On the Impact of Computer Cognitive Training on Working Memory and Fluid
Intelligence
Richard J. Shavelson, Kun Yuan, and Alicia C. Alonzo
Stanford University
Torkel Klingberg
Karolinska Institute
Maria Andersson
CogMed
At one time a not-so-budding artist, Gabi Salomon, turned his attention instead to
understanding the impact of media on cognition. In his characteristically subtle way, he
announced in his dissertation: “Research with different media and modes of presenting
material is not new .... Unfortunately the results of this research are rather meager and
usually either over-generalized or much too specific to serve as guidelines for educational
practice” (1968, p. 16). Fortunately for us, his interest in media was enduring; a major
thrust of his research has been to understand the impact of media’s symbolic forms on
students’ knowing, reasoning and performing.
As Dick Snow pointed out in his preface to Gabi’s classic work, Interaction of
Media, Cognition, and Learning, Gabi’s contribution was not only in his experimental
studies but also in his theorizing in a field that up to that time had been almost devoid of
theory:

Research reported here was supported by a grant from the Wallenberg Global Learning Network... etc.
MARY, IS THERE A STANDARD BLURB TYPICALLY USED FOR SUCH PURPOSES?

Now at the University of Iowa
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Salomon has sought a deep-structure theory … based in an understanding of
the characteristics of symbol systems. He has sought further to show that
these characteristics can shape not only knowledge acquisition and
organization through a particular medium but also the differential
development of cognitive skills (Snow in Salomon, 1979, p. x).
Gabi recognized that media as part of the environment might affect mental
growth. But he argued that until there was experimental evidence that media’s symbol
systems could be made to affect cognition, the search for environmental impact might
prove elusive. To this end, his theorizing led to ingenious experiments in which he
varied “filmic coding elements” to see their impact on mental skills of skilled and
unskilled learners. In one experiment using a Breughel painting, he created three filmic
conditions and a control group: (1) modeling (conjectured to be helpful for unskilled
learners), (2) short-circuiting (alternating between the full painting and a detail back to
full then to another detail—conjecturing little facilitative effect), (3) activation (three
slides of the painting--conjecturing helpful for skillful learners), and (4) control (no
treatment).
His findings were generally consistent with his theoretical conjectures
(Salomon, 1974). In another study, he varied two coding elements—close-up/long shot
and the zoom-in/out—hypothesizing that these two forms might affect cognitive skills
differentially; they did (Salomon & Cohen, 1977).
All of this work was done with television and film. Just think what Gabi might do
with computers. In 1986, Gabi and one of us waxed euphoric about the potential: “For
the first time, a genuinely ‘thinking’ interacting technology, the computer, has become
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readily available to education …. [for] extending and shaping the mind” (Shavelson &
Salomon, 1986, p. 4). We went on:
The pedagogical promise of the new information technology is boundless if
we can master the technology. This promise lies partly in mapping the three
attributes of the technology—information, symbol systems, and activities—
onto their mental correlates: knowledge structures, internal modes of symbolic
representation, and cognitive operations, respectively (p. 4).
Now we “fast-forward” (a coding element that probably can be found in
his work) to our work today on computer cognitive training of memory and
executive control functions. We use the symbol-systems and other functions of
today’s computers in an attempt to expand cognition, namely human working
memory capacity and fluid intelligence. As we shall see, Gabi’s theory—that of
capitalizing on coding elements—now over 25 years old, still has lessons for
“next steps” in our work. But we are getting ahead of the story. We now turn to
our research following along the path Gabi set.
Working Memory and Fluid Intelligence
Defining Working Memory
Working memory (WM) is responsible for temporarily maintaining and
manipulating information during cognitive activity (Baddeley, 2002). It is closely related
to a wide range of high-level cognitive abilities such as reasoning, problem-solving, and
learning (Kyllonen & Christal, 1990), and thus is related to academic achievement across
domains (Abu-Rabia, 2003; Daneman & Tardif, 1987; De Smedt, Ghesquiere, &
Verschaffel, 2004; Gathercole, Pickering, Knight, & Stegmann, 2004).
