Research shows abnormal function of the pre-frontal cortex in Attention Deficit Hyperactivity Disorder
(ADHD). This cortex is involved in the control of executive functions related to planning and execution
of goal-oriented strategies, working memory, inhibitions, cognitive flexibility, and selective attention.
Selective attention involves focus on the target stimulus, ignoring competing distractions. The Stroop
Test (Stroop, 1935) is usually used to evaluate selective attention. This study investigated whether
children with ADHD could exhibit modified performance in the Stroop Test. Using a computerized
version of this test (Capovilla, Montiel, Macedo, & Charin, 2005), the study compared the reaction
times (RTs) of 62 Brazilian children, between 8 and 12 years of age, 31 of whom were diagnosed
with ADHD and sent to psychiatric clinics, and 31 without ADHD studying in regular schools. All
children with ADHD satisfied the criteria of the DSM-IV-TR and were evaluated with the Conners
Abbreviated Questionnaire (Goyette, Conners, & Ulrich, 1978), completed by parents and teachers.
The results revealed that children with ADHD exhibit greater interference in RT than children without
ADHD. This corroborated the hypothesis that children with ADHD exhibit a deficit in selective
attention, consisting in augmented RTs, as measured by the Computerized Stroop Test.
Keywords: Stroop Test, ADHD, neuropsychology, selective attention, executive functions
La investigación ha mostrado la función anormal del córtex prefrontal en el trastorno de déficit de
atención con hiperactividad (TDAH). Este córtex está implicado en el control de las funciones
ejecutivas relacionadas con la planificación y ejecución de estrategias orientadas a objetivos, la
memoria de trabajo, las inhibiciones, la flexibilidad cognitiva y la atención selectiva. La atención
selectiva implica centrarse en el estímulo diana, ignorando las distracciones que compiten para la
atención. Normalmente se emplea la prueba Stroop (Stroop, 1935) para evaluar la atención selectiva.
En este estudio se investigó si le ejecución en la prueba Stroop de niños con TDAH podría sufrir
modificaciones. Empleando una versión informatizada de esta prueba (Capovilla, Montiel, Macedo
y Charin, 2005), el estudio comparó los tiempos de reacción (TR) de 62 niños brasileños, con
edades entre los 8 y los 12 años, de los cuales 31 fueron diagnosticados de TDAH y estaban en
clínicas psiquiátricas, y 31 sin TDAH que estudiaban en colegios normales. Todos los niños con
TDAH cumplían los criterios del DSM-IV-TR y fueron evaluados con el Cuestionario Abreviado de
Conners (Goyette, Conners y Ulrich, 1978), cumplimentado por padres y profesores. Los resultados
revelaron que los niños con TDAH mostraron más interferencia en TR que los niños sin TDAH.
Esto corroboró la hipótesis de que los niños con TDAH exhiben un déficit en la atención selectiva,
que consiste en TR aumentados, tal y como se miden con la Prueba Stroop Informatizada.
Palabras clave: Prueba Stroop, TDAH, neuropsicología, atención selectiva, funciones ejecutivas
Computerized Stroop Test to Assess Selective Attention
in Children with Attention Deficit Hyperactivity Disorder
Ellen Carolina dos Santos Assef1, Alessandra Gotuzo Seabra Capovilla1,
and Fernando Cesar Capovilla2
1Universidade São Francisco, Brazil
2Universidade de São Paulo, Brazil
The Spanish Journal of Psychology
2007, Vol. 10, No. 1, 33-40
Copyright 2007 by The Spanish Journal of Psychology
Correspondence concerning this article should be sent to Alessandra Gotuzo Seabra Capovilla, Post-Graduate Program Strict Sense
in Psychology of the University of Sao Francisco, Av. Alexandre Rodrigues Barbosa, 45 - Centro - Itatiba – SP, CEP 13251-900 (Brazil).
Phone: 55 11 4534-8040. Fax: 55 11 4524-1933.
