72.2
33.1
25.3
28.8 26.2
30.2 25.4
33.7 30.4
36.0 39.3
33.6
9.20*
17.47"
17.92"
17.80"
19.62"
False alarm rate
Age (A): F(2, 74) = 0.66
Younger
Middle-age
Older
F(2, 74)
A × P: F(8, 296) = 2.40
Practice(P):~4,296)=3.99*
5.9
3.8
4.2
0.40
13.9
4.4
5.6
4.3
2.3
6.2
0.68
15.1
3.5
16.0
2.9
1.0
2.7
0.62
A xP:~8,296)=1.05
10.1
1.4
7.6
3.1
1.2
4.5
0.94
8.2
2.1
13.3
0.9
1.6
3.8
1.83
2.5
3.1
8.7
aThe dfs differ because some older adults did not complete any arithmetic problems in this session; hence, dfs appears in
parentheses to the right of the respectiveF values.
*p < .01.
monitoring task in both studies. One possibility is
that these differences are a reflection of reduced
tone detection sensitivity because increased age
is frequently accompanied by some hearing loss
(Fozard, 1990). A second possibility is that the
poor performance is attributable to impaired tone
discrimination or localization ability to reduction
and not in auditory detections per se. That is,
because other tones were occurring within the
task after each button press, and also on other
computers in the same room, some participants
may have had difficulty determining which tones
were critical signals. Indeed, several of the participants mentioned this problem in the postexperi-
mental questionnaire. Finally, a third factor that
may have contributed to poor performance in the
auditory monitoring task was an inability to
concentrate on several simultaneous activities.
That is, the auditory monitoring task could have
been deliberately ignored because the other tasks
were perceived to be so demanding. A number of
participants reported that they neglected the auditory task because they felt they could not handle
it along with the other tasks or did not think that
they could discriminate the tones and thus it was
not worth the effort.
In an attempt to examine the plausibility of the
first of these interpretations for the age differ-
328
SALTHOUSE, HAMBRICK, LUKAS, AND DELL
ences in auditory monitoring performance, we
conducted two additional pilot studies. In one of
the pilot studies, 20 of the older adults and 10 of
the younger adults from Study 2 were tested with
the auditory monitoring task alone approximately
4 months after their initial participation. Two
5-min sessions of the auditory monitoring task
were performed in which the high (2092 Hz) and
low (523 Hz) tones were equally likely. The
average hit rate was .99 for the younger adults
and .88 for the older adults, with average false
alarm rates of .01 and .09, respectively. The hit
rate minus false alarm rate difference was therefore .98 for the young adults and .79 for the older
adults when the auditory monitoring task was
performed alone. Particularly for the older adults,
this discrimination level was substantially higher
than the values (see Table 8) of .91 and .35,
respectively, in the last five sessions of Study 2
when the auditory monitoring task was performed concurrently with the other synthetic
work tasks. Furthermore, only 2 of the 20 older
adults had hit rate minus false alarm rate difference scores of less than .50 when the auditory
monitoring was performed alone.
The second pilot study involved 12 older
adults who performed the auditory monitoring
task alone for two 3-rain sessions each with the
tones from Study 1 (i.e., 1046 and 1319 Hz) and
with the tones from Study 2 (i.e., 523 and 2092
Hz) in a counterbalanced order. The average hit
rate minus false alarm rate difference was .89 for
the tones of Study 2 and .65 for the tones of Study
I. None of these 12 participants had differences
with the Study 2 tones of less than .50, and only 4
had hit rate minus false alarm rate differences less
than .50 for the tones used in Study 1. Therefore,
results of these two supplementary studies indicate that although there were some age-related
differences in auditory detection or discrimination ability, the age differences in auditory monitoring performance were much smaller than when
the auditory monitoring was performed concurrently with the other tasks. Thus it appears that
most of the age-related differences in auditory
monitoring performance in the synthetic work
situation were attributable either to difficulties in
dealing with several simultaneous activities or to
deficits in auditory localization, and not to differences in the ability to discriminate between the
high and low tones.
Age differences were also quite pronounced in
the arithmetic task, particularly when it was an
important source of points, as in all sessions of
Study 1 and in the last five sessions of Study 2.
Although numeric abilities have sometimes been
reported as relatively preserved with increased
age (e.g., Schaie & Willis, 1993), the current
arithmetic task was in an unfamiliar format and
placed a premium on rapid responses. Both of
these characteristics probably contributed to the
age differences in the initial sessions, and the
faster processing speed of young adults was
likely responsible for much of their greater
improvement with practice on this task.
There was little relation between the measure
of static visual acuity and performance on the
synthetic work tasks, but it is probably premature
to conclude that visual factors were unimportant
in this situation. For example, stronger relations
might have been found, had measures of dynamic
or peripheral visual acuity, contrast sensitivity, or
eye fixation or saccade duration been available.
Substantial practice-related improvements occurred for most participants. Some of these
improvements were probably attributable to
greater concentration on relevant aspects of the
tasks, as reflected in the reductions in the number
of list retrievals in the memory task and in the
number of lapses in the visual monitoring task.
Increased efficiency in the arithmetic task was
also a major factor contributing to performance
improvements because with additional practice
participants attempted more problems, but fewer
plus and minus operations were used, and the
solution accuracy was higher. As in many realw o r d activities, the highest performance in this
synthetic work situation seems to be achieved by
first mastering the requirements of the externally
paced tasks and then improving efficiency at the
self-paced tasks.
In conclusion, moderately large age differences have been found in a synthetic work
time-management situation with most of those
differences evident within the first 25 min of
performing the tasks. Because the differences
were maintained over 2 hr of performance and
were still evident after an attempt at job redesign
to minimize the sources of difficulty for older
adults, it appears likely that they reflect an
age-related limitation in the ability to perform
several tasks simultaneously. However, this mul-
SYNTHETIC WORK
titasking ability may itself be determined by more
elementary characteristics of processing because
measures of processing speed accounted for over
70% of the variance in the final level of performance in both studies. Regardless of cause, these
differences could have important practical implications because they suggest that older workers
are likely to be somewhat less efficient than
younger workers in at least the initial phases (at
the very least) of many moderately complex jobs.
Although many workers stayed in the same
career in the past throughout their working lives,
this trend appears to be changing. In the future,
more workers of all ages may find themselves
having to learn new jobs. The results of the
current studies seem clear in suggesting that
fairly large age-related differences in proficiency
may be evident in the early phases of learning
these jobs. What cannot be determined from the
current results and what should be an important
priority for future research is the extent to which
those differences can be reduced or eliminated by
providing workers with additional experience or
training.
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Received August 25, 1995
Revision received March 25, 1996
Accepted March 28, 1996 •