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Early research in this area (Ebbinghaus, 1885; James, 1890; Miller, 1956) focused
on the temporary storage of information, similar to what Atkinson and Shriffrin (1968)
called short-term memory (STM). However, STM could not adequately describe the kind
of temporary memory that complex cognitive tasks require (Shah & Miyake, 1999) and
STM came to be seen as one component of a larger system known as WM. Most
researchers agree that WM includes multiple subsystems working together to activate
task-related information, maintain activation, and manipulate information during
cognitive activity (Miyake & Shah, 1999).
WM is generally considered to serve two functions: storage and executive control.
The storage function of WM is typically characterized as working memory capacity
(WMC), which refers to the amount of information activated and retained during
cognitive functioning.
Baddeley (2002) proposed a WM model in which separate
components are responsible for acoustic/verbal and visuospatial information (such as
shapes, locations, or movements). The existence of phonological and visuospatial stores
in WM is widely accepted (e.g., Barnard, 1999; Oberauer, Süß, Schulze, Wilhelm, and
Wittmann, 2000) and there is evidence for storage of additional types of information,
such as tactile and kinesthetic (Kieras, Meyer, Mueller, and Seymour, 1999).
The executive control function is more controversial. While researchers agree
that WM includes this function (Baddeley, 2002; Baddeley & Hitch, 1974; Barnard,
1999; Cowan, 1999; Engle, Kane, & Tuholski, 1999; Lovett, Reder, & Lebiere, 1999),
they disagree as to how it is realized. The argument centers around the question of
whether the executive control function is carried out consciously, by a separate
component of WM, or whether it is merely the result of interactions between other
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subsystems of WM. Baddeley’s model included a separate central executive, responsible
for allocating attention and coordinating activities between other WM components.
Engle, Kane, and Tuholski (1999), Schneider (1999), and Kieras et al. (1999) support his
notion of a separate and conscious executive control component in WM. However, other
researchers argue that other subsystems of WM and their interactions can account for the
executive control function (e.g., Barnard, 1999; Lovett et al., 1999). Both Barnard and
Lovett et al. successfully predicted performance on WM tasks, using their models of WM
without a separate executive control function.
For our study’s purposes, it is sufficient to note that WM consists of both a shortterm memory function and an executive control function. Computer cognitive training
might affect either or both.
Measuring Working Memory
The lack of consensus about working memory itself is reflected in the variety of
WM measures.
Simple memory span tasks can be thought to measure the storage
component of WM. These tasks present subjects with a series of stimuli, usually letters,
words, digits, shapes, or positions, and require them to recall those stimuli in the same or
reverse order as they were presented. These tasks may be divided into those measuring
phonological storage (e.g., Ackerman, Beier, & Boyle, 2002; Kail & Hall, 2001; Masson
& Miller, 1983; Oberauer, Süß, Schulze, Wilhelm, & Wittmann, 2000; Rosen & Engle,
1997) and visuospatial storage (Cornoldi & Venville, 2003; Corsi, 1972; Della Sala,
Gray, Baddeley, Allamano, & Wilson, 1999; Vecchi & Richardson, 2001). Although
these simple memory span tasks are still used in current studies of WM, most researchers
view them as tapping primarily the storage component of WM and not the control
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function of WM (Conway, Cowan, Bunting, Therriault, & Minkoff, 2002; Engle,
Tuholski, Laughlin, & Conway, 1999). However, some researchers believe backward
simple memory span tasks require more of the control function of WM than forward
simple memory span tasks do (e.g., Oberauer et al., 2000).
To better reflect the dual components of WM (storage and attention control),
Daneman and colleagues (Daneman & Carpenter, 1980; Daneman & Tardif, 1987)
followed Baddeley and Hitch’s (1974) definition of WM to develop a series of dual tasks.
These tasks required simultaneous processing and storage of information. For example,
the processing task for reading-span dual tasks is to read a series of sentences while the
memory task is to recall the last word in each sentence. A number of similar tasks have
been devised, varying the content and difficulty of the processing and memory tasks (e.g.,
Cantor, Engle, & Hamilton, 1991; Daneman & Tardif, 1987; Engle, Nations, & Cantor,
1990; Hambrick & Engle, 2001; Turner & Engle, 1989).
Daneman and Tardif (1987) asserted that the dual tasks measure both the storage
and the control function of WM and viewed them as a more valid WM measurement tool
than simple memory span tasks. Studies have found dual tasks to have higher and more
consistent correlations with higher-order cognitive abilities than do simple memory span
tasks (Engle, Tuholski, Laughlin, & Conway, 1999; Unsworth & Engle, 2006).