E-mail: email@example.com, firstname.lastname@example.org
The diagnosis of attention deficit hyperactivity disorder
is fundamentally clinical, based on criteria from classificatory
systems such as the International Classification of Diseases
(ICD-10; World Health Organization, 1992) and the
Diagnostic and Statistical Manuel of Mental Disorders, 4th
edition, text revision (American Psychiatric Association,
2000). The predominant behavioral character of the
symptomatology of the disorder, in the majority of cases
identified in infantile populations, renders discrimination
between the illness and maladjusted behavior from
environmental factors difficult. Allied to such behavioral
alterations, there are cognitive alterations that must be studied
and understood better.
Since the first definitions of the disorder, it has been
speculated to be based on the neurons involved. Currently,
the consensus that there is a cerebral dysfunction has driven
research to incorporate other areas to understand the
phenomenon. Neuropsychology has been one of the areas
emphasized in the investigation of the relationship between
behavior, cognition, and the central nervous system
(Gazzaniga, Ivry, & Mangun, 2002; Gil, 1999; Luria, 1973).
The combination of sophisticated techniques of neuroimaging
and neuropsychological evaluations has corroborated the
hypothesis of the presence of a cerebral malfunction in
children with ADHD, and improved understanding of the
cognitive mechanisms involved.
Phenomena characteristic of ADHD, such as difficulty
in maintaining attention, control of behavior and distracting
thoughts, and control of motor function (Barkley, 1997;
Houghton et al., 1999; Mattos, 2002), have been associated
with frontal encephalic alterations, but especially pre-frontal
ones. These cortical regions are principally responsible for
coordination, integration, maintenance, and monitoring of
further cognitive functions related to other cerebral areas.
Called “executive functions,” this group of frontal abilities
has been understood basically as consisting of the ability to
form action strategies to achieve goals and objectives
(Damásio, 1995; Gazzaniga, et al., 2002; Goldberg, 2002;
Kerns & Berembaum, 2003; Lezak, 1995). Such functions
are not united, but involve components such as the ability
to select information, planning, attention monitoring,
organization of memorization strategies, discrimination of
memories, inhibition of interference during a memory, and
cognitive flexibility (Gazzaniga et al., 2002; Lezak, 1995).
In fact, according to various authors, malfunctions in
the pre-frontal cortex and their connections to the subcortical
network may be responsible for behaviors typical of ADHD,
such as deficits in behavioral inhibitions, working memory,
planning, attention, self-control, and directing action toward
goals (Barkley, 1997; Barnett et al., 2001; Carter, Krener,
Chaderjian, Nortycutt, & Wolfe, 1995; Knapp, Rohde,
Lyszkowski, & Johannpeter, 2002; MacPherson, Phillips, &
Sala, 2002; Silberstein et al., 1998).
The prevalence of ADHD is near 3% to 8% of the school
population (Andrade & Scheuer, 2004; Bush et al., 1999;
Freire & Pondé, 2005; Mattos, Saboya, Kaefer, Knijnik, &
Soncini, 2003; Szobot, Eizirik, Cunha, Langleben, & Rohde,
2001). Given this elevated prevalence, it is fundamental to
precisely identify the children who exhibit the disorder,
based on the understanding of the related difficulties, so as
to promote effective intervention. Given that, according to
the reviewed literature, the executive functions have been
shown to be compromised in individuals with ADHD
(Barkley, 1997), it is increasingly important to develop and
validate tests of executive functions, verifying whether such
instruments discriminate between individuals with or without
To develop evaluation instruments for executive
functions, it is necessary to first understand what the
components of these functions are. Some authors (e.g.,
Gazzaniga et al., 2002; Lezak, 1995) have proposed that a
specific group of abilities is involved in the executive
functions, including aspects of working memory, planning,
flexibility, selective attentions and inhibitive control. Within
such components, the present article focuses especially on
selective attention, which corresponds to one of the functions
of the attention system, and presupposes, concomitantly,
orientation and concentration directed towards a stimulus,
ignoring or decreasing the emphasis on other concurrent
stimuli (Sternberg, 1996). According to Rosin (2001), this
system allows response to a relevant target stimulus and
suppression of irrelevant, distracting stimuli, as the response
time to a target is usually greater when the target appears
accompanied by distractions than when not accompanied
Various instruments have been used to analyze attention
processes, such as the Gottschaldt Shuffled Figures Test,
the Odd Man Out Test, and Stroop’s word-color procedure,
which has been related more specifically to selective
attention (Gazzaniga et al., 2002; Gil, 1999; Sternberg, 1996).
