Intern. J.

Neuroscience, 113:397–410, 2003

Copyright  2003 Taylor & Francis
0020-7454/03 $12.00 + .00
DOI: 10.1080/00207450390162164

EXECUTIVE FUNCTION
IN YOUNG COLOMBIAN ADULTS
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DAVID A. PINEDA
University of San Buenaventura
University of Antioquia
Medellin, Colombia

VILMA MERCHAN
University of San Buenaventura
Medellin, Colombia
For personal use only.

The aim of this article was to observe the correlation between executive
function (EF) variables, and to determine the factor structure of the EF
in young university students, as mathematical models for supporting its
multidimensional structure. Participants were both males and females,
aged 16 to 21 years (N = 100) and with normal Full Scale IQ selected
in a randomized and representative approach in private universities of
Medellín, Colombia. They were students of verbal, visual-spatial, and
mathematical careers. An executive function assessment battery was applied
and which included: Wisconsin Card Sorting Test (WCST), Trail Mak-
ing Test (TMT) A and B, verbal fluency test (FAS) by phonologic and
semantic cues, and Stroop’s conflict word/color test. The results were
as follows: An orthogonal structure of five factors, which explained
74.9% of the variance, was found. Factor 1 was formed by WCST vari-
ables (organization and flexibility), and explained 25.8% of the variance.
Errors from the Stroop reading and naming were assigned to factor 2,
which explained 17.3% of the variance. Factor 3 was the time for ex-
ecuting Stroop’s test, and explained 13.1% of the variance. Factor 4
was TMT A and B (10.1%). Factor 5 was verbal fluency (8.5% of the
variance). In conclusion, executive function in young university students
was conformed by five orthogonal cognitive dimensions.
Keywords cognitive organization, executive function, factor analysis, in-
hibitory control

Received 12 August 2002.

Address correspondence to David A. Pineda, Carrera 46# 2 Sur – 45, Consultorio 254,
Medellin, Colombia. E-mail: dpineda@epm.net.co

397
 398 D. A. Pineda and V. Merchan

The term “factor” was used extensively to explain the deficit that
can underlie an overt clinical disorder. Sometimes this term was
used for referring simply to the “basic deficit” or “underlying de-
fect” affecting normal psychological performance (Luria, 1966, 1970,
1973, 1976, 1984). These factors would, in consequence, represent
the basic elements of cognition, or the “basic cognitive abilities” or
“basic factors in cognition.” However, Luria discussed this question
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in some detail only with regard to language in his last book entitled
Fundamentals of Neurolinguistics (1976). However, it is not easy to
deduce the impaired factors in other neuropsychological syndromes
(e.g., frontal lobe syndromes). This “factor theory” of cognitive ac-
tivity represents one of the most interesting and outstanding points
in Luria’s neuropsychological perspective. Unfortunately, Luria did
not fully develop his “factor theory” of psychological activity. This
theoretical approach would assume that cognition is a material reality
formed by several links (factors) of multiple chains of cognitive ac-
tivities (functional systems), which was demonstrated by the syndromic
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analysis (Luria, 1966, 1973, 1976). Factor analysis is a mathemati-

cal procedure (Gorsuch, 1983; Pineda, Ardila, Rosselli, Cadavid,
Maneheno, & Mejía, 1998) that could objectively explain Luria’s
factor hypothesis, allowing replications of the findings.
Evidently, the core problem is how to determine those funda-
mental factors underlying normal cognition. A correlation procedure
between performance in different cognitive tasks seems to represent
a provocative possibility. For Benson (1994), any complex psycho-
logical activity requires the participation of different brain areas. As
an example, according to Benson and Geschwind (1970), six differ-
ent brain areas participate under normal conditions in reading aloud:
(1) primary visual cortex, (2) association visual cortex, (3) angular
gyrus, (4) temporal areas involved in language recognition, (5) frontal
language area (Broca’s area), and (6) primary motor cortex control-
ling language articulation. Benson points out that, depending on
the material used in reading, other additional brain areas could also
be involved in the reading process. This whole array of brain areas
would represent the brain system underlying the “reading aloud pro-
cess,” and supporting the “functional system for reading.” In the
case of damage to any of these written language processing levels,
a deficit in reading would appear, even though it would be different
 Executive Function in Adults 399

