Available online at www.sciencedirect.com
Intelligence 36 (2008) 584 – 606
Working memory and intelligence are highly
related constructs, but why?
Roberto Colom a,⁎, Francisco J. Abad a , Mª Ángeles Quiroga b ,
Pei Chun Shih a , Carmen Flores-Mendoza c
a
Universidad Autónoma de Madrid, Spain
Universidad Complutense de Madrid, Spain
Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
b
c
Received 18 May 2007; received in revised form 5 January 2008; accepted 8 January 2008
Available online 12 February 2008
Abstract
Working memory and the general factor of intelligence (g) are highly related constructs. However, we still don't know why.
Some models support the central role of simple short-term storage, whereas others appeal to executive functions like the control of
attention. Nevertheless, the available empirical evidence does not suffice to get an answer, presumably because relevant measures
are frequently considered in isolation. To overcome this problem, here we consider concurrently simple short-term storage, mental
speed, updating, and the control of attention along with working memory and intelligence measures, across three separate studies.
Several diverse measures are administered to a total of 661 participants. The findings are consistent with the view that simple shortterm storage largely accounts for the relationship between working memory and intelligence. Mental speed, updating, and the
control of attention are not consistently related to working memory, and they are not genuinely associated with intelligence once the
short-term storage component is removed.
© 2008 Elsevier Inc. All rights reserved.
Keywords: Working memory; Intelligence; Short-term storage; Mental speed; Executive functioning; Updating; Controlled attention
There are several studies reporting strong relationships, at the latent variable level, between working
memory and intelligence (Ackerman, Beier, & Boyle,
2002, 2005; Colom, Rebollo, Palacios, Juan-Espinosa, &
Kyllonen, 2004; Colom, Abad, Rebollo, & Shih, 2005;
Colom & Shih, 2004; Conway, Cowan, Bunting,
Therriault, & Minkoff, 2002; Kane, Hambrick, Tuholski,
Wilhelm, Payne, & Engle, 2004; Kyllonen & Christal,
⁎ Corresponding author. Facultad de Psicología, Universidad
Autónoma de Madrid, 28049 Madrid, Spain. Tel.: +34 91 497 41 14
(Voice).
E-mail address: [email protected] (R. Colom).
0160-2896/$ - see front matter © 2008 Elsevier Inc. All rights reserved.
doi:10.1016/j.intell.2008.01.002
1990; Miyake, Friedman, Rettinger, Shah, & Hegarty,
2001; Stauffer, Ree, & Carreta, 1996; Engle, Tuholski,
Laughlin, & Conway, 1999). However, the components
underlying their strong relationship remain mysterious,
despite the research efforts made to date.
We think this is because published reports do not
comprise a comprehensive and concurrent assessment of
the constructs of interest. There are some studies
considering verbal and quantitative tasks only (Conway
et al., 2002; Engle, Tuholski, et al., 1999), whereas others
analyze spatial tasks only (Miyake et al., 2001). There are
some studies measuring working memory and short-term
memory (Colom, Abad, et al., 2005; Colom, Flores-
R. Colom et al. / Intelligence 36 (2008) 584–606
Mendoza, Quiroga, & Privado, 2005; Engle, Tuholski,
et al., 1999; Kane et al., 2004), whereas others measure
working memory and mental speed (Fry & Hale, 1996).
Therefore, researchers undertake hard inferences about
the components presumably underlying the relationship
between working memory and intelligence. Is mental
speed a key component? Is short-term storage capacity? Is
the control of attention? Is executive functioning? Still we
don't know.
1. Overview of the present studies
Working memory tasks comprise short-term storage
plus some sort of processing requirements (Conway,
Kane, Bunting, Hambrick, Wilhelm, & Engle, 2005;
Engle, Kane, & Tuholski, 1999; Miyake & Shah, 1999)
so their correlation with intelligence could be attributed
to storage, processing, or both.
The present studies address the contribution of these
storage and processing components. It must be emphasized from the outset that the tasks modelled for measuring the constructs of interest follow the mainstream.
This underscoring implicates that here we are not
concerned with the question of whether or not executive
tasks, for instance, measure what they intend to. The tasks
are modelled in ways routinely employed in the literature
to tap the considered constructs. Their relationships regarding the working memory–intelligence network are
explored concurrently.
Therefore, short-term storage is operationalized by
simple memory span tasks, whereas working memory is
defined by complex memory span tasks (Colom,
Rebollo, Abad, & Shih, 2006). Engle, Tuholski et al.
(1999) declare that “tasks thought to be good short-term
memory tasks (…) can be performed with relative
removal of attention from the representation of the list
items”, whereas “working memory tasks are characterized as dual tasks in that attention must be shifted back
and forth between the representation of the list items and
the so-called processing component of the task” (p. 314).
Miyake et al. (2001) state: “for simplicity (and to follow
the convention in the field) we hereinafter refer to simple
storage-oriented span tasks with no explicit concurrent
processing as short-term memory span tasks and to
complex span tasks that involve not only a storage
requirement but also an explicit concurrent processing
requirement as working memory span tasks. According
to this classification, traditional verbal span measures
such digit and word spans are considered short-term span
tasks, whereas more complex span measures such as
reading or operation spans are considered working
memory span tasks” (p. 622).
585
Here we measure short-term memory by tasks
requiring the temporary maintenance of verbal, quantitative, or spatial simple items for latter recall, whereas
working memory is measured by tasks requiring processing + storage verbal, quantitative, or spatial information.
Conway et al. (2005) discuss the problem of scoring
procedures for working memory tasks, suggesting that
they should exhaust the information collected with a task.
Therefore, participants' scores were obtained as the
number of correct answers in both the processing and
storage sub-tasks.
Because the processing component is multi-faceted,
we measure mental speed (study 1), mental speed and
executive functioning (study 2), and mental speed,
executive functioning, and controlled attention (study 3)
along with measures of short-term storage, working
memory, and intelligence.
Mental speed is measured by simple verbal, quantitative, and spatial verification tasks. Participants are
requested to verify, as quickly and accurately as possible,
if a given test stimulus is presented within a small sized
memory set. Note we are tapping the construct of mental
speed as a property of the working memory system (i.e.
short-term recognition speed). The design expressly
avoids tapping constructs such as perceptual speed.
We can make this latter argument fully clear by comparing our approach with the study reported by Conway
et al. (2002). According to our view, these researchers
measured speed by tasks that do not tap directly the
construct of interest. Firstly, they used psychometric speed
tests (pattern comparison, letter comparison, and digit
copying) widely known as measures of perceptual speed
(Carroll, 1993). Mental speed may or may not correlate to
perceptual speed, but mental speed, as a component of the
working memory construct, should implicate at least
minimal temporary storage requirements. Secondly, Conway et al.'s (2002) dependent measure was not speed per
se, but the total number of correct responses. Thirdly, it is
difficult to understand the high correlation between shortterm memory and perceptual speed (.40), but the very low
correlation between perceptual speed and working
memory (−.06) in their study. Finally, the correlation
between the perceptual speed factor and intelligence was
surprisingly low (.07). For these reasons, we are inclined to
suggest that, although interesting, Conway et al.'s (2002)
operationalization of the speed component of the working
memory construct should be seen with reservations.
Executive functioning is usually defined by the control
and regulation of mental processes. Miyake, Friedman,
Emerson, Witzki, and Howerter (2000) as well as Friedman
et al. (2006) analyzed factors representing three executive functions: inhibition, shifting, and updating. Inhibition
586
R. Colom et al. / Intelligence 36 (2008) 584–606
implicates the suppression of automatic responses, shifting
requires switching between tasks, and updating is based
on the on-line addition or subtraction of information from
the working memory system. Given that Friedman et al.
(2006) found that only updating was genuinely related
to intelligence, we focus here on tasks measuring this
executive function (except in study 2, where shifting is also
measured).
Regarding the control of attention, we follow Baddeley's (2002) advice: “the capacity to focus available
attentional capacity is clearly an important feature of the
central executive. It is however important to acknowledge
that not all tasks, or indeed all complex tasks, are heavily
dependent on this capacity” (p. 90). Therefore, it is interesting to have specific measures of controlled attention.
The control of attention can be defined as the ability to
maintain mental representations in a highly active state in
the presence of interference (Engle, Kane, et al., 1999).
Kane and Engle's (2002) review nominates as measures of
controlled attention the flanker task, the antisaccade task,
or the stroop task, among others (see also Heitz & Engle,
2007). Thus, we measure this construct by means of a
quantitative version of the flanker task (Eriksen & Eriksen,
1974) and a version of the Simon task (Simon, 1969).
Finally, intelligence is measured by several standardized tests. These tests generally tap the constructs of fluid
intelligence (Gf), crystallized intelligence (Gc), and
spatial intelligence (Gv). Further, these constructs are
collapsed into a single higher-order factor thought to
represent general intelligence (g). We acknowledge that
other classifications are possible, like perceptual vs.
verbal tests (see Johnson & Bouchard, 2005) but we focus
on g, so this is not especially relevant here.
In summary, here we consider a broad spectrum of
verbal, quantitative, and spatial cognitive tasks and tests in
order to define factors for working memory, short-term
memory, mental speed, executive functioning, controlled
attention, and general intelligence as representative as
possible (Ackerman et al., 2005). We are looking for the
short-term storage and discrete processing components
that predict the relationship between working memory
and intelligence.
2. Study 1
2.1. Method
2.1.1. Participants
One hundred and eleven participants (70% females)
took part in the study to fulfil course requirements. They
were recruited at high school (35%) and college
institutions (65%). Their mean age was 18.0 (SD = 2.7).
2.1.2. Measures
Short-term memory was measured by three tasks
requiring the temporary maintenance of verbal, quantitative, or spatial simple items for latter recall: forward
letter span, forward digit span, and Corsi Block.
Forward letter span (FLSPAN) and forward digit
span (FDSPAN). Single letters or digits (from 1 to 9)
were presented on the computer screen at the rate of
one letter or digit per second. Unlimited time was
allowed to type in direct order the letters or digits
presented. Set size of the experimental trials ranged
from three to nine items (7 levels × 3 trials each = 21
trials total). Letters or digits were randomly grouped
to form trials. The score was the number of
accurately reproduced trials.
Corsi Block. Nine boxes were shown on the
computer screen. Three different configurations of
boxes changing on each trial were used. One box at a
time turned orange for 650 ms each and the order in
which they were sequentially highlighted must be
remembered. There was unlimited time to respond.
The sequences of the experimental trials increased
from 3 to 9 (7 levels × 3 trials each = 21 trials total).