The original version of the Stroop Test (Stroop, 1935)
consisted of four parts. In the first part, the subjects had to
read the names of colors written in black ink. In the second
part, they read the names of colors written in colored ink,
with no correlation between the name written and the color
of the ink. In the third part, they had to say the name of the
color of squares. Finally, in the fourth part, the same stimuli
were presented as in the second part, but the subjects had
to say the color of the ink with which the words had been
written, disregarding the actual words.
The Victoria version (Regard, 1981/1999) has three steps.
In the first, written words are presented (names of colors)
written in black ink; in the second stage, colored circles
(red, blue, yellow, green); and, in the last step, written words
(names of colors) printed in colored ink, without any
correlation between the color of the ink and the written
word. In the first step, the subject must read the words as
quickly as possible. In the second and third steps, the subject
must say the color of the circles and printed words,
ASSEF, CAPOVILLA, AND CAPOVILLA
Sensitivity to interference becomes clear in the last step
of the test, as this task demands the selection of relevant
information (in this case, attention to the color of the ink and
suppression of the verbal content). In the general population,
reaction time (RT) in this part of the task tends to increase
in relation to the preceding steps, reflecting the so-called
color-word interference effect. Patients with lesions on the
frontal lobe require even more RT in the third step and can
exhibit decreased performance, with more errors (Gil, 1999).
The study of Bush et al. (1999) analyzed performance in
the Stroop task under functional magnetic resonance in adults
with and without ADHD. The results revealed that both groups
exhibited an interference effect in the task. However, patients
with ADHD showed greater activation of the frontal-striate
circuits, indicating hyperactivity in the anterior cingulate
cortex, in contrast to normal adults. In fact, in a revision,
Bush, Luu, and Posner (2000) point that various studies
corroborate the idea that the anterior cingulate cortex is part
of the network involved in attention, being important as much
for cognitive regulation processes as for emotional ones.
According to Bush et al. (2000), the anterior cingulate
cortex does not have a unitary structure or function; it is one
of the principal divisions and, in cognitive aspects, it is related
to the dorsal region, and, in affective aspects, to the rostro-
ventral region. Thus, the dorsal region tends to be active in
cognitive tasks, such as the Stroop color-word Test, and
inhibited in emotional tasks, such as the Stroop emotional
Test, whereas the opposite pattern tends to occur with the
rostro-ventral region. Such a division is important, as alterations
in each of these regions can cause specific attention problems
or problems related either to neutral information or to emotional
information. This may explain possible discrepancies observed
in daily situations and formal tests, as formal tests usually
employ neutral stimuli, whereas daily situations can require
attention to information with an emotional content.
Sergeant, Geurts, and Oosterlaan (2002) conducted a
review of research related to evaluations of executive functions
in ADHD, among other pathologies. Among the studies, the
majority employed children and adolescents with ADHD as
subjects. A large part of these studies identified significant
differences between groups with and without ADHD in the
Stroop Test, although some did not reveal significant effects,
or even revealed superior performance for the group with the
disorder. Such discrepancies may be due to different measures
used in the studies, because the score, total locution time,
and/or RT in each item were all used.
In fact, studies with the Stroop Test have shown that,
most of the time, the evaluation in terms of scores does not
statistically discriminate between groups with and without
ADHD (Scheres et al., 2004). The same thing was observed,
for example, in the research of Willcutt et al. (2000), in
which children with ADHD, from 8 to 16 years old,
exhibited slightly lower scores in the Stroop Test in
comparison with children without the disorder, and slightly
higher scores when contrasted with a group with a reading
disorder. The group formed of children with ADHD and
reading disorder exhibited even lower performance. However,
such differences were not statistically significant. Such
findings reinforce the need to evaluate other measurements,
such as RT, in studies with the Stroop Test.
In this context, RT, or the interval between the perception
of a stimulus and the emission of a response, can be used
as a performance measure. This is because it allows
investigation of subtle deficits in the evaluated cognitive
process, even when the subject commits no errors by
omission or errors in action (submission of a response when
a distraction arises or in the absence of any stimulus). In
fact, this measurement of processing speed, especially in
attention tasks, tends to be elevated in children with ADHD
in comparison to control groups (Araújo, 2004).