For our study, then, we include measures that tap predominantly short-term
memory (memory-span tasks) and measures that tap both short-term memory and the
executive control function (dual tasks). Including both types of measures enables us to
track the impact of computer cognitive training on these two functions of WM.
Relationship between WM and fluid intelligence
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WMC has been found to be correlated with fluid intelligence or the ability to
reason abstractly and adapt to complex or novel situations (Conway, et al., 2002; Engle,
et al., 1999; Kane, Hambrick, & Conway, 2005; Kyllonen & Christal, 1990); at issue,
however, is whether WM is isomorphic with fluid intelligence. Kyllonen and Christal
(1990) found the correlation between WM and fluid intelligence to be 0.82 to 0.88 and
argued that these two constructs were the same. However, correlations reported in other
studies were lower, ranging from 0.20 to 0.85 (Ackerman, Beier, & Boyle, 2005; Beier &
Ackerman, 2004; Jurden, 1995; Kane, et al., 2005; Oberauer, Schulze, Wilhelm, & Süß,
2005). Differences in how WM was measured provide one possible reason for the
different correlations reported. Kyllonen and Christal’s WM measures were more
complicated than typical dual tasks, looking more like problem solving tasks. Conway et
al. (2002) contended that WM might be the most important among many factors that
influence fluid intelligence.
Klingberg et al. (2005) used a computerized cognitive training (CCT) program
called RoboMemo (Cogmed Cognitive Medical Systems AB, Stockholm, Sweden) to
train WM in a randomized controlled experiment. Their results showed that children with
attention-deficit/hyperactivity disorder (ADHD) significantly increased WMC, improved
fluid intelligence, and reduced ADHD symptoms through the training. However, because
children in Klinberg et al.’s study represented a narrow, special population and the study
was implemented in a clinical setting, the results cannot be extended to a larger
population or school settings. Our study attempted to examine the effects of CCT on the
WM and fluid intelligence of typical middle school students.
Purpose and Conjectures
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The purpose of our research was to determine whether CCT could improve
middle school students’ WM and if so, what, if any, influence the training had on their
intelligence. We conjectured that CCT would improve students’ WM, especially shortterm memory, and that changes in WM would lead to an increase in fluid intelligence.
Methods
Design and Participants
A randomized controlled experiment was carried out at a middle school in
northern California in 2006. Thirty-seven students (13 girls and 24 boys) who chose to
participate in the study (and forgo an elective class) were given tests of WM and fluid
intelligence and then randomly assigned to CCT or a control condition (Figure 1). In both
conditions, students received five weeks of training and took the same set of tests
afterward. The average age of the participants was 13.5 years old (SD = .70). Eight
students were diagnosed as having attention deficit disorder (ADD), attention
deficit/hyperactivity disorder (ADHD), or a learning disability (LD). Analyses comparing
regular and diagnosed students did not find any significant differences on our measures.
Therefore, these eight students were included in the analysis with regular students.
Exp.
Worked on WM training for 30-40
(N=18) minutes a day, 5 days a week, 5 weeks.
Pretest
(N=51)
Randomly
Assigned
Ctr.
Worked on computerized science
(N=19) lessons and low WM training for 30-40
minutes a day, 5 days a week, 5 weeks.
Figure 1. Research design.
Training
8
Posttest
(N=37)
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CCT was conducted in a computer lab at the middle school (see Figure 2) for five
days a week, 25 days in total. Notice that students sat individually at tables facing the
wall and wore headphones as they worked on their laptops. Our intent was to prevent
distractions so that students could fully concentrate on their work.
Figure 2. Training setting.
Students in the experimental group worked on 10 different WM exercises
included in the CCT program RoboMemo® (2005, Cogmed Cognitive Medical Systems
AB, Stockholm, Sweden). The CCT included three letter span tasks, two backward digit
span tasks, and five visuo-spatial WM tasks, including static visuo-spatial tasks (one 2D
visuo-spatial task, one 3D visuo-spatial task), and three dynamic visuo-spatial tasks, in
which students recall the positions of rotated or moving objects (Klingberg et al., 2005;
Olesen, Westerberg, & Klingberg, 2004). The program continuously adjusted the
difficulty level of each exercise to match the student’s performance. Students completed
10-15 trials on eight exercises each day for a total of 115 WM trials per day. Training
time averaged about 30 - 40 minutes per day, depending on the exercise set and the
student’s performance level.