depending on the specific impaired area. Furthermore, other abili-

ties also relying on one of these processing levels (“factors” or
“links”) will be also affected.
Factor analysis has been used as a mathematical strategy to ana-
lyze the structural correlation of neuropsychological test batteries
(e.g., Ardila, Rosselli, & Bateman, 1994; Ardila, Galeano, & Rosselli,
1998; Ostrosky, Ardila, & Rosselli, 1999; Ponton, Satz, & Herrera,
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1994). Interpreting factorial analysis results, however, has been con-

troversial. Even when using the very same tests, factor structure
may be different (Bornstein, 1983; Bornstein & Chelune, 1988; Swier-
cinsky & Hallenbeck, 1975; Wagman, Heinrichs, & Carpenter, 1987).
Thus, setting a stable factor structure of cognitive processes has
been elusive. Furthermore, some specific neuropsychological tests,
such as the Wisconsin Card Sorting Test (Goldman et al., 1996) and
the Stroop’s Neurological Screening Test (Goldberg, Kelsoe, Weinberger,
Pliskin, Kirwin, & Berman, 1988) can result in rather specific fac-
tors. Evidently, factor solutions in such cases present a limited utility
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and alternative factor models should be searched.

The ideal situation in a factor analytic study is to find those
specific cognitive test variables holding a zero correlation with the
variables measuring other different cognitive factors, and only cor-
related with those variables measuring the very same cognitive links
(Dillon & Goldstein, 1984; Gorsuch, 1983; Kim & Mueller, 1978).
However, this is almost impossible, as every test variable is simul-
taneously measuring more than one domain, and saturated by more
than one factor. As a matter of fact, neuropsychological measures
were not developed departing from previous structural analyses, but
from intuitive clinical observations. A test directed to measure memory,
for instance, is also evaluating attention, language, executive func-
tion (EF), etc., depending on its conditions (Ardila, 1995). Differ-
ences in factor structure of neuropsychological tests depends upon
the characteristics of the instruments, the conditions used in testing,
and the selected statistical criteria (Ardila, 1995; Boone, Ponton,
Grosuch, González, & Miller, 1998; Greve, Brooks, Crouch, Will-
iam, & Rice, 1997; Gorsuch, 1983). Factor structure may be differ-
ent if normal or abnormal subjects are included (Kopelman, 1991;
Della Sala, Gray, Spinnler, & Trivelli, 1998); if several instruments
are used to measure just a single domain (Boone et al., 1998); and
 400 D. A. Pineda and V. Merchan

if normal adults (Ardila et al., 1998) or abnormal children (Chase-

Carmichael, Ris, Weber, & Scheffi, 1999; Fletcher, 1996; Pineda et
al., 1998; Pineda, Ardila, & Rosselli, 1999) are included.
EF is a riddle to be solved, as its tests were sometimes con-
structed rather intuitively. By selecting tasks, they appeared to re-
quire organization and planning, rather than being based on math-
ematical analysis of their components (Della Sala et al., 1998). During
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recent years, the definition of cognitive tests sensitive for evaluating

EF has been an emerging field in neuropsychology. Several tests,
such as the Wisconsin Card Sorting Test, Stroop’s Test, Verbal
Fluency (FAS), and Trail Making Test (TMT A and B) have been
validated for assessment of EF, by comparing prefrontal lobe dam-
aged patients to nonfrontal damaged participants (Chase-Carmichael
et al., 1999; Della Sala & Logie, 1993).
The objective of our research was to challenge the hypothesis
of a multidimensional model for normal EF. The purpose of this
article was to evaluate the factor structure of EF in a sample of
For personal use only.

young university students, using several sensitive tests, and to vali-

date the frontal lobe function.

PARTICIPANTS AND METHODS

Sample

One hundred healthy young university students from three private

universities were selected; both males and females were included,
aged 16–21 years, taking the first year of different types of careers.
Three groups of careers were chosen: mathematics, verbal, and visual-
spatial. A representative sample was selected using a randomized
procedure. The sample included students belonging to middle and
high socioeconomic strata of Medellin City (Colombia).
Medellin is a modern Northwestern city of Colombia, with a
population of 2,000,000 inhabitants, and has two public and nine
officially accredited private universities. The population of these
private universities attending the first semester was 3,600 students.
For an expected proportion of participants getting mean scores be-
tween 0.4 to 0.6, a confidence level of 95%, and an accepted error
 Executive Function in Adults 401

of 5%, the calculated representative sample should be 72 to 122

participants.
Each one of the nine private universities was assigned a number,
and three of them were selected by randomization, representing a
0.3 proportion. The list of students for the first semester was ob-
tained from these universities. A meeting was arranged with each
group in each career. A randomized sorting was carried out using
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the number of students in the list of each group from each career.
The representative distribution of the initial population by sex, socio-
economic strata (SES), and the proportion of students per career,
was restrictively taken into account. Finally, 100 students agreed to
participate and signed the informed consent approved by the Bioet-
hics Committee of the University of San Buenaventura. The charac-
teristics of the sample are summarized in Tables 1 and 2.