In the face of the lack of attention evaluation instruments
for children in Brazil, and additional evidence of selective
attention in ADHD, the present study investigated the
existence of possible differences between children with and
without ADHD in terms of score and RT in a computerized
version of the Stroop Test.
Sixty-two children participated, between 8 and 12 years
old, divided into two groups. Group 1 consisted of 31
children diagnosed with Attention Deficit Hyperactivity
disorder (ADHD). Of these, 28 were boys and 3 girls, the
average age being 124.4 months. In terms of the type of
school attended, 18 were students from public schools, and
13 came from private institutions, namely, from two services
of infantile psychiatry in the State of Sao Paulo. The
selection of this group was done conforming to the non-
probabilistic intentional method (Bisquerra, Sarriera, &
Martínez, 2004), according to previously established
inclusion and exclusion criteria.
The diagnosis of ADHD was done psychiatrically, by
means of semi-structured interviews with one of the parents
or legal guardians and teachers. A questionnaire of symptoms
proposed by the DSM-IV-TR (APA, 2000) was used in the
anamnesis with the parents, made up of 18 items
corresponding to the nine items for inattentiveness and nine
for hyperactivity/impulsivity, in addition to the criteria related
to the presence of these symptoms before 7 years of age,
in a minimum of two distinct contexts, with significant
social, academic, or occupational behavior. In addition, two
versions of the Abbreviated Conners Questionnaire (Goyette,
Conners, & Ulrich, 1978) were used, for parents and
teachers, translated and adapted by Barbosa and Gouveia
(1993) for Brazilian children. This questionnaire, which
determines the quantity of the child’s hyperactive behavior,
consists of 42 and 40 items, respectively, to which the child’s
COMPUTERIZED STROOP TEST IN ADHD EVALUATION
parents and teachers respond on a 4-point Likert-type rating
scale, ranging from 0 (never) to 3 (always). Thus, all of the
children diagnosed with ADHD met the DSM-IV-TR (APA,
2000) criteria, as well as obtaining scores above the
minimum in the Abbreviated Conners Questionnaire.
For inclusion in Group 1, in addition to the psychiatric
diagnosis, three inclusion criteria were employed. The first
specified that the children must be attending a regular school.
The second specified that their chronological age must be
between 8 complete years and 11.5 years. The third specified
that they could not be using any medication that could
interfere with their cognitive or emotional behavior. After
consulting neuropediatricians, it was decided that children
using medication with methylphenidate should interrupt the
medication for a minimum period of two days before
undergoing the neuropsychological evaluation.
Four exclusion criteria were used; if any of them were
met, the child was excluded from the study. Any child with
a concomitant psychiatric diagnosis was excluded, including
any developmental disorder, mental retardation, mood and
anxiety disorders. This criterion was determined according
to the criteria of the DSM-IV-TR (APA, 2000), based on
semi-structured interviews with parents and teachers.
Children with delayed neuropsychomotor development,
sensory, motor, or neurological deficits, as well as children
with intellectual deficiencies were excluded. Finally, children
who attended special classes were excluded. However, the
type of school attended was not an exclusion criterion;
therefore, both children attending private and public schools
Group 2 was made up of a selection of 31 children from
a database provided by Cozza (2005). This database included
the data of children in the 3rdand 4thgrades, all in the public
school system in the State of Sao Paulo. Of these children,
only those with a percentage lower than 75 in the Attention
and Hyperactivity Deficit Scale (Benczik, 2000) were chosen,
that is, those who did not exhibit symptoms of inattentiveness
or hyperactivity. Such children were paired in age and sex
with those of Group 1, resulting in a group with 28 boys
and 3 girls with an average age of 123.9 months of age.
Group 2 was characterized as not having a known history
of ADHD or abnormal neuropsychomotor development, or
intellectual deficiency (percentage below 50 in the Progressive
Matrices Test—Raven, 1938; cf. Angelini, Alves, Custódio,
Duarte, & Duarte, 1999) or other pathologies that could alter
cognitive behavior. The remainder of inclusion and exclusion
criteria was similar to those for the group with ADHD, so
that the groups would be comparable.