Students in the control group worked on computerized science lessons and a
placebo version of the WM training program—MegaMemo (2005, Cogmed Cognitive
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Medical Systems AB, Stockholm, Sweden). Two types of training sessions were
alternated throughout the 25 days of training: Students watched science videos from
www.unitedstreaming.com (Discovery channel programs or similar instructional videos)
for 15-30 minutes and answered relevant questions on one day. On alternating days, they
conducted internet research related to the previous day’s video and worked on the same
WM training exercises as experimental group students did in RoboMemo, but the
difficulty of their exercises remained on a low level instead of adjusting to their
performance.
Six trained coaches were randomly assigned to experiment and control group
students. They were present in the computer lab during the training, watching over
students while they worked, keeping a training log, encouraging students, and providing
feedback once a week based on students’ performance during training. Figure 3 shows an
example of an experimental group student’s performance record which coaches used to
provide feedback. During the feedback meeting with control group students, coaches
discussed their answers on the science questions and their performance in MegaMemo.
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Figure 3. Performance record coaches used to give feedback.
Measures
Five measures were used as indicators of students’ WM and fluid intelligence.
(See Table 1 for brief descriptions.) The internal consistency reliability coefficients for
these measures ranged from 0.76 ~ 0.90 (see Table 2). This finding indicates that these
tests provided reliable measures of the corresponding psychological constructs. We
examined the dimensionality of the WM scores with an exploratory factor analysis using
primary axis factoring and promax rotation with Kaiser normalization rotation. We found
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two factors: an attention control and memory storage factor (dual tasks), and a storage
only factor—numerical WM, and visuospatial WM. The two factor solution accounted
for 81% of the total variance. The correlation between the two factors was 0.52. These
findings are consistent with the corresponding construct definitions and research
literature.
Table 1 Summary of Measures Used in the Study
Measure
Source
Span-board Task:
Simple
Span Task
Cogmed Cognitive Medical Systems AB,
Sweden (Klingberg et al., 2005)
Forward, Backward, Total
Digit Span:
Educational Testing Service (Ekstrom et
al., 1976)
Forward, Backward, Total
Operation Span
Attention and Working Memory Lab,
Georgia Institute of Technology (Conway
et al., 2005; Unsworth et al., 2005)
Reading Span
Attention and Working Memory Lab,
Georgia Institute of Technology (Conway
et al., 2005)
Raven’s Progressive Matrices
Oxford Psychologists Press (Raven, 1998)
Dual Task
Fluid
Intelligence
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Results and Discussion
Among the WM measures, the two dual tasks correlated most highly with each
other (0.72 and 0.74 at pre- and posttest, respectively).. The correlations between Digit
Span and the dual task measures were higher than those between Span-board and the dual
tasks at both pre- and posttest. In addition, the correlation between simple memory span
and the dual tasks increased over the training.
WM scores correlated significantly with fluid intelligence (Raven) at both preand posttest (Table 2; reliabilities on main diagonal). Reading Span showed the highest
correlation with fluid intelligence.
Table 2. Correlation Matrix with Reliabilities on the Main Diagonal
Measure
Pretest
Note:
1.
2.
Span-Board Total
Digit Span Total
Operation Span
Reading Span
Raven
Span-Board
Total
.82
.44**
.35*
.19
.45**
Posttest
Digit Span Operation
Total
Span
.30
.47**
.89
.56*
.49**
.76
.44**
.72**
.37*
.35**
Reading
Span
.47**
.58*
.74**
.79
.57**
Raven
.40*
.36*
.38*
.46**
.82
*Correlation is significant at .05 level (2-tailed); **Correlation is significant at .01 level (2-tailed);
Diagonal cells display test reliability. The reliabilities given are generally the average Cronbach α
on pre- and posttest. For Span-Board, we report the test-retest reliability for the control group.
On average, the experimental group students increased more on WM and fluid
intelligence than did control group students (see Figure 4). We ran a series of analyses of
covariance to test these observed differences statistically, using the posttest as the
dependent variable, the corresponding pretest as the covariate, and training group as the
independent variable. We found that experimental group students’ average improvement
was significantly greater than that of control group students on Span-board and Digit
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Span. However the observed differences on Operation Span and Reading Span were not
statistically significant, although they were in the predicted direction.