The score was the number of boxes reproduced
appropriately.
Working memory was measured by three tasks
requiring processing + storage verbal, quantitative, or
spatial requirements: alphabet (Kyllonen & Christal,
1990), computation span (Ackerman et al., 2002), and
letter rotation (Miyake et al., 2001).
Alphabet. Successor and predecessor operations must
be applied to a string with a given number of letters.
If the first screen presented the letters B, L, A and the
second screen displayed the operation + 1, then the
correct response was C, M, B. The string of letters
was presented for 3 s, the operation to apply was
presented for 1.5 s, and there were unlimited time to
enter a response. The trials increased the number of
letters from three to seven (5 levels × 4 trials each = 20
trials total). For two trials within a given block 1 or 2
positions must be added, while for the other two trials
1 or 2 positions must be subtracted. The type of
addition and subtraction was randomized within a
given block of trials. The score was the number of
correct trials.
Computation span. This task included a verification
task and a recall task. 6 s was allowed to see a math
equation (but no time limit was set to verify its
accuracy) like (10/2) + 4 = 8, and the displayed
R. Colom et al. / Intelligence 36 (2008) 584–606
solution, irrespective of its accuracy, must be
remembered. After the final equation of the trial
was displayed, the solutions from the equations must
be reproduced in their correct serial order. Each math
equation included two operations using digits from 1
to 10. The solutions were single-digit numbers. The
experimental trials ranged from three to seven
equation/solutions (5 levels × 3 trials each = 15 trials
total). The score was the number of correct answers
in the verification and recalling tasks.
Letter rotation. Several capital letters were presented
sequentially and they can be displayed normal or
mirror imaged. Further, the letters can be rotated in
one of seven orientations (multiples of 45°). There
was a verification task (is the letter normal or mirror
imaged?) and a recall task (the orientation of the
displayed letters — where was the top of each letter
pointing?). The letters were presented for a maximum of 3 s, but no time limit was set to deliver the
normal or mirror imaged response. After each set, a
grid was depicted to mark the places corresponding
to the positions of the tops of the presented letters in
their correct serial order. The experimental trials
increased progressively in size from two to five
letters (4 levels × 3 trials, 12 trials total). The score
was the number of correct answers in the verification
and recalling tasks.
Verbal, quantitative, and spatial mental speeds were
measured by verification tasks (short-term recognition
speed). Participants were requested to verify, as quickly
and accurately as possible, if a given test stimulus was
presented within a small sized memory set (set sizes
always ranged from two to four or five single items).
The participants pressed the computer key 1 for a “yes”
answer and the computer key 0 for a “no” answer.
Two main dependent speed measures were used: the
standard score, based on mean reaction time to all the
trials from correct answers only, and the elementary
score, based on the simplest trials (set size = 3 for verbal
speed and set size = 2 for quantitative and spatial speeds)
from correct answers only. This distinction was thought
to ensure that we were treating this speed component of
the working memory system with minimal (although not
zero) short-term memory loadings.
Verbal speed. Several letters were sequentially
displayed for 650 ms each. Those letters defined a
memory set comprised by three, four, or five
uppercase and lowercase letters. After the last
displayed letter, a fixation point appeared for
500 ms. Finally, the probe letter appeared in order
587
to decide, as quickly and accurately as possible, if it
had the same meaning as one of the letters presented
within the memory set. Therefore, its physical
appearance (uppercase or lowercase) must be
ignored. Half of the trials requested a positive
answer. The experimental trials ranged from three
to five letters (3 levels × 10 trials each = 30 trials
total). The score was the mean RT for the correct
answers.
Quantitative speed. Several single digits were
sequentially displayed for 650 ms each. Those digits
defined a memory set comprised by two, three, or
four digits. After the last displayed digit, a fixation
point appeared for 500 ms. Finally, the probe digit
appeared in order to decide, as quickly and accurately
as possible, if it can be divided by one of the digits
presented within the memory set. Half of the trials
requested a positive answer. The experimental trials
ranged from two to four digits (3 levels × 10 trials
each = 30 trials total). The score was the mean RT for
the correct answers.
Spatial speed. Several arrows were sequentially
displayed for 800 ms each. Those arrows defined a
memory set comprised by two, three, or four arrows.
The arrows can be displayed in one of seven
orientations (multiples of 45°). After the last displayed
arrow, a fixation point appeared for 500 ms. Finally,
the probe arrow appeared in order to decide, as quickly
and accurately as possible, if it had the same
orientation as one of the arrows presented within the
memory set. The arrows have distinguishable shapes in
order to guarantee that their orientation is both
memorized and evaluated. Half of the trials requested
a positive answer. The experimental trials ranged from
two to four arrows (3 levels × 10 trials each = 30 trials
total). The score was the mean RT for the correct
answers.
Note that in all these computerized tasks, participants
completed a set of three practice trials as many times as
desired to ensure they understood the instructions.
Finally, intelligence was measured by the inductive
reasoning (R) and vocabulary (V) subtests from the
Primary Mental Abilities (PMA) Battery (Thurstone,
1938) and by the rotation of solid figures test (Yela,
1969).
PMA-R. This test comprises 30 letters' series items.
The rule (or rules) underlying a given sequence of
letters [a-c-a-c-a-c-a-c] must be extracted in order to
select a given letter from a set of six possible
alternatives [a-b-c-d-e-f]. Only one alternative is
588
R. Colom et al. / Intelligence 36 (2008) 584–606
Table 1
Correlation matrix, descriptive statistics, and reliability indices (study 1)
Tasks and tests
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
1. Forward letter span
.63 .44 .58
.59
.44
.16
.02
.06
.25
.10
.10
.39 .17
.45
2. Forward digit span
.34 .39
.53
.32
.11
−.10
.04
.23
.01
.02
.30 .21
.32
3. Corsi Block
.35
.39
.48
.17
.00
.10
.24
.11
.19
.45 .39
.31
4. Alphabet
.55
.45
.12
.12
.17
.19
.22
.25
.44 .22
.58
5. Computation span
.50
.21
.19
.14
.30
.32
.27
.50 .28
.37
6. Letter rotation
.23
.09
.11
.24
.20
.21
.41 .22
.34
7. Standard verbal speed
.41
.47
.83
.53
.59
.25 .02
.23
8. Standard quantitative
.49
.37
.82
.49
.31 .19
.17
speed
9. Standard spatial speed
.43
.44
.78
.21 .18
.21
10. Elementary verbal speed
.51
.51
.34 .06
.21
11. Elementary quantitative
.53
.39 .15
.28
speed
12. Elementary spatial
.23 .24
.26
speed
13. PMA-reasoning
.44
.55
14. Rotation of solid figures
.25
15. PMA-vocabulary
Mean
9.3 11.3 9.6 5.8 14.2 45.6 932
1724
1032
843
1461
911
18.4 7.7 26.7
Standard deviation
2.8
3.3 2.8 3.8
6.5
9.4 306
695
301
327
659
309
6
3.9
9.2
Reliability (α)
.88
.87 .83 .85
.91
.77
.74
.91
.74
.81
.80
.83
.90 .83
.91
Note: Correlations of speed measures with the remaining measures are reflected for clarity of interpretation.
correct. The score was the total number of correct
responses.
Rotation of solid figures. This test comprises 21
items. Each item includes a model figure and five
alternatives must be evaluated against it. The
participant must evaluate which alternative can be
rotated within a 3D space to fit the model figure.
Only one alternative is correct. The score was the
total number of correct responses.
PMA-V. This is a synonym test that comprises 50
items. The meaning of four alternative words must be
evaluated against a given word that serves as model.
For instance, STOUT: Sick–Fat–Short–Rude. Only
one alternative is correct. The score was the total
number of correct responses.
2.1.3. Procedure
Testing took place in three sessions. The cognitive
tasks and intelligence tests were administered either
individually or collectively (in groups of no more than
10 participants) for a total of 3 h/sessions approximately.
The first and second sessions were dedicated to
cognitive tasks, whereas the third session was dedicated
to intelligence testing.
2.2. Results
The descriptive statistics, raw correlations, and reliability indices are shown in Table 1.
SEM analyses are conducted using AMOS 5.0
(Arbuckle, 2003). The models are assessed by the next
fit indices. The CMIN/DF (Chi Square/Degrees of
Freedom) ratio is first considered given that it is usually
taken as a rule of thumb (Jöreskog, 1993). Values
showing a good fit must be around 2.0 or lower. Second,
the RMSEA index is sensitive to misspecification of the
model. RMSEA values between 0 and .05 indicate very
good fit, values between .05 and .08 indicate reasonable
fit, and values greater than .10 indicate poor fit
(Jöreskog, 1993; Byrne, 1998; Ackerman et al., 2002).
Finally, CFI is also reported; acceptable values must be
larger than .90 (Marsh, Balla, McDonald, 1988).
First, we explore the relationships among short-term
memory, working memory, and standard mental speed.
The fit of this model is quite good: χ2 (24) = 28.95,
CMIN/DF = 1.2, RMSEA = .043, CFI = .98. Fig. 1 depicts the coefficients between these constructs.
Fig. 1 shows a large relationship between short-term
memory and working memory (.89). Further, there is a
significant relation between working memory and mental
speed (.31), whereas the relation between short-term
memory and mental speed is non-significant (.12, p = .33).
Thus, working memory shares a significant amount of
variance with the short-term memory factor, as well as
with the mental speed factor.
The small (and non-significant) relationship between
short-term memory and mental speed is consistent
with the negligible storage requirement of the modelled
R. Colom et al. / Intelligence 36 (2008) 584–606
Fig. 1. Confirmatory factor analysis (CFA) testing the relationships
among short-term memory (STM), working memory (WM), and
mental speed (Speed). Elementary speed results are shown in
parenthesis. FLSPAN = forward letter span, FDSPAN = forward
digit span. Dashed lines indicate no significant relations.
measures of mental speed. This is worth noting, because
it could be argued that the low demanding short-term
storage requirement of these mental speed tasks reduces
their quality as measures of speed mainly. The results
tell that this is not the case.
Nevertheless, to ensure that the current findings are
solid, we test a new model using the elementary speed
589
score, based on the simplest trials (set size = 3 for verbal
speed and set size = 2 for quantitative and spatial speeds)
from correct answers only. The fit of this model is very
good: χ2 (24) = 31.5, CMIN/DF = 1.3, RMSEA = .053,
CFI = .98. Fig. 1 depicts the coefficients (in parenthesis)
which are almost identical to those obtained from the
standard speed scores.