Computerized Stroop Test. This version, developed by
Capovilla, Montiel, Macedo and Charin (2005), evaluates
selective attention, or capacity to attend to specific
characteristics of a stimulus, ignoring characteristics
irrelevant to the task. It was based on the Victoria version
(Regard, 1981) and consists of three parts.
The first part consists of computerized presentation of
the names of four colors (yellow, blue, green, and red),
written in capital letters, Times New Roman font, size 72,
in black. Each word appears six times and remains visible
on the screen for an undetermined period of time. The order
is semi-random, so that the same word never appears two
consecutive times throughout the test. The subject’s task is
to read each word as quickly as possible. This part of the
test is intended to obtain a baseline to evaluate the reading
ability and determine whether this ability is high enough so
as not to hinder the interference effect. This is because the
effect of color-word interference may be absent if the reading
ability is lower than expected.
In the second part, 24 colored circles are presented, 6
circles for each of the four colors (blue, red, yellow, and
green), distributed semi-randomly. Each circle remains on
the screen for 0.040 seconds. This time limitation of the
presentation was adopted because research has shown that
rapid presentation, with presentation under 50ms, renders
conscious access to the presented content difficult and
amplifies the color-word interference effect (MacLeod &
Rutherford, 1992; Mogg, Kentish, & Bradley, 1993). The
task is similar to the first part, having to name the color of
the circle, to provide a baseline for the analysis of RT in
the third part.
In the third and last part, the circles are replaced by
written words, corresponding to the four colors, however
the words are printed in colors that do not correspond to
the written word (for example, the word “green” written in
blue letters). The subject must name the color in which the
word is written, ignoring the meaning of the written word.
For the computerized version, IBV software, developed
by Macedo, Capovilla, Diana, and Covre (1998), was used,
that allows recording of the participant’s voice at each
stimulus, as well as their RT. The experimenter operates the
software, selecting the buttons “Stop,” “Next,” or “Pause”
that appear in the lower right corner of the computer screen.
This adaptation allows auditory reexamination of the
subject’s actions, enabling later qualitative and qualitative
The study was initially approved by the Ethics in
Research Committee of the University of Sao Francisco and
authorized by infantile research services. Once the children
and their guardians gave consent, data collection began. The
Stroop Test was administered individually, taking about 20
minutes, using a laptop computer.
At the beginning of the test, the participant is told that
the instrument is made up of three parts, the first one
consisting of the reading of words, the second and third
ones consisting of naming the colors as quickly as possible.
ASSEF, CAPOVILLA, AND CAPOVILLA
COMPUTERIZED STROOP TEST IN ADHD EVALUATION
The software records participant’s score, RT, and
performance time. Measurements of color-word interference
score and color-word interference RT were used in this study.
For scoring purposes, 0 point was assigned for no
response or an error, and 1 point for each correct response.
The mean score was the mathematical average of the points
obtained in each part of the test (maximum = 1 point,
minimum = 0). Thus, the test generates three scores (mean
word reading, naming of colored circles, and naming of
colors of printed words) and a fourth score, the mean color-
word interference effect, which corresponds to the score in
Part 3 minus the score in Part 2 (Regard, 1981/1999).
For the purpose of analyzing RT, four measurements
were taken: (a) mean RT in word reading, (b) mean RT in
naming of the colored circles, (c) mean RT in naming of
the colors of the printed words, and (d) mean RT in color-
word interference, which corresponds to the mean RT in
Part 3 minus the mean RT in Part 2.
In this study, only the mean color-word interference score
and the mean color-word interference RT were considered,
as these reflect most appropriately the mean color-word
interference effect, that is, the alteration in color-word
interference performance (i.e., score or RT) due to the
incongruence in Part 3 between the color and the printed
word. Only the performance of participants who achieved
a score of 80% or better in Part 1, reading, was considered,
as this variable is of extreme relevance for performance of
In the performance analysis on the Stroop Test, 2
participants out of the 62 did not obtain the minimum
necessary score on Part 1 to allow analysis of performance
on the test. Therefore, the data from these two children, as
well as the data from their respective pairs, were excluded
from the analysis.