Instruments
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Wechsler Adult Intelligence Scale, Spanish Version, 7th Ed. (WAIS)

(Wechsler, 1988) was used as a measure of general cognitive activ-
ity. A shortened version was applied using four verbal (information,
similarities, vocabulary, and arithmetic) and four performance subtests

TABLE 1. Distribution of demographic characteristics of a sample

of 100 university students

Variables X (SD) N

Age 18.5 (1.5) 100

16–17 16.7 (0.3) 27
18–19 18.3 (0.5) 48
20–22 20.7 (0.8) 25

Sex 100
Male 41
Female 59

Socioeconomic stratum 100

Middle 67
High 33

Type of career 100

Visual spatial 24
Verbal 45
Mathematic 31
 402 D. A. Pineda and V. Merchan

TABLE 2. Intellectual characteristics of a representative and randomized sample of

100 students, from the first semester of different types of careers at private universities

Visual Verbal Mathematic

Variables (N = 24) (N = 45) (N = 31) F p

Age 18.8 18.6 18.1 1.59 0.2

(1.9) (1.2) (1.4)
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Verbal IQ 112.3 109.5 111.5 0.96 0.3

(10.4) (9.7) (5.2)

Performance IQ 109.6 106.7 110.4 2.15 0.4

(7.6) (8.9) (8.3)

FSIQ 112.6 110.6 113.9 1.6 0.2

(8.2) (9.4) (5.5)

(picture completion, picture arrangement, block design, and digit

symbol). Verbal, performance, and full-scale intellectual quotients
(VIQ, PIQ, and FSIQ) were calculated following the manual’s in-
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structions.
Wisconsin Card Sorting Test (WCST) (Heaton, 1981) standard
version with 4 key and 128 trial cards was used. The number of
achieved categories, correct responses, errors, perseverative errors,
non-perseverative errors, and failure for maintaining set were scored.
This test has norms for the Colombian population (Pineda et al.,
1999; Pineda, Merchán, Rosselli, & Ardila, 2000; Rosselli & Ardila,
1993).
Trail Making Test A & B (TMT A & B) (Reitan & Wolfson,
1994) format for adults was used with 25 numbers in the first trial,
and 13 numbers and 12 letters in the second trial. Time and number
of errors were scored. It has been used before in the Colombian
population (Pineda et al., 2000).
Verbal Fluency (FAS) was measured by phonologic (/f/, /a/, /s/),
and semantic (animals and fruits) guided word production in 1 min
was measured. This test has normative data in Colombian children
and adults (Ardila, Rosselli, & Puente 1994; Pineda et al., 1999).
Stroop’s conflict word/color test (Spreen & Strauss, 1991) was
used for evaluating the capability for controlling the habitual re-
sponses and shifting in favor of an unusual one; the shortened version
of color-word interference test was used with three 21.5 × 14 cm
 Executive Function in Adults 403

cards, each containing ten rows of five items, written in arial 28

points (50 items). The names of four colors (green, red, blue, and
yellow) were written several times in a randomized order in the first
card with black ink. In the second card, Xs were drawn with green,
red, blue, and yellow ink. In the third card, the words green, red,
blue, and yellow were drawn with different color inks (the word
green was sometimes drawn with red, other times with yellow, or
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with blue ink). The task was divided in three parts: A) reading
(participants read the words written with black ink), B) naming (par-
ticipants named the color of Xs), and C) conflict (participants named
the color of the ink without reading the word). Times and errors for
each part were scored.

Procedure
After the randomized sorting, each student was contacted by phone,
an appointment was arranged, and a brief interview was carried out
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in order to explain the different steps of the assessment and to dis-

card major psychiatric and neurological problems. An informed consent
was signed by each participant and the neuropsychological battery
was applied in one session of 90 to 120 min, by trained neuropsy-
chologists.