Second, the relation between short-term memory and
intelligence, between working memory and intelligence,
and between mental speed and intelligence is tested, but
in separate analyses. The coefficients are .65, .82, and
.42, respectively (for elementary mental speed the
coefficient is .50). Therefore, short-term memory,
working memory, and mental speed are significantly
related to intelligence.
Third, a model in which the short-term storage and
mental speed factors predict working memory is tested
(Fig. 2). The fit of this model is excellent: χ2 (25) = 29.9,
CMIN/DF = 1.2, RMSEA = .042, CFI = .98. Short-term
storage predicts working memory with a value of .88,
whereas mental speed shows a value of .23. When the
elementary speed score is considered, the fit of the
model is also appropriate: χ 2 (25) = 35.1, CMIN/
DF = 1.4, RMSEA = .061, CFI = .97. Further, the regression weights are pretty the same: .86 for short-term
storage and .35 for mental speed.
Finally, the general model is tested. Short-term storage
and mental speed predict working memory. Short-term
storage, mental speed, and the working memory residual
(variance unpredicted by storage and speed) predict the
intelligence factor. The working memory residual (WM-r)
is obtained from the variance unexplained by the shortterm memory and mental speed factors. The fit for this
model is reasonable: χ2 (49) = 73.79, CMIN/DF= 1.5,
RMSEA = .068, CFI = .94.
Fig. 2. Structural equation model (SEM) testing the relationship of short-term memory and mental speed with working memory. Elementary speed
results are shown in parenthesis.
590
R. Colom et al. / Intelligence 36 (2008) 584–606
Fig. 3 shows that the coefficient between short-term
memory and intelligence is .63, whereas the coefficient
between mental speed and intelligence is .41. Both
coefficients are significant (p b .01). However, the
coefficient between the working memory residual
(WM-r) and intelligence (.38) is not statistically
significant (p = .11). These results indicate that shortterm memory and mental speed predict intelligence,
whereas the working memory factor (with short-term
storage and speed partialed out) does not.
Once again, to know if the current findings are solid,
we test a new model using the elementary speed score
(based on the simplest trials only). The fit of this model
is reasonable: χ 2 (49) = 84.79, CMIN/DF = 1.7,
RMSEA = .081, CFI = .92. Fig. 3 depicts the coefficients
(in parenthesis) which are almost identical to those
obtained from the standard speed scores. The coefficient
between short-term memory and intelligence is .59,
whereas the coefficient between mental speed and
intelligence is .45. Both coefficients are significant
(p b .01). However, the coefficient between the working
memory residual (WM-r) and intelligence (.34) is not
statistically significant (p = .22). This result reinforces
the conclusion that short-term memory and mental
speed predict intelligence, whereas the working memory
factor (with short-term storage and speed partialed out)
does not.
2.3. Discussion
This study shows that short-term storage and mental
speed account for the relationship between working
memory and intelligence. Note that 88% of the working
memory variance is explained by short-term storage and
mental speed. Further, the residual working memory
factor (with short-term storage and mental speed
partialed out) does not predict individual differences in
intelligence: the high coefficient linking working
memory (comprising short-term storage and mental
speed) and intelligence (.82) turn to be non-significant
when short-term memory and short-term recognition
speed are statistically removed.
Beyond this general finding, the results have several
further points of interest.
First, short-term memory and working memory share
79% of their variance, whereas working memory and
mental speed share 9% of their variance. This suggests
that short-term storage is the main component explaining the relationship between working memory and
intelligence.
Fig. 3. General model testing the relationship of short-term memory, mental speed, and the working memory residual with the intelligence factor.
Elementary speed results are shown in parenthesis. FLSPAN = forward letter span, FDSPAN = forward digit span, STM = short-term memory, WM =
working memory, WM (r) = working memory residual, Intell = intelligence, PMA-R = reasoning subtest from the Primary Mental Abilities Battery, Solid
Figure = rotation of solid figures, PMA-V = vocabulary subtest from the Primary Mental Abilities Battery. Dashed lines indicate no significant relations.
R. Colom et al. / Intelligence 36 (2008) 584–606
To appropriately evaluate the high shared variance
between short-term and working memory it is necessary to
consider the accumulated empirical evidence: (a) analyzing verbal measures, Engle, Tuholski et al. (1999)
and Conway et al. (2002) found that these constructs
share 46% and 67% of their variance respectively;
(b) considering spatial measures, Miyake et al. (2001)
found that they share 74% of their variance; (c) measuring
verbal and spatial measures, the re-analysis performed by
Colom, Abad et al. (2005) from the Kane et al.'s (2004)
dataset found that they share 98% of their variance; (d) like
Kane et al. (2004), Colom, Abad et al. (2005) considered
verbal and spatial short-term and working memory
measures, finding that they share 79% of their variance.
Therefore, the average shared variance can be roughly
estimated at a value of 75%. Thus, the evidence suggests
that short-term and working memory do not reflect sharply
distinguishable cognitive limitations (r = .87).
Second, mental speed is related to working memory,
but not to short-term memory. This suggests that the shortterm storage and mental speed components of the working
memory system are only weakly related. Therefore, their
contribution to the prediction of intelligence might be
considered separately. Further, the contribution of the
short-term storage component of working memory must
be seen as much more relevant than that of mental speed,
which suggests, as stated before, that the temporary
storage of the information is the main factor underlying
the relationship between working memory and intelligence (Colom & Shih, 2004; Colom, Flores-Mendoza,
et al., 2005; Colom, Rebollo, et al., 2006).
Third, the relation between short-term memory and
intelligence is almost the same as the relation between
short-term memory and intelligence when the four-way
relationship among short-term memory, working memory, mental speed, and intelligence is analyzed. This result
is consistent with the finding reported by Colom, Abad
et al. (2005) after the analysis of the three-way relationship among short-term memory, working memory, and
intelligence. However, both findings are not consistent
with the assumption held by Kane et al. (2004), namely,
that the shared variance between working memory and
short-term memory reflect executive functioning rather
than common short-term storage. Indeed, Colom, Abad
et al.'s (2005) findings are consistent with Engle, Tuholski
et al. (1999) and Conway et al. (2002) given that the factor
representing common storage does not change its nature
when the storage component is statistically extracted from
the working memory factor.
Fourth, the relation between mental speed and
intelligence is very close in magnitude to the relation
between mental speed and intelligence when the four-
591
way relationship among short-term memory, working
memory, mental speed, and intelligence is analyzed
(regardless of the consideration of standard or elementary mental speed scores). Thus, the factor representing
common mental speed does not change its nature when
this processing component is statistically extracted from
the working memory factor.
Finally, the results are not consistent with the theory
proposed by Engle, Kane et al. (1999). This theory
assumes that short-term storage has little to do to predict
the relationship between working memory and intelligence, as noted above. Further, mental speed should not
contribute to the relation between working memory and
intelligence. The theory predicts that the central executive
(controlled attention) component of the working memory
system is responsible for the relationship between working
memory and intelligence. Crucially, their theory postulates
that controlled attention must be distinguished from shortterm storage and mental speed. The implication is that the
residual working memory component obtained after
partialing out short-term storage and mental speed must
predict individual differences in intelligence. However,
the results of this first study indicate that this is not likely.
In conclusion, this first study shows that the ability of
working memory to predict individual differences in
intelligence is no longer statistically significant once its
short-term storage and short-term recognition speed
components are partialed out. This finding is not consistent with theoretical accounts appealing to other
working memory components like executive functioning
or controlled attention.
Nevertheless, we acknowledge that direct measures of
executive functioning and controlled attention must be
explicitly considered to strengthen this main conclusion.
Factors representing these cognitive functions are
required, in addition to short-term storage and mental
speed. With this purpose in mind, the second study focuses
on the same constructs considered in the first study, but the
role of executive functioning is also explicitly addressed.
3. Study 2
3.1. Method
3.1.1. Participants
261 university undergraduates (80% females) took
part in the study. They participated to fulfil a course
requirement. Their mean age was 20.2 (SD = 3.4).
3.1.2. Measures
Short-term memory was measured by forward letter
span (FLSPAN), forward digit span (FDSPAN), and dot
592
R. Colom et al. / Intelligence 36 (2008) 584–606
memory. FLSPAN and FDSPAN were the same as in
study 1.
The dot memory task was modelled after Miyake
et al. (2001). One five × five grid was displayed for
750 ms at the computer screen. Each grid had between
two and seven spaces comprising solid dots. After the
grid presentation, the locations that contained dots must
be recalled, clicking with the mouse on an empty grid.
The experimental trials increased from two to seven dots
(6 levels × 3 trials each = 18 trials total). The score was
the number of dots correctly reproduced.
Working memory was measured by reading span,
computation span, and dot matrix. Computation span
was the same task administered in study 1.
The reading span task was modelled after Kane et al.
(2004). Participants verified which discrete sentences,
presented in a sequence, did or did not make sense.
Sentences were adapted from the Spanish standardization of the Daneman and Carpenter's (1980) reading
span test (Elosúa, Gutiérrez, García-Madruga, Luque, &
Gárate, 1996). Each display included a sentence and a
to-be remembered capital letter. Sentences were 10–15
words long. As soon as the sentence–letter pair
appeared, the participant verified whether it did or did
not make sense (it did half the time) reading the capital
letter for latter recall. Once the sentence was verified by
pressing the answer buttons (yes/1–no/2) the next
sentence–letter pair was presented. At the end of a
given set, participants recalled, in their correct serial
order, each letter from the set. Set sizes of the
experimental trials ranged from 3 to 6 sentence/letter
pairs per trial, for a total of 12 trials (4 levels × 3 trial = 12
trials total). The score was the number of correct
answers in the verification and recalling tasks.
The dot matrix task was modelled after Miyake et al.
(2001). A matrix equation must be verified and then a
dot location displayed in a five × five grid must be
retained. The matrix equation required adding or
subtracting simple line drawings and it was presented
for a maximum of 4.5 s. Once the response was
delivered, the computer displayed the grid for 1.5 s.
After a given sequence of equation–grid pairs, the grid
spaces that contained dots must be recalled clicking with
the mouse on an empty grid. The experimental trials
increased in size from two to five equations and dots (4
levels × 3 trials = 12 trials total). The score was the
number of correct answers in the verification and recalling tasks.1
1
Fig. 1 in Colom, Escorial, Shih, and Privado (2007) shows an
example for the dot matrix task.
Mental speed was measured by the same short-term
recognition speed tasks administered in study 1.
However, given that we did not find any effect for the
distinction between standard and elementary scores,
only standard scores were considered in study 2.