The precision of the Stroop Test to measure RT and
interference score was calculated considering the raw data
on all of the 72 items. For RT, a Cronbach’s alpha of .68
was obtained and a Spearman-Brown coefficient of .83, both
being satisfactory. For the interference score, a Cronbach’s
alpha of .82 and a Spearman-Brown coefficient of .84 were
obtained, both sufficiently satisfactory.
Two measurements of interference were used to
compare groups with and without ADHD: mean score in
Part 3 minus mean score in Part 2, and mean RT in Part
3 minus mean RT in Part 2. Initially, outliers, whose
performance was beyond two standard deviations of the
average of the group, were excluded, as well as their
respective pairs. This was the case of 2 outliers with ADHD
in interference RT, and 3 outliers (1 with ADHD and 2
without) in interference score. Therefore, all told, 4
participants were excluded from RT analysis and 6 from
interference score analysis.
Table 1 summarizes the descriptive statistics obtained
for RT and interference score (i.e., the average in Part 3
minus the average in Part 2) in each group. For RT, the
greater the difference between the Part 3 and Part 2, the
greater the interference effect on directing attention to the
relevant stimulus. For the interference score, the more
negative the score resulting from the difference between
Part 3 and Part 2, the greater the interference effect, that is,
the lower the capacity to respond correctly to a target
stimulus (in this case, the color in which the word was
printed) in the presence of a distracting stimulus (i.e., the
written word that names a color). In the present study, a
greater RT with interference was observed in the group with
ADHD than in the group without the disorder, but a lower
interference score with distraction was not found.
In order to determine whether ANCOVA was adequate,
fulfillment of the normality criteria of the distributions was
verified, finding that the interference RT distribution satisfied
normality requirements (p = .179 and p = .200, for the group
with and without ADHD, respectively), but that the
Interferential and Descriptive Statistics for Interference RT (in Thousandths of a Second) and Descriptive Statistics for
Interference Scores in the Stroop Test for Groups with and without ADHD
the Stroop Test
Confidence interval of 95%
Groups M SD
Lower limit Upper limit
ADHD 0.785 0.053.678.892
Interference RT(1, 54) = 8.50.005
Without ADHD0.5370.053.430 .644
ADHD –0.042 0.026–.094.010
Without ADHD–0.067 0.026 –.119–.015
Note. For interference RT, higher values imply a greater interference effect. For the interference score, lower values (i.e., the more
negative this value) imply a greater interference effect.
ASSEF, CAPOVILLA, AND CAPOVILLA
interference score did not (p = .200 and p = .005 for the
group with ADHD and without ADHD, respectively).
Therefore, a univariate ANCOVA was conducted, with
interference RT as the dependent variable, the group (with
and without ADHD) as factor, and age and type of school
as covariates. Age was used as covariate because the group
with ADHD was, on average, older than the group without
ADHD (124.4 months compared to 123.6 months). The
school type was used as covariate because, in Brazil, there
is a significant difference in the quality of teaching between
private and public schools, as performance in private schools
is usually better. Thus, to avoid possible differences between
the groups due to age or type of school, these two variables
were controlled with ANCOVA. As the type of school
variable was categorical, public and private levels were
dummy coded as 0 and 1, respectively. The Levene test
revealed homogeneity of variances (p = .135), showing that
ANCOVA was suitable.
The ANCOVA revealed a significant effect for the type
of group on interference RT, F (1, 54) = 8.50, p = .005, n2
= 0.145. No significant effects were found for the covariates
of age (p = .937) or type of school (p = .231).
This study investigated the difference between groups
of children with and without ADHD in terms of interference
score and interference RT in the Stroop Test. As this
computerized version has only recently been developed,
precision analyses were initially carried for both
measurements, which provided sufficiently satisfactory
As the measurement of Stroop interference score did not
meet normality criteria, inferential analyses were conducted
on the RT data only. Upon comparing children with and
without ADHD with ANCOVA, it was observed that the
children with ADHD showed greater interference effect on
RT in the emission of a response to the stimulus than the
children without ADHD. This statistically significant
difference provides concurrent evidence to validate the
Computerized Stroop Test, even after having controlled the
effects of age and type of school. The covariates of age and
school type did not yield significant effects.