Statistic Analysis
Pearson correlation analysis for executive function variables was
developed. Exploratory factor analysis with 13 fairly normalized
variables, using a Varimax orthogonal rotated matrix, for hypotheti-
cally nonrelated dimensions, was performed. SPSS 8.0 software was
used.

RESULTS

High correlation coefficients (r > .6) were observed only between

variables belonging to the same test, mainly between variables from
WCST. Correlations between variables of different tests were low,
meaning that each test measured different dimensions of the executive
 404 D. A. Pineda and V. Merchan

system, and allowed us to anticipate the hypothesis that these dimen-

sions were orthogonal sets. TMT A & B had significantly good
correlation (r = .66). Similarly, Stroop’s test times had low to modest
correlations (r between .17 and .56), and Stroop’s test reading and
naming errors showed strong correlation (r = .76), but only modest
correlation with Stroop’s test conflict (r = .47 and .51, respectively).
Phonologic FAS had significantly low correlation (r = .23) with
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Semantic FAS, and Semantic FAS had significantly low correlation

with Stroop’s test B (r = .23) and C (r = .20) errors (see Table 3).
Factor analysis with the 13 normalized variables found five fac-
tors with eigenvalues over 1. This factor structure explained 74.9%
of the variance. Factor 1 was formed by four WCST variables. This
factor could correspond to cognitive activities of organization and
flexibility, and explained 25.8% of the variance. Errors from the
Stroop’s test reading and naming were assigned to factor 2, which
explained 17.3% of the variance. This factor could be considered as
sustained attention. Factor 3 was the time for executing Stroop’s
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test variables. This factor could represent speed for inhibitory con-
trol, and explained 13.1% of the variance. Factor 4 was the time for
executing TMT A & B, which should be considered as visual-motor
speed, which explained 10.1% of the variance. Factor 5 was one of
verbal fluency and explained 8.5% of the variance (see Table 4).

DISCUSSION

A structure of five orthogonal factors in normal young, healthy, and

high cognitive function adults is reported. The 13 selected variables
for the factor analysis were rigorously normalized in order to keep
mathematical parametric conditions. The factor structure of the ex-
ecutive function appears very stable because of the high coefficient
of communality of all variables included in the model (.55 to .87),
and because of the high, nonshared, loading coefficient of the vari-
ables belonging to each factor (.69 to .95). This structure explained
a very high percentage (74.9%) of the underlying variance to the
model, demonstrating that all variables included in the analysis be-
long to this model, and the five factors conform sets of clearly
separate dimensions.
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TABLE 3. Executive function tests (Pearson’s correlation analysis)

WCST WCST WCST WCST TMT TMT Stroop Stroop Stroop Stroop Stroop Stroop Fas FAS
errors pers err N-P err FMS A B A time A errors B time B errors C time C errors phon sem

WCST category –0.86** –0.74** –0.71** –0.28* –0.36 –0.15 –0.06 0.02 –0.14 0.11 –0.13 –0.11 0.02 0.06
WCST errors 0.86** 0.81** 0.24* 0.01 0.1 0.12 –0.07 0.14 –0.14 0.17 0.08 0.09 –0.02
WCST pers err 0.41* 0.17 0.07 0.1 0.11 –0.03 0.17 –0.14 0.1 0.01 0.14 –0.09
WCST N-P err 0.27* –0.06 0.05 0.08 –0.09 0.07 –0.1 0.19 0.13 0.00 –0.11
WCST FMS –0.11 0.02 –0.02 0.03 0.03 0.01 –0.03 0.04 –0.05 –0.03
TMT A 0.66** 0.16 0.14 0.12 0.04 0.19 0.09 –0.06 –0.13

405
TMT B 0.08 0.15 0.13 0.05 0.32* 0.16 –0.07 –0.06
Stroop A time 0.13 0.34* 0.13 0.17 0.18 –0.06 0.07
Stroop A errors –0.03 0.76** –0.06 0.47** –0.05 0.04
Stroop B time 0.00 0.56** 0.09 –0.14 –0.23*
Stroop B errors –0.04 0.51** 0.00 0.06
Stroop C time 0.32* –0.07 –0.20*
Stroop C errors –0.06 0.00
FAS—phonolog 0.23*

*p = .01 to .05.
**p < .001.
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406