Executive functioning (updating and shifting) was
measured by 2-back, keep track, and number–letter. The
2-back task was modelled after Hockey and Geffen
(2004). Upper and lower case letters (B, b, D, d, F, f, N, n)
were presented in one of eight equidistant spatial locations
around the center of a computer monitor. These positions
were 30, 60, 120, 150, 210, 240, 300, and 330° from the
vertical axis. No stimuli were presented on the X-axis (90
and 270°) or the Y-axis (0 and 180°). Stimuli were
presented for 200 ms, and participants had 1300 ms to
respond. There were 66 experimental stimuli of which 21
were target stimuli. Participants pressed the space bar of
the keyboard to make a target response (a letter presented
in the same spatial location 2 positions back in the
sequence). The score was the number of correct answers.
Keep track was modelled after Miyake et al. (2000).
In each trial, participants saw several target categories at
the bottom of the computer screen. Fifteen items,
including two or three exemplars from each of the six
possible categories (Odd, even, vowel, consonant,
lowercase pairs of letters, and uppercase pairs of letters)
were then presented serially and in random order for
1500 ms each, with the target categories remaining at the
bottom of the screen. The task was to remember the last
item presented in each target category and then write
down the items at the end of the trial. For example, if the
target categories were odd, consonant, and even, then, at
the end of the trial, participants recalled the last odd
number, the last consonant, and the last even number in
the list. Therefore, participants had to monitor the items
presented and update their memory representations for
the appropriate categories when the presented item was
a member of one of the target categories. Before the task
begins, participants see all categories and the exemplars
in each to ensure that they know to which category each
item belong and then practice with three target
categories. After the practice trials, they performed
three trials with four target categories, and three trials
with five target categories, recalling a total of 27 items.
The number of items recalled correctly was the dependent measure.
The number–letter task was modelled after Miyake
et al. (2000). A number–letter pair (4B) was presented
in one of four quadrants on the computer screen.
Participants were instructed to indicate whether the
number is odd (by pressing the computer key 1) or even
(by pressing the computer key 0) (2, 4, 6, and 8 for even;
R. Colom et al. / Intelligence 36 (2008) 584–606
3, 5, 7, and 9 for odd) when the number–letter pair was
presented in either of the top two quadrants and to
indicate whether the letter was a consonant (by pressing
the computer key 1) or a vowel (by pressing the
computer key 0) (G, K, M, and R for consonant; A, E, I,
and U for vowel) when the number–letter pair was
presented in either of the bottom two quadrants. The
number–letter pair was presented only in the top two
quadrants for the first block of 32 trials, only in the
bottom two quadrants for the second block of 32 trials,
and in a clockwise rotation around all four quadrants for
the third block of 128 trials. The trials within the first
two blocks required no task switching, whereas half of
the trials in the third block required participants shifting
between these two types of categorization operations. In
all trials participants responded by button press (1 for
even or vowel and 0 for odd or consonant) and the next
stimulus was presented 150 ms after the response. The
shift cost was the difference between the average RTs of
the trials in the third block that required a mental shift
593
(trials from the upper left and lower right quadrants) and
the average RTs of the trials from the first two blocks in
which no shifting was necessary.
Note that for all these computerized tasks, participants completed a set of three practice trials as
many times as desired to ensure they understood the
instructions.
Finally, fluid intelligence was measured by the
abstract reasoning (AR) subtest from the Differential
Aptitude Test (DAT) Battery (Bennett, Seashore, &
Wesman, 1990) and the inductive reasoning (R) subtests
from the Primary Mental Abilities (PMA) Battery
(Thurstone, 1938). Crystallized intelligence was measured by the verbal reasoning (VR) subtest from the
DAT and the vocabulary (V) subtest from the PMA.
Spatial intelligence was measured by the mental rotation
(S) subtest from the PMA and the rotation of solid
figures test (Yela, 1969). PMA-R, PMA-V, and rotation
of solid figures were the same tests administered in
study 1.
Table 2
Correlation matrix, descriptive statistics, and reliability indices (study 2)
Tests and
tasks
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
1. DAT-AR
.61 .48 .48 .49 .33 .32
.27
.31
.24
.41
.48
.17
.22
.36
2. PMA-R
.42 .31 .48 .37 .32
.32
.24
.27
.43
.39
.05
.25
.31
3. PMA–E
.54 .29 .23 .09
.16
.21
.11
.19
.23
.01
.13
.25
4. Solid
.34 .28 .25
.23
.23
.09
.27
.30
.10
.19
.20
figures
5. DAT-VR
.48 .28
.33
.30
.22
.36
.30
.11
.21
.21
6. PMA-V
.23
.20
.24
.17
.27
.23
.08
.15
.20
7. FLSPAN
.58
.23
.36
.45
.40
.13
.05
.09
8. FDSPAN
.27
.36
.46
.42
.13
.17
.13
9. Dot
.15
.34
.37
.06
.04
.20
memory
10. Reading
.40
.39
.08
.04
.01
span
11. Computation
.53
.15
.26
.30
span
12. Dot matrix
.09
.08
.17
13. Verbal
.32
.31
speed
14. Quant.
.43
Speed
15. Spatial
speed
16. 2-Back
17. Keep
track
18. Number–
letter
Mean
23.2 19.2 23.6 7.8 26.9 31.2 9.5 13.0 71.5 49.3 18.6 80.9 892.6 1,659.2 877.5
SD
6.7 4.7 9.9 3.6 5.7 6.9 2.6
2.8
5.2
8.1
5.6
7.7 382.6
730.6 298.6
Reliability (α) .86
.87 .73 .81 .89 .80 .81
.80
.78
.66
.91
.79
.85
.92
.75
16
17
18
.32
.25
.16
.25
.19
.28
.11
.12
.30
.30
.15
.18
.30
.09
.09
.12
.18
.24
.09
.28
.28
.26
.11
.11
.19
.16
.13
.13
.15 .21
.21
.28 .35
.20
.10
.26 .31
.08 .14
.14
.12 .24
.18
.12 .29
.14 .10
.23
12.6 15.2 143.7
4.5
4.9 184.8
NA
.79 NA
Note: Correlations of speed measures with the remaining measures are reflected for clarity of interpretation. NA = not available.
594
R. Colom et al. / Intelligence 36 (2008) 584–606
DAT-AR is a series test based on abstract figures.
Forty items are comprised in this test. Each item
includes four figures following a given rule, and the
participant must choose one of five possible alternatives.
The score was the total number of correct responses.
DAT-VR is a reasoning test comprising 40 items. A
given sentence stated like an analogy must be
completed. The first and last words from the sentence
are missing, so a pair of words must be selected to
complete the sentence from five possible alternative
pairs of words. For instance: … is to water like eating
is to … (A) Travelling–Driving, (B) Foot–Enemy,
(C) Drinking–Bread, (D) Girl–Industry, (E) Drinking–
Enemy. Only one alternative is correct. The score was
the total number of correct responses.
PMA-S comprises 20 items. Each item includes a
model figure and six alternatives must be evaluated
against it. Some alternatives are simply rotated versions
of the model figure, whereas the remaining figures are
mirror imaged. Only the rotated figures must be
selected. Several alternatives could be correct for each
item. The score was the total number of correct
responses (appropriately selected figures — simply
rotated) minus the total number of incorrect responses
(inappropriately selected figures — mirror imaged).
3.1.3. Procedure
Testing took place in four sessions — 1 h each.
Intelligence tests and cognitive tasks were collectively
administered in groups of no more than 20 participants
for a total of 4 h approximately. The first and second
sessions were dedicated to intelligence testing, whereas
the third and fourth sessions were dedicated to cognitive
tasks.
3.2. Results
The descriptive statistics, raw correlations, and
reliability indices are shown in Table 2.
SEM analyses are conducted using AMOS 5.0. The
models are assessed by the same fit indices described in
study 1.
First, we explore the relationships among short-term
memory, working memory, mental speed, and executive
functioning. The fit of this model is very good: χ2 (48) =
82.16, CMIN/DF = 1.7, RMSEA = .052, CFI = .94. Fig. 4
depicts the resulting coefficients between these constructs.
Fig. 4 shows a large relationship between short-term
memory and working memory (.83). Working memory
correlates .38 with mental speed, whereas the relation
between short-term memory and mental speed is .26.
Fig. 4. Confirmatory factor analysis (CFA) testing the relationships among short-term memory, working memory, mental speed, and executive
functioning. FLSPAN = forward letter span, FDSPAN = forward digit span.
R. Colom et al. / Intelligence 36 (2008) 584–606
Note the high correlation (.90) between working
memory and the executive factor.
This latter correlation deserves some comment,
because Table 2 shows that keep track and number–
letter are much higher correlated with computation span
and dot matrix than between themselves and with 2back. Further, keep track shows higher correlations with
the three measures of short-term memory than with the
other executive measures. The implication is that the
executive factor is not particularly well defined, and that
their measures reflect a great storage component.
Note that this is a usual finding and cannot be
attributed to the particular measures modelled in the
present study. Thus, for instance, Miyake et al. (2001)
noticed that although the correlation among executive
tasks are frequently lower than with other withinconstruct correlations “zero-order correlations of this
magnitude (often .30 or less) are common among
executive tasks, partly because they involve a good deal
of variance related to non-executive processes as well as
measurement error” (p. 630). The correlations among
the executive tasks and the remaining measures
discussed above suggest that the non-executive processes of the former are strongly related to temporary
storage.
595
Second, the relation between short-term memory and
intelligence, between working memory and intelligence,
between mental speed and intelligence, and between
executive functioning and intelligence is tested, but in
separate analyses. The resulting coefficients are .56, .69,
.51, and .86 respectively. Therefore, all the cognitive
factors are significantly related to intelligence.
Third, short-term storage, mental speed, and executive functioning predict working memory. This model
reveals which factors are significant predictors of
working memory. However, this model must take into
account the correlation between the predictors. To
overcome this multicollinearity problem, the shortterm storage factor is defined by all the measures
(Conway et al., 2002), whereas the speed and executive
factors are defined by variance orthogonal to the storage
factor (Fig. 5). Importantly, Fig. 5 shows that the nature
of the short-term memory factor remains the same when
the other six measures contribute to its variance (note
the weights for FLSPAN, FDSPAN, and dot memory in
Figs. 4 and 5). The fit of this model is good: χ2 (45) =
95.3, CMIN/DF = 2.1, RMSEA = .066, CFI = .92.