Standardized Gain = (Posttest-Pretest)/Standard Deviation
1.20
1.00
0.80
Treatment
Control
0.60
0.40
0.20
0.00
Span-Board Total
Digit Span Total
Operation Span
Reading Span
Raven
Outcome Measures
Figure 4. Comparison of standardized change on all outcome measures.
Since both Span-board and Digit Span mainly measure the ability to remember
information for a short period of time, the significant improvement of the experimental
group students on these two scales showed that CCT effectively improved students’
short-term memory. As indicated in prior literature, Operation Span, and Reading Span
tapped both the ability to remember information and the ability to control different
cognitive tasks at the same time. Thus, experimental group students’ improvement on
both dual tasks indicated that either the training simply improved the memory portion of
the dual tasks or that it also improved students’ control function in WM. Regardless the
change in cognitive control was not as substantial as that in memory capacity.
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On average, the experimental group students improved more on the measure of
fluid intelligence than did the control group students; however, this difference was not
statistically significant. One possible explanation is that the improvement in WM did not
transfer immediately to fluid intelligence. It might take some time for the improvement in
fluid intelligence to become evident. Another possible explanation focuses on the impact
of WM on fluid intelligence for regular students. Klingberg et al. (2005) reported
significant improvement in fluid intelligence for children with ADHD using the cognitive
training program. For children with ADHD, limited WM constrains their informationprocessing ability, which significantly restricts their fluid intelligence. Therefore,
improvement in WM will substantially enhance their fluid intelligence. WM may not
pose the same bottleneck for regular students as for children with ADHD. For regular
students (including the 8 diagnosed students), their WM may be sufficient to process
information such that the same improvement in WM will not lead to a similar significant
increase in fluid intelligence as that for children with ADHD.
Conclusion
Computer Cognitive Training effectively improved experimental students’
memory capacity and attention control, with a greater change in the former than in the
latter, compared to control students’ performance.
While the difference between
experimental and control groups was statistically significant for the memory capacity
measures, the change in scores on the attention-control measures, although in the
predicted direction, was not statistically significant. CCT also increased average scores
on a measure of fluid intelligence although the difference was not statistically significant.
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We conjectured a number of possible explanations for these findings. Paramount
among them was the explanation that the CCT exercises focused on short-term memory
to a greater extent than on cognitive control. This is not to say students could perform the
exercises by relying upon memory alone as task difficulty increased; some cognitive
control had to be involved. However, strategies for controlling attention and handling
multiple pieces of information were not trained directly nor were they modeled by the
symbol systems used in the CCT.
CCT did not train strategies directly. Rather, students’ mental effort at the tasks
was expected to lead to the evolution of personal strategies as task difficulty increased.
This construction of strategies was thought to be a more effective approach than directly
teaching such strategies. This is a theoretical conjecture that can and should be tested
empirically.
However, Gabi would have pointed out that the symbolic capacities of computers
might have been used to model one or more cognitive strategies for handling information
overload in WM. For example, the computer might simulate the chunking of a series of
digits, words, or light sequences. Or it might highlight the relationship among a sequence
of lights presented horizontally and then show what happens when the lights are rotated
by 90 degrees. In both cases, these simulated cognitive strategies would be introduced
and then faded out as student performance improved.
Such use of symbols and
representations with students’ associated actions would simulate the internal cognitive
strategies that students might adopt to reduce task overload and improve their
performance. Again, this conjecture could be tested experimentally.
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Optimistically, if we are able to improve working memory, and correspondingly
intelligence, in a manner that generalizes beyond the specific tasks trained, a wide range
of students might be in a better position to learn from experiences in and out of school.
Gabi’s theorizing about the impact of media on cognition suggests ways in which such
generalization might be had. School tasks that involve multiple sequences of reasoning
might become more accessible to students. Remembering information presented in class
might become easier.
And the capacity to produce complex arguments might be
facilitated. There is some evidence that in special populations of students (ADHD), WM
improvement has led to improvement in reading and mathematics.
Perhaps this is
possible as well for students more generally, especially those currently struggling to keep
up in school.
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