TABLE 4. Factor analysis of executive function tests (Varimax orthogonal)

Factor I Factor 2 Factor 3 Factor 4 Factor 5

Eigenvalue Eigenvalue Eigenvalue Eigenvalue Eigenvalue
3.4 2.2 1.7 1.3 1.1
% Variance % Variance % Variance % Variance % Variance
Variables Communalities 25.8 17.3 13.1 10.1 8.5

WCST: errors 0.91 0.95

WCST: categories 0.83 –0.91
WCST: N-P errors 0.71 0.84
WCST: pers errors 0.70 0.81

406
Stroop A error 0.87 0.93
Stroop B error 0.86 0.92
Stroop B time 0.78 0.85
Stroop C time 0.63 0.72
Stroop A time 0.55 0.25 0.69
TMT A 0.82 0.90
D. A. Pineda and V. Merchan

TMT B 0.83 0.90

Phonologic FAS 0.63 0.78
Semantic FAS 0.60 0.75
 Executive Function in Adults 407

Our findings show that executive function tests assessed different

cognitive dimensions, and formed different nonrelated sets of vari-
ables. These data are similar to those reported by several previous
studies (Boone et al., 1998; Boone, 1999; Della Sala, Gray, Spinnler,
& Trivelli, 1998; Greve, Ingram, & Bianchini, 1998; Kopelman,
1991; Pineda, Ardila, Rosselli, Cadavid, Mancheno, & Mejía, 1998).
All these studies have sustained the theoretical models of a multiple
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dimension structure for the EF, as was primarily proposed by Luria

for language (1966, 1970, 1973, l976), and later by Stuss & Benson
(1986) for EF.
The multiple dimension model assumes that EF is really not a
single activity, but rather a complex system, which has different
cognitive operations for anticipation, goal selection, organization,
planning, monitoring, shifting, controlling time, and speed, and using
environmental feedback for metacognitive acceptance or rejection
of the behavior (Della Sala et al., 1998; Lezak, 1995; Stuss & Benson,
1986).
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“Syndromic analyses” from patients with frontal lobe damage

have supported this model. The earliest report was the Phineas Gage
case recently discussed, using new information based on mod-
ern neuroimaging techniques for reconstructing the possible lesions
(Damasio, Groboski, Frank, Galaburda, & Damasio, 1994). After
this case, many similar subsequent ones have been described (Eslinger
& Damasio, 1985). Patients may appear to function as much as
before brain damage, with little or no obvious mental deterioration,
including preservation of a high level of intelligence, but with se-
vere behavioral problems, and important difficulties for solving com-
plex problems. Diverse types of dissociations have been described,
as evidence for multiple factor structure of the “executive system”
(Boone, 1999; Della Sala et al., 1998; Eslinger & Damasio, 1985;
Lezak, 1995; Stuss & Benson 1986).
Correlation analyses revealed that only variables derived from
WCST have high correlation coefficients. Correlations between vari-
ables of other tests were only low or modest, in spite of belonging
to the same task, confirming the hypothesis that test variables are
simultaneously measuring more than one domain, and that neuro-
psychological measures were not developed by departing from pre-
vious structural analyses, but from intuitive clinical observations. A
 408 D. A. Pineda and V. Merchan

test constructed for assessing memory, for instance, is also evaluat-

ing attention, language, executive function (EF), etc., depending on
the conditions of its administration (Ardila, 1995). Phonological and
semantic FAS had significantly low correlation, demonstrating that
these two variables are assessing different links inside verbal flu-
ency. Phonological cues for searching a word, with restrictive rules,
may require different mental operations than those demanded for
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semantic categorical production.

It is possible that the structure of five factors for EF appears
different from those reported in other studies. However, as authors
of these investigations have affirmed, differences in factor structure
of neuropsychological tests depend on the characteristics of the in-
struments, the conditions used in testing, and the selected statistical
criteria (Ardila, 1995; Boone, Ponton, Grosuch, González, & Miller,
1998; Gorsuch, 1983). Factor structure may be different if normal
or abnormal subjects are included (Kopelman, 1991; Della Sala,
Gray, Spinnler, & Trivelli, 1998), or if several instruments are used
For personal use only.

to measure just a single domain (Boone et al., 1998). In this study,

only young university students were included, and the results apply
only to this selected population. It is postulated that if the same
tests were applied to different populations, different EF structures
would be found.

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