Fig. 5 shows that short-term storage predicts working
memory to a high degree (.85). Interestingly, removing
the simple storage variance from the executive factor
Fig. 5. Structural equation model (SEM) testing the relationship of executive functioning, short-term memory and mental speed with working
memory. Dashed lines indicate no significant relations.
596
R. Colom et al. / Intelligence 36 (2008) 584–606
Fig. 6. General model testing the relationship of short-term memory and the working memory residual with the general factor of intelligence. Specific
measures for fluid intelligence, crystallized intelligence, and spatial intelligence are omitted for simplicity.
has dramatic effects on their measures; all the regression
weights turn to be non-significant. This is largely
consistent with the argument made above concerning
the storage component of the executive measures.
Mental speed does not predict working memory either,
even when the short-term memory factor predicts spatial
speed only.2
Finally, the general model is tested: given that shortterm memory is the single predictor of working memory,
short-term memory (STM) and the working memory
variance unpredicted by STM (WM-r) are allowed to
predict the intelligence factor (Fig. 6). The fit of this
2
One anonymous reviewer tested an alternative model to that
shown in Fig. 5: STM, executive (Exec), and speed factors are
correlated, and these three factors predict WM. The reviewer's
conclusion was: “there is a strong correlation between the STM and
Exec factors, and comparatively speaking, none of the partial
regression weights relating the more elemental factors to WM appears
to be very substantial in impact. For example, the Exec to WM partial
regression weight, although apparently large in magnitude (.84)
comes with a large standard error, with a parameter-to-s.e. t ratio less
than 2.0. The chi-square change of 7 is significant at p b 0.01, but only
marginally. The factorial correlations between Exec and WM,
between STM and WM, and between Speed and WM in the model
are 0.89, 0.83, and 0.39, respectively. These results seem to suggest
that it is the common variability shared between the more elemental
factors, particularly between the STM and Exec factors, that is the
most predictive of WM. Given that the Exec factor in the present
study was defined using variables also involving the STM mechanisms but the STM factor was indicated by variables involving
minimum executive functions, the common variability shared
between the two factors is plausibly more of the STM rather than
the Exec mechanisms”. Therefore, the implication is pretty the same
to that derived from Fig. 5, which reinforces our theoretical
interpretation.
model is very good: χ2 (48) = 81.6, CMIN/DF = 1.7,
RMSEA = .052, CFI = .97.
Fig. 6 shows that short-term storage and the working
memory residual predict the intelligence factor. Therefore, short-term storage does not fully account for the
relationship between working memory and intelligence.
The obtained working memory residual contains
variance, not tapped by short-term memory, accounting
for the relationship with intelligence.
3.3. Discussion
The results comprise several points of interest. First,
short-term storage predicts working memory to a high
degree (72% of explained variance). This is consistent
with study one.
Second, mental speed does not predict working
memory variance. This result is not consistent with
study one and cannot be explained by the fact that shortterm memory is allowed to predict speed measures. Note
that the storage factor actually predicts one single speed
measure.
Third, the executive factor is not a significant
predictor of working memory once its storage component is removed. As Table 2 shows, executive measures
are more related to short-term and working memory
measures than among themselves. Leaving executive
components alone has the effect of erasing their
relationship to the working memory factor.
Fourth, the general model shows that both short-term
storage and working memory variance unpredicted by
the former significantly predict the intelligence factor.
Given that the executive and speed factors are not
related to working memory, these factors were not
R. Colom et al. / Intelligence 36 (2008) 584–606
597
controlled attention will behave as a good predictor of
working memory, and (b) when the storage and attention
components of working memory are statistically
removed, the corresponding residual will be unrelated
to the intelligence factor.
considered further in the general model in which the
intelligence factor acted as the dependent variable.
This latter finding resembles the general model reported
by Colom, Abad et al. (2005). These researchers found that
broadly defined short-term memory and working memory
factors predicted a general intelligence factor. Actually,
they did show that the coefficients for short-term storage
and one working memory factor with its storage component
statistically removed were exactly the same in magnitude.
Colom, Abad et al. (2005) noted that the nature of the
working memory residual factor is largely mysterious.
The present findings suggest that executive functioning
and mental speed are not significant components of
working memory, and, therefore, that there would be
another component still undetected. The obvious candidate is controlled attention, as suggested by Engle, Kane
et al. (1999) and Engle, Tuholski et al. (1999).
Therefore, the third study evaluates the same
constructs considered in the second study plus the
construct of controlled attention. It is predicted that (a)
4. Study 3
4.1. Method
4.1.1. Participants
Two hundred and eighty-nine university undergraduates (80% females) took part in this study. They
participated to fulfil a course requirement. Their mean
age was 20.3 (SD = 2.9).
4.1.2. Measures
Short-term memory was measured by forward digit
span (FDSPAN) and Corsi Block. Both tasks were the
same as administered in study 1.
Table 3
Correlation matrix, descriptive statistics, and reliability indices (study 3)
Tests and
tasks
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
1. APM
− 32
.54 .27 .26 .33 .45 .37 .40 .26 .30 .35 .40
.09
.23
2. PMA-R
.56 .45 .35 .45 .39 .27 .37 .25 .32 .31 .43
.24
.27
3. DAT-AR
.35 .41 .48 .54 .41 .45 .30 .46 .39 .49
.19
.25
4. PMA-V
.41 .30 .31 .18 .24 .26 .26 .22 .25
.25
.25
5. DAT-VR
.40 .36 .23 .30 .26 .24 .20 .30
.26
.26
6. DAT-NR
.40 .24 .35 .25 .33 .43 .41
.28
.26
7. Solid
.56 .52 .14 .36 .32 .35
.22
.33
figures
8. PMA-S
.53 .06 .29 .23 .37
.20
.24
9. DAT-SR
.13 .30 .29 .36
.13
.25
10. FDSPAN
.25 .45 .29
.12
.08
11. Corsi
.38 .41
.14
.28
Block
12. Computation
.44
.13
.14
span
13. Dot matrix
.24
.20
14. Quantitative
.59
speed
15. Spatial speed
16. 2-Back
17. Keep track
18. Letter
memory
19. Quantiative
attention
20. Spatial
attention
Mean
10.7 18.2 12.4 28.5 12.5 9.8 7.1 23.9 13.3 9.1 71.6 63.8 54.1 1,267.6 731.7
SD
2.7
4.5 3.8 7.3 2.9 3.5 3.7 10.7 5.0 2.7 12.7 14.3 5.4 738.6 263.4
Reliability (α)
.66
.84 .79 .89 .60 .70 .80 .73 .86 .81 .80 .93 .69
.93
.88
16 17
18
19
20
.33
.29
.39
.23
.26
.30
.29
.26
.26
.34
.25
.26
.25
.20
.22
.22
.23
.23
.16
.16
.14
.23
.22
.26
.23
.16
.29
.24
.30
.20
.35
.22
.19
.33
.33
.31
.34
.13
.32
.14
.27
.30
.20
.02
.18
.44
.14
.19
.22
.12
.29
.27
.30
.21
.35
.21
.29 .31
.21
.29
.27
.16
.21 .22
.13 .02
.28
.29
.28
.23
.24
.15 .06
.28 .22
.29
.42
.24
.17
.13
.36
.28
.19
.12
.61
14.7 14.6 16.5 621.1 514.4
5.2 4.9 3.9 108.4 99.1
NA
.77 NA
.96
.91
Note: Correlations of speed measures with the remaining measures are reflected for clarity of interpretation. NA = not available.
598
R. Colom et al. / Intelligence 36 (2008) 584–606
Working memory was measured by computation span
and dot matrix. Computation span was the same task
administered in studies 1 and 2. Dot matrix was the same
task administered in study 2.
Mental speed was measured by quantitative and spatial
speeds. These tasks were modelled after the short-term
recognition speed tasks administered in studies 1 and 2,
but 60 trials were considered now. Further, half of the
trials presented one single stimulus in the memory set,
whereas the other half presented two stimuli in the
memory set. Therefore, contrary to study 2, this study
obtained an elementary speed score based on mean
reaction time for the correct answers only.
Executive functioning was measured by 2-back, keep
track, and letter memory. Versions of these same three
tasks were employed by Friedman et al. (2006) to represent the executive construct of updating. 2-Back and
keep track were the same tasks administered in study 2.
Letter memory was modelled after Miyake et al.
(2000). Several letters from a list were presented serially
for 1000 ms per letter. The task was simply to recall the
last four letters presented in the list. To ensure that the task
requires systematic and continuous updating, the instructions required rehearsing the last four letters by mentally
adding the most recent letter and deleting the fifth letter
back. For example, if the letters presented were “L, B, M,
C, N, D, O, E, P, F”, participants rehearsed mentally “L”,
“LB”, “LBM”, “LBMC”, “BMCN”, “MCND”, “CNDO”,
“NDOE”, “DOEP”, and then recalled “OEPF” at the end
of the trial. Instructions emphasized continuous updating
of memory representations until the end of each trial.
Participants performed a minimum of three practice trials,
with a length of seven letters, and they can repeat them as
many times as desired to ensure appropriate understanding. There were six experimental trials of varying
length (15, 17, 19, 21, 23, 25) randomly presented, for a
total of 24 letters recalled. The dependent measure was the
number of letters recalled correctly.
Controlled attention was measured by means of a
quantitative version of the flanker task (Eriksen and
Eriksen, 1974) and a version of the Simon task (Simon,
1969). The quantitative task required deciding, as fast as
possible, if the digit presented in the center of a set of three
digits was odd (by pressing the computer key 1) or even
(by pressing the computer key 0). The target digit (e.g.
odd) can be surrounded by compatible (e.g. odd) or
incompatible (e.g. even) digits. The spatial task required
deciding if an arrow (horizontally depicted) pointed to the
left (by pressing the computer key 1) or to the right (by
pressing the computer key 0) of a fixation point. The
target arrow pointing to a given direction (e.g. to the left)
can be presented at the left (e.g. compatible) or at the right
(e.g. incompatible) of the fixation point. In both tasks,
there were a total of 32 practice trials and 80 experimental
trials. Half of the trials were compatible and they were
randomly presented across the entire session. The mean
reaction time for the incompatible trials was the dependent
measure.3
Finally, fluid intelligence was measured by screening
versions (even numbered items) of the Advanced
Progressive Matrices Test (APM) and the abstract
reasoning (AR) subtest from the DAT; the inductive
reasoning (R) subtest from the PMA was also administered. DAT-AR and PMA-R were described in study 2.
The APM comprises a matrix figure with three rows and
three columns with the lower right hand entry missing.
There are eight alternatives and participants must choose
the one completing the 3 × 3 matrix figure. The score was
the total number of correct responses.
Crystallized intelligence was measured by screening
versions (even numbered items) of the verbal reasoning
(VR) and numerical reasoning (NR) subtests from the
DAT. The vocabulary subtest (V) from the PMA was also
administered. DAT-VR and PMA-V were described in
study 2. DAT-NR consists of quantitative reasoning
problems. For instance:
Which number must be substituted by the letter P if the
sum is correct?
5P þ 2 ¼ 58
ðAÞ3; ðBÞ4; ðCÞ7; ðDÞ9; ðEÞNone of them
The score was the total number of correct responses:
Spatial intelligence was measured by the screening
version of the spatial relations (SR) subtest from the
DAT (even numbered items), the rotation of solid
figures test, and the mental rotation (S) subtest from the
PMA. Rotation of solid figures was described in study 1,
whereas PMA-S was described in study 2. DAT-SR is a
mental folding test. Each item is composed by an
unfolded figure and four folded alternatives. The
unfolded figure is shown at the left, whereas figures at
the right depict folded versions. Participants are asked to
choose one folded figure matching the unfolded figure
at the left. The score was the total number of correct
responses (well chosen folded figures).
3
Note that we did not use mean RT for incompatible trials minus
mean RT for compatible trials as the dependent measure. This is so
because (a) both RTs were very highly correlated (approx. 90) so the
resulting differential score showed low reliability (Jensen & Reed,
1990), (b) this subtraction ignores the fact that some participants are
faster than others, and (c) participants must control their attention
when there is a real conflict or incompatibility (Engle & Kane, 2004).
R. Colom et al. / Intelligence 36 (2008) 584–606
4.1.3. Procedure
Testing took place in four sessions — 1 h each. The
intelligence tests and cognitive tasks were collectively
administered in groups of no more than 20 participants
for a total of 4 h approximately. The first and second
sessions were dedicated to intelligence testing, whereas
the third and fourth sessions were dedicated to cognitive
tasks.
4.2. Results
The descriptive statistics, raw correlations, and
reliability indices are shown in Table 3.
SEM analyses are conducted using AMOS 5.0. The
models are assessed by the same fit indices described in
study 1.
First, the relationships among short-term memory,
working memory, mental speed, executive functioning
(updating), and controlled attention are explored. The resulting model shows a correlation larger than 1.0 between
short-term memory and working memory. Therefore, both
factors are collapsed to define a measurement model in
599
which a general memory span factor (short-term and working memory) is correlated with updating, mental speed,
and controlled attention. The fit of this model is appropriate: χ2 (37) =97.1, CMIN/DF = 2.6, RMSEA = .075,
CFI= .92. Fig. 7 depicts the coefficients between these
constructs.
Fig. 7 shows a high correlation between memory span
and updating (.80), although the memory span factor is
also correlated with the remaining factors: .52 with
controlled attention, and .31 with mental speed.
Second, the relation between memory span and
intelligence, between mental speed and intelligence,
between updating and intelligence, and between controlled attention and intelligence is tested, but in separate
analyses. The resulting coefficients are .84, .47, .79, and
.52 respectively. Therefore, all the cognitive factors are
significantly related to intelligence.
Third, the next model is tested: mental speed, updating, and controlled attention predict memory span.
The predictors are freely correlated. The fit of this model
is reasonable: χ2 (38)=105.9, CMIN/DF=2.8, RMSEA=
.079, CFI=.91.
Fig. 7. Confirmatory factor analysis (CFA) testing the relationships among memory span, updating, mental speed, and controlled attention. FDSPAN =
forward digit span.
600
R. Colom et al. / Intelligence 36 (2008) 584–606
Fig. 8. Structural equation model (SEM) testing the relationship of mental speed, updating, and controlled attention (CA) with memory span. Dashed
lines indicate no significant relations.
Fig. 8 shows that only updating predicts the memory
span factor (.70). Mental speed and controlled attention
are correlated with updating, but they do not predict
memory span (p = .56 and .14, respectively). The
implication is that mental speed and controlled attention
are not genuinely related to memory span.
Finally, the general model is tested. In this model, a
short-term storage factor is defined by all the memory span
measures (STM and WM) plus the executive measures.
This is quite reasonable, because, as Table 3 shows, there
are significant correlations between all these measures.
Note that the simple short-term storage measures show
almost identical weights than in the measurement model
(Fig. 7), which is consistent with the statement that the
nature of the short-term memory factor does not change
when all the measures define the storage factor on the
general model. Therefore, it can be assumed that this
short-term storage factor captures the storage component
of the working memory and executive measures. The
second factor is defined by the working memory measures
only, whereas the third factor is defined by the executive
measures only. Now the assumption is that the working
memory and executive factors capture variance specific
of these constructs partialing out their simple storage
component. Thus, there are three orthogonal factors
predicting the intelligence factor (g). The fit of this
model is appropriate: χ2 (98)= 193.6, CMIN/DF = 2.1,
RMSEA = .061, CFI = .93.
Fig. 9 shows several points of interest. First, the
working memory factor is not related to the intelligence
factor once its storage component is removed. It is
interesting to note the high weights for both computation span and dot matrix on the short-term storage factor.
Further, the weights of these measures on the working
memory factor are no longer significant. Second, the
executive factor (removing its storage component) still
predicts the intelligence factor with a value of .40. This
executive factor is now defined by keep track and 2back, because letter memory shows a non-significant
weight. Finally, the short-term storage factor shows the
highest weight over the intelligence factor (.73).
4.3. Discussion
This study can be considered the final step in the way,
because all the constructs of interest are considered
concurrently. As noted in the introduction, none of the
previously published studies measured in such a
comprehensive way presumably relevant candidate
constructs to account for the strong relationship between
working memory and intelligence.
The findings have several points of interest. First, the
relationship between simple short-term storage and
working memory is so high that their corresponding
factors must be collapsed to define a single factor. This
is largely consistent with the statement that it is very
R. Colom et al. / Intelligence 36 (2008) 584–606
601
Fig. 9. General model testing the relationship of working memory, short-term memory, and updating with the general factor of intelligence (g).
Specific measures for fluid intelligence, crystallized intelligence, and spatial intelligence are omitted for simplicity.
difficult to demonstrate that simple and complex
memory span measures are fuelled by clearly distinguishable mental operations (Colom, Rebollo, et al.,
2006; Colom, Shih, Flores-Mendoza, Quiroga, 2006;
Unsworth and Engle, 2007).
Second, all the considered cognitive factors are
significantly related to the general memory span factor.
The highest correlation is for the executive factor
(updating), but mental speed and controlled attention are
also significantly related to memory span. This finding
shows that all these constructs might have a role to
account for the relationship between memory span and
general intelligence.
Third, mental speed and controlled attention are not
genuinely related to the memory span factor. When
these factors, along with updating, are allowed to predict
memory span, only updating shows a significant weight.
This latter factor correlates with both mental speed
and controlled attention, but it is still significantly
related to the memory span factor, whereas speed and
attention are no longer related to memory span. Thus,
apparently memory span has nothing to do with
mental speed and the control of attention. This finding
is entirely consistent with Buehner, Krumm, and Pick
(2005) who show that (1) attention has no significant
impact on reasoning, and, (2) the common variance
between working memory components and reasoning is
not mediated by speed variance.
Fourth, the findings are consistent with the statement
that simple short-term storage drives the relationship
between working memory and intelligence. The general
model (Fig. 9) shows a large relationship between shortterm memory and intelligence. The working memory
factor is no longer related to intelligence once its simple
short-term storage component is removed.
Finally, updating is genuinely related to intelligence.
Subtracting the simple short-term storage component of
the executive factor does not have the effect of turning
its regression weight with the intelligence factor to the
point of no significance. The implication is that the
updating factor taps something more than simple
storage. Further, the non-storage component of this
executive factor contributes in a non-negligible way to
the prediction of intelligence.
5. Cross-validating SEM
In order to cross-validate the results of the SEM
analyses, we compute first regression analyses in which
a WM composite score was predicted by the remaining
composite scores (STM and mental speed — study 1;
STM, mental speed, and executive functioning — study
602
R. Colom et al. / Intelligence 36 (2008) 584–606
2; STM, mental speed, executive functioning, and
controlled attention — study 3). The WM residual
score (WM-r) representing the WM variance unpredicted by the predictors is computed. Second, given that
executive functioning is highly loaded by temporary
storage requirements (see above), the executive score is
predicted by STM to compute an executive residual
score (executive-r). Finally, STM, mental speed, executive functioning-r, controlled attention, and WM-r
predict the g composite score in a regression analysis.
Hierarchical multiple regressions were applied to
examine the relationship among the scores; STM was
entered first. The findings reveal that WM residuals add
predictive power to that achieved by the remaining
predictors. R values change from .53 to .64 in the first
sample [STM = .53, Speed = .06, WM-r = .05], from .47
to .59 in the second sample [STM = .47, Speed = .06,
WM-r = .03, Executive-r = .03], and from .50 to .71 in
the third sample [STM = .50, Executive-r = .09, WMr = .06, Speed = .05, Attention = .01]. The changes are
statistically significant (p b .01) in all samples. Nevertheless, the contribution of WM residuals is not
especially noteworthy [.05, .03, and .06, respectively].
Therefore, these findings support the statement that
short-term storage (STM) primarily drives the relationship between working memory and intelligence. Mental
speed, executive functioning, and controlled attention
are weakly involved.
6. General discussion
6.1. The central role of simple short-term storage
Here we considered concurrently several mainstream
constructs presumably relevant to understand the large
relationship between working memory and intelligence.
They were progressively incorporated from study 1 to
study 3 in order to gain knowledge in a gradual way.
This general discussion begins underscoring the consistencies and inconsistencies across studies.
First, simple short-term storage is a main working
memory component. The measures administered in the
three studies followed the convention in the field: all of
them did not require any explicit concurrent processing
besides the simple temporary retention of a given
memory set for latter recall. In clear contrast, the
considered working memory measures required shifting
attention back and forth between the mental representation of the relevant information and the concurrent
processing requirement. However, the correlation across
the three studies reported here ranged from .83 to 1.0, a
finding entirely consistent with the .90 relationship
between short-term memory and working memory
summarized in the discussion of study 1. Given this
evidence, one may wonder if there are other constructs
accounting for the working memory variance.
Study 1 showed that short-term storage was 15 times
more important than mental speed to account for the
working memory variance. Study 2 indicated that only
short-term storage predicts the working memory factor.
Finally, study 3 revealed a complex picture: the general
span factor (comprising short-term and working memory measures) was predicted by updating. However,
when a general short-term memory factor was obtained
from the short-term memory, working memory, and
updating measures, genuine/specific working memory
was no longer related to intelligence. In summary, shortterm storage is the best candidate as core component of
the working memory system.
Second, the role of mental speed, defined as shortterm recognition speed, was generally low. In fact, only
study 1 showed a positive relationship with working
memory. But even in such case, the relevance of the
speed component was very low, especially when
compared with that of simple short-term storage (see
also Burgaleta & Colom, in press).
Third, executive functioning represented in studies 2
and 3, mainly by its updating component, was highly
loaded by simple short-term storage requirements. This
was almost the same finding regarding working
memory. Study 2 showed that once the short-term
component of the executive factor is removed, it is no
longer related to working memory. Study 3 revealed a
dramatic reduction of the executive measures' regression weights over their factor when they were allowed to
load on the short-term memory factor. Therefore, the
heavy simple storage load of executive functioning was
a consistent finding across studies 2 and 3.
Fourth, the control of attention did not predict at all
working memory variance. It can be assumed that the
factor representing the control of attention focuses on
the inhibition component of the central executive
(Friedman et al., 2006). If this is the case, then the
implication is that both inhibition and updating are
weakly related to working memory, despite the fact that
the control of attention and updating show significant
and high relationships to working memory on the
corresponding measurement models. Interestingly,
when both factors were allowed to predict working
memory, only updating showed a significant regression
weight. However, even this significant weight was not
especially noteworthy, because removing the simple
short-term storage component of updating reduced
dramatically its relationship with intelligence.
R. Colom et al. / Intelligence 36 (2008) 584–606
In conclusion, the relationship between working
memory and intelligence can be essentially explained
by the short-term storage component of the former.
Mental speed, updating, and the control of attention do
not consistently predict working memory, whereas shortterm storage does.
Nevertheless, we must acknowledge that the reported
findings should not be generalized to the general
population. The samples assessed in the present studies
were mostly comprised by female university undergraduates. As noted by Earl Hunt in his review of the
present paper “as several studies have shown speedrelated declines of intelligence with increasing age, it is
at least arguable that different results would be obtained
with a wider range of adult ages (…). University students
are human (mostly), but there are humans who aren't
university students”. We agree.
6.2. Should we move beyond simple short-term storage?
The theory of Engle and associates supports the view
that executive functioning, especially the control of
attention, accounts for the high correlation between
working memory and intelligence (Conway et al., 2002;
Conway, Kane, & Engle, 2003; Engle & Kane, 2004; Kane
et al., 2004). This theory rejects the role of both simple
short-term storage and mental speed. The findings reported
here support their approach concerning mental speed, but
are in clear contrast regarding short-term storage.4
Beyond the results shown in the present studies, the
findings reported by Ackerman et al. (2002), Colom,
Abad et al. (2005), Colom, Rebollo et al. (2006), and Süß,
Oberauer, Wittman, Wilhelm, and Schulze (2002), among
others, are not consistent with Engle, Kane, et al.'s (1999)
general theory either.
First, Ackerman et al. (2002) did not support the
equivalence of working memory with an underlying
construct of controlled attention. Their analyses concerning both the consistency of stimulus-response mappings
and the changing relation between mental speed and
working memory over speed test practice, failed to find
supporting evidence for the controlled attention model.
Second, Colom, Rebollo et al. (2006) re-analyzed
five key datasets comprising working memory, shortterm memory, and intelligence measures, finding that
short-term storage is a better predictor of intelligence
4
Recently, Unsworth and Engle (2007) proposed that individual
differences in working memory came from two sources: (1) the ability
to temporarily retain information in primary memory and (2) the
ability to recover information from secondary memory. However, we
still don't have specific studies aimed at testing this framework
regarding the relationship between working memory and intelligence.
603
than working memory (with its storage component
partialed out) across datasets. The Colom, Rebollo
et al.'s (2006) report was interpreted by Cowan (2005)
as suggesting that capacity limitations for temporary
storage are of primary interest.
Third, Colom, Abad et al. (2005) found that the
almost perfect relationship between working memory
and intelligence becomes profoundly unstable when the
short-term storage component of the former is partialed
out: “WM and g are (almost) isomorphic constructs,
although that isomorphism vanishes when the storage
component of WM is partialed out. This suggests that
the short-term storage component of the WM system is a
crucial underpinning of g” (p. 637).
Finally, Süß et al. (2002) reported a study supporting
the view that the storage + processing combination is not
that crucial to predict the relationship between working
memory and intelligence: “particularly interesting is that
those tasks that do not require manipulation of information (spatial short-term memory, spatial coordination,
and the two short-term memory versions of memory
updating) are no less related to reasoning than the storage
and processing tasks (reading span, math span, or the
memory updating tasks with updating operations). This
shows that a good predictor of complex cognitive
performance need not necessarily be a combination of
storage and processing demand” (p. 275).
Further, Beier and Ackerman (2004) examined the
relationship between short-term storage and intelligence
after the analyses of two large datasets comprising a high
number of measures. These researchers predicted that the
relationship between short-term memory and intelligence
“would be large and on a par with the relationship
between working memory and intelligence” (p. 617).
Their results confirmed the prediction, finding a high
relationship between short-term memory and intelligence
(from .71 to .83) and concluding that “the relative recent
introduction of working memory tasks as measures of
intelligence may not necessarily add much to the
‘explanation’ of variance in (intelligence) over wellconstructed measures of (short-term memory)” (p. 618).
Finally, Oberauer, Lange, and Engle (2004) published
a study failing to support theories identifying working
memory with the executive abilities to resist interference
or to coordinate two concurrent tasks. Their results
suggest that the difference between working memory
and short-term memory cannot be interpreted as
measuring the added contribution of a general executive
device. The unique predictive power of working memory
tasks cannot be attributed to general executive attention.
We think that the available evidence is overwhelmingly:
working memory is highly correlated with intelligence
604
R. Colom et al. / Intelligence 36 (2008) 584–606
mainly because of its simple short-term storage component.
We have suggested this since the consideration of broadly
defined short-term and working memory factors representing the constructs of interest (Colom, Abad et al., 2005;
Colom, Flores-Mendoza, et al., 2005; Colom, Rebollo,
et al., 2006; Colom, Shih, et al., 2006). However, we
conceded that direct measures of mental speed and
executive functioning, in addition to short-term and
working memory measures, were required to empirically
support this statement. This was exactly the goal of the
studies reported here, and the findings converge: mental
speed, updating, and the control of attention do not
systematically contribute to account for the relationship
between working memory and intelligence.
6.3. The tentative theoretical interpretation
We endorse Cowan's (2005) statement that “the field
of working memory was confused in its use of
terminology because researchers sometimes were identifying the concept with activation and other times were
identifying it with the attended, consciously available
portion of memory and thought” (p. 44). Despite the
efforts made to clarify the situation (Miyake & Shah,
1999) we still do not have a clear cut common framework
of reference and this is especially true when discussing
and researching the relationships between working
memory and intelligence (Ackerman et al., 2005).
How to interpret from theory the findings reported in
the present article? We underscore that this is an
empirically oriented work, and, therefore, we do not
have any particular or preferred theoretical agenda. We
were interested in getting knowledge regarding the
storage and processing components of working memory
that could account for the strong relationship between
working memory and intelligence. The consistency across
studies points to the central role of simple short-term
storage, but some sort of executive component appears to
be involved (updating) especially after the findings
observed in the comprehensive study 3.
If capacity limitations for temporary storage (short-term
memory) and, to a lesser degree, the updating component
of executive functioning, account for the strong relationship between working memory and intelligence, then the
embedded processes model (EPM) proposed by Cowan
(1999) can be taken as a tentative framework.
The fact that individual differences in working
memory are highly related to higher-order cognitive
abilities support the view that both share common
mental resources (Daneman and Carpenter, 1980; Case,
Kurland, & Goldberg, 1982; Cowan, 2001; PascualLeone, 2001). In this vein, Cowan (2005) acknowledges
that “although it is clear that storage and processing
demands are different, it is not as clear to me that they do
not draw on a common resource” (p. 149).
Biologically, frontal and parietal areas are connected
regions with different functions. Frontal lobe damage
produces loss of control, while parietal lobe damage
results in attention problems (Jung & Haier, 2007).
Cowan (1995) have suggested that the frontal lobe
contains “pointers” to the relevant information stored in
the parietal lobe. Therefore, the frontal area keeps the
appropriate neural systems active in order to maintain the
representation of the relevant information. Parietal areas
receive inputs from the senses and could be the brain site
for the representation of integrated information. The
generalization is that frontal areas do not suffice to
account for individual differences in working memory
(Wager & Smith, 2003). This is also true for the general
factor of intelligence (Colom, Jung, & Haier, 2006).
Frontal areas update (control) the relevant information,
while parietal areas hold the updated information within
certain limits. This general framework was taken by
Colom, Jung, and Haier (2007) to interpret their
neuroimaging findings. They determined the overlap in
brain areas where regional gray matter volumes are
correlated to measures of g and memory span, showing
that their common anatomic framework implicates frontal
regions belonging to Brodmann area (BA) 10 (right
superior frontal gyrus and left middle frontal gyrus), along
with the right inferior parietal lobule (BA 40).
In summary, working memory and intelligence are
highly related because they share capacity limits. These
limits refer to both the amount of information that can be
temporarily retained in a reliable state (short-term
storage) and the ability to update the relevant information. Both mechanisms could rely on discrete brain
regions belonging to frontal and parietal areas. Nevertheless, further research is intensively required to test the
likelihood of this tentative psycho-biological model of
the working memory–intelligence relationship.
Acknowledgements
The research referred to in this article was supported
by grants funded by the Spanish Ministerio de Ciencia y
Tecnología (Grant No. BSO2002-01455) and by the
Spanish Ministerio de Educación y Ciencia (SEJ200607890). We would like to thank Miguel Burgaleta, Jesús
Privado, and Aida Aguilera for their assistance during
testing sessions and tasks programming. We also thank
Earl Hunt, Wendy Johnson, Andrew Conway, and one
anonymous reviewer for their helpful comments to
previous versions of this article.
R. Colom et al. / Intelligence 36 (2008) 584–606
References
Ackerman, P. L., Beier, M. E., & Boyle, M. O. (2002). Individual
differences in working memory within a nomological network of
cognitive and perceptual speed abilities. Journal of Experimental
Psychology-General, 131, 567−589.
Ackerman, P. L., Beier, M. E., & Boyle, M. O. (2005). Working memory
and intelligence: The same or different constructs? Psychological
Bulletin, 131, 30−60.
Arbuckle, J. L. (2003). AMOS 5.0, SmallWaters.
Baddeley, A. (2002). Is working memory still working? European
Psychologist, 7, 85−97.
Beier, M. E., & Ackerman, P. L. (2004). A reappraisal of the
relationship between span memory and intelligence via “best
evidence synthesis”. Intelligence, 32, 607−619.
Bennett, G. K., Seashore, H. G., & Wesman, A. G. (1990). Differential
Aptitude Test, (5th Edition) Madrid: TEA.
Buehner, M., Krumm, S., & Pick, M. (2005). Reasoning=working
memory≠attention. Intelligence, 33, 251−272.
Burgaleta, M. and Colom, R., (in press). Short-term storage and mental
speed account for the relationship between working memory and
fluid intelligence. Psicothema.
Byrne, B. M. (1998). Structural equation modelling with LISREL,
PRELIS, and SIMPLIS: Basic concepts, applications, and
programming. Erlbaum: Mahwah.
Carroll, J. B. (1993). Human cognitive abilities. Cambridge: Cambridge
University Press.
Case, R., Kurland, D. M., & Goldberg, J. (1982). Operational
efficiency and the growth of short-term memory span. Journal of
Experimental Child Psychology, 33, 386−404.
Colom, R., Abad, F., Rebollo, I., & Shih, P. C. (2005). Memory span and
general intelligence: A latent-variable approach. Intelligence, 33,
623−642.
Colom, R., Escorial, S., Shih, P. C., & Privado, J. (2007). Fluid
intelligence, memory span, and temperament difficulties predict
academic performance of young adolescents. Personality and
Individual Differences, 42, 1503−1514.
Colom, R., Flores-Mendoza, C., Quiroga, M.Á., & Privado, J. (2005).
Working memory and general intelligence: The role of short-term
storage. Personality and Individual Differences, 39, 1005−1014.
Colom, R., Jung, R. E., & Haier, R. J. (2006). Distributed brain sites
for the g-factor of intelligence. Neuroimage, 31, 1359−1365.
Colom, R., Jung, R. E., & Haier, R. J. (2007). General intelligence and
memory span: Evidence for a common neuro-anatomic framework. Cognitive Neurosychology, 24, 867−878.
Colom, R., Rebollo, I., Abad, F., & Shih, P. C. (2006). Simple span
tasks, complex span tasks. and cognitive abilities: A re-analysis of
key studies. Memory and Cognition, 34, 158−171.
Colom, R., Rebollo, I., Palacios, A., Juan-Espinosa, M., & Kyllonen,
P. C. (2004). Working memory is (almost) perfectly predicted by g.
Intelligence, 32, 277−296.
Colom, R., & Shih, P. C. (2004). Is working memory fractionated onto
different components of intelligence? Intelligence, 32, 431−444.
Colom, R., Shih, P. C., Flores-Mendoza, C., & Quiroga, M. A. (2006).
The real relationship between short-term memory and working
memory. Memory, 14, 804−813.
Conway, A., Cowan, N., Bunting, M., Therriault, D., & Minkoff, S.
(2002). A latent variable analysis of working memory capacity,
short-term memory capacity, processing speed, and general fluid
intelligence. Intelligence, 30, 163−183.
Conway, A. R. A., Kane, M. J., Bunting, M. F., Hambrick, D. Z.,
Wilhelm, O., & Engle, R. W. (2005). Working memory span tasks:
605
A methodological review and user's guide. Psychonomic Bulletin
and Review, 12, 769−786.
Conway, A. R. A., Kane, M. J., & Engle, R. W. (2003). Working
memory capacity and its relation to general intelligence. Trends in
Cognitive Sciences, 7, 547−552.
Cowan, N. (1995). Attention and memory: An integrated framework.
New York: Oxford University Press.
Cowan, N. (1999). An embedded-processes model of working
memory. In A. Miyake, & P. Shah (Eds.), Models of working
memory (pp. 62−101). Cambridge: Cambridge University Press.
Cowan, N. (2001). The magical number 4 in short-term memory: A
reconsideration of mental storage capacity. Behavioral and Brain
Sciences, 24, 87−185.
Cowan, N. (2005). Working memory capacity. New York: Psychology
Press.
Daneman, M., & Carpenter, P. A. (1980). Individual-differences in
working memory and reading. Journal of Verbal Learning and
Verbal Behavior, 19, 450−466.
Elosúa, M. R., Gutiérrez, F., García-Madruga, J. A., Luque, J. L., &
Gárate, M. (1996). Adaptación española del “Reading Span Test”
de Daneman y Carpenter [Spanish standardization of the Reading
Span Test]. Psicothema, 8, 383−395.
Engle, R. W., & Kane, M. J. (2004). In B. Ross (Ed.), Executive attention,
working memory capacity, and a two-factor theory of cognitive
controlPsychology of Learning and Motivation: Advances in
Research and Theory, vol. 4. (pp. 145−199).
Engle, R. W., Kane, M. J., & Tuholski, S. W. (1999). Individual
differences in working memory capacity and what they tell us about
controlled attention, general fluid intelligence, and functions of the
prefrontal cortex. In A. Miyake, & P. Shah (Eds.), Models of working
memory (pp. 102−134). Cambridge: Cambridge University Press.
Engle, R. W., Tuholski, S. W., Laughlin, J. E., & Conway, A. R. A.
(1999). Working memory, short-term memory, and general fluid
intelligence: A latent-variable approach. Journal of Experimental
Psychology-General, 128, 309−331.
Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters upon
the identification of target letter in a non-search task. Perception
and Psychophysics, 16, 143−149.
Friedman, N. P., Miyake, A., Corley, R. P., Young, S. E., DeFries, J. C.,
& Hewitt, J. K. (2006). Not all executive functions are related to
intelligence. Psychological Science, 17, 172−179.
Fry, A. F., & Hale, S. (1996). Processing speed, working memory, and fluid
intelligence: Evidence for a developmental cascade. Psychological
Science, 7, 237−241.
Heitz, R. P., & Engle, R. W. (2007). Focusing the spotlight: Individual
differences in visual attention control. JEP: G, 136, 217−240.
Hockey, A., & Geffen, G. (2004). The concurrent validity and test–retest
reliability of a visuospatial memory task. Intelligence, 32, 591−605.
Jensen, A. R., & Reed, T. E. (1990). Simple reaction time as a suppressor
variable in the chronometric study of intelligence. Intelligence, 14,
375−388.
Johnson, W., & Bouchard, T. J. (2005). The structure of human
intelligence : It is verbal, perceptual, and image rotation (VPR), not
fluid and crystallized. Intelligence, 33, 393−416.
Jöreskog, K. (1993). Testing structural equation models. In K. A.
Bollen, & J. S. Long (Eds.), Testing structural equation models
(pp. 294−315). Newbury Park: Sage.
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration
Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30, 135−187.
Kane, M. J., & Engle, R. W. (2002). The role of prefrontal cortex in
working-memory capacity, executive attention, and general fluid
606
R. Colom et al. / Intelligence 36 (2008) 584–606
intelligence: An individual-differences perspective. Psychonomic
Bulletin and Review, 9, 637−671.
Kane, M. J., Hambrick, D. Z., Tuholski, S. W., Wilhelm, O., Payne,
T. W., & Engle, R. W. (2004). The generality of working memory
capacity: A latent-variable approach to verbal and visuospatial
memory span and reasoning. Journal of Experimental Psychology-General, 133, 189−217.
Kyllonen, P. C., & Christal, R. E. (1990). Reasoning ability is (little
more than) working-memory capacity. Intelligence, 14, 389−433.
Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness of fit
indexes in confirmatory factor analysis: The effect of sample size.
Psychological Bulletin, 103, 391−410.
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., &
Howerter, A. (2000). The unity and diversity of executive functions
and their contributions to complex “Frontal Lobe” tasks: A latent
variable analysis. Cognitive Psychology, 41, 9−100.
Miyake, A., Friedman, N. P., Rettinger, D. A., Shah, P., & Hegarty, M.
(2001). How are visuospatial working memory, executive functioning, and spatial abilities related? A latent-variable analysis. Journal
of Experimental Psychology; General, 130, 621−640.
Miyake, A., & Shah, P. (1999). Models of working memory.
Cambridge: Cambridge Univeristy Press.
Oberauer, K., Lange, E., & Engle, R. W. (2004). Working memory
capacity and resistance to interference. Journal of Memory and
Language, 51, 80−96.
Pascual-Leone, J. (2001). If the magical number is 4, how does one
account for operations within working memory? Behavioral and
Brain Sciences, 24, 136−138.
Simon, J. R. (1969). Reactions toward the source of stimulation.
Journal of Experimental Psychology, 81, 174−176.
Stauffer, J., Ree, M., & Carreta, T. (1996). Cognitive-components tests
are not much more than g: An extension of Kyllonen's analyses.
Journal of General Psychology, 123, 193−205.
Süß, H., Oberauer, K., Wittman, W., Wilhelm, O., & Schulze, R.
(2002). Working memory capacity explains reasoning ability—
And a little bit more. Intelligence, 30, 261−288.
Thurstone, L. (1938). Primary mental abilities. Psychometric Monographs, 1.
Unsworth, N., & Engle, R. W. (2007). The nature of individual
differences in working memory capacity: Active maintenance in
primary memory and controlled search from secondary memory.
Psychological Review, 114, 104−132.
Unsworth, N., & Engle, R. W. (2007). On the division of short-term
memory and working memory: An examination of simple and
complex span and their relation to higher order abilities. Psychological
Bulletin, 133, 1038−1066.
Wager, T. D., & Smith, E. E. (2003). Neuroimaging studies of working
memory: A meta-analysis. Cognitive, Affective, and Behavioral
Neuroscience, 3, 255−274.
Yela, M. (1969). Rotación de Figuras Macizas [Rotation of solid figures].
Madrid: TEA.

CS 251: Artificial Intelligence

こちらのポスター(PDFファイル)

February 1, 2015

Paper 2 - India Job Updates

安定性および流動性の指標 - Hitachi

B7 MCL-E-679F(R)

My first trials and what I learned from them

ARCHIVES - Population Studies Center

update on mesothelioma

Effects of attentional load on postural control - 名古屋大学

レジデント業積2014 - 国立病院機構東京医療センター

The 100 Episode Guide - INAF/IASF-Bo