These results corroborate the interference effect on RT in
the Stroop Test as a valid measurement to discriminate between
groups with and without ADHD. That is, when orientation
and selection of a relevant stimulus are required while ignoring
other competing stimuli, children with ADHD display greater
RT in the task. In the author’s studies (Stroop, 1935), time
measurement was already used to discriminate the interference
effect, which was replicated in other studies (e.g., Gazzaniga
et al., 2002, Rosin, 2001; Sergeant et al., 2002).
Computerized application of the Stroop Test provides a
great advantage to the analysis of such time measurements,
as it allows precise recording of RT in thousandths of a
second, increasing its sensitivity. Furthermore, computerization
improves standardization of the conditions for presenting the
stimuli and collecting responses, allowing greater rigor in the
control of the conditions of the evaluation, making the test a
more trustworthy resource for neuropsychological evaluation.
The results of this study should be investigated in greater
detail in future studies in order to increase comprehension
of the results of children with ADHD in the computerized
version of the Stroop Test. An aspect to be observed is that
the groups were not paired based on intelligence, because
the scores in the Raven Progressive Matrices Test
(1938/1999) were only used as a cutting point for the
exclusion criteria. Thus, it is important that future studies
pair subjects also in intelligence.
Other factors should also be investigated in greater detail,
because, according to the literature (Houghton et al., 1999;
Sergeant et al., 2002), many external variables may interfere
in the evaluation of results from ADHD, such as the use of
medication (especially methylphenidate), type of school,
age, sex, and comorbidity, among others. It is important to
study, as well, the intrinsic characteristics of each version
of the Stroop Test. In the current version, for example,
presentation is computerized, so each stimulus is presented
isolated on the screen, and the next stimulus is only
presented after the participant has responded to the previous
stimulus. This eliminates interference from the distraction
of presenting various stimuli simultaneously, as occurs, for
example, in the Regard (1981/1998) pencil-and-paper
version. If, on the one hand, this eliminates interference
from distraction, on the other hand, it restricts the possibility
of erroneous responses, and increases RT.
In order to provide continuity to studies employing the
computerized Stroop Test in the evaluation of selective
attention, new studies are being conducted, seeking to derive
normative data for Brazilian children, which will allow
neuropsychologists to compare patients’ performances.
Possible effects of important variables, such as sex, type of
school, and age on performance in the Stroop Test in children
with psychiatric disorders may be studied more thoroughly
in this extensive normalizing investigation, verifying whether
the same patterns observed in this study emerge, such as
absence of an effect of age or school type.
American Psychiatric Association. (2000). Diagnostic and statistical
manual of mental disorders 4thed. text revision. Washington,
D.C.: Author. (Translation: C. Dornelles, Porto Alegre, RS:
Andrade, E.R., & Scheuer, C. (2004). Analysis of the efficacy of
methylphenidate using the abbreviated Conners questionnaire
in attention deficit hyperactivity disorder. Neuropsychiatric
Archives, 62, 81-85.
COMPUTERIZED STROOP TEST IN ADHD EVALUATION
Araújo, C. (2004). Neuropsychiatric evaluation of attention. In
L.E.L.R. Valle & F.C. Capovilla (Eds.), Multidisciplinary themes
in neuropsychology and learning (pp. 501-505). Ribeirão Preto,
São Paulo: Tecmedd.
Barbosa, G.A., & Gouveia, V.V. (1993). The hyperactivity factor
in the Conners Questionnaire: Conceptual validity and
diagnostic standards. Themes, 46, 188-202.
Barkley, R.A. (1997). Behavioral inhibition, sustained attention,
and executive functions: Constructing a unifying theory of
ADHD. Psychological Bulletin, 121, 65-94.
Barnett, R., Maruff, P., Vance, A., Luk, E. S. L., Costin, J., Wood,
C., & Pantelis, C. (2001). Abnormal executive function in
attention deficit hyperactivity disorder: The effect of stimulant
medication and age on spatial working memory. Psychological
Medicine, 31, 114-121.
Benczik, E.B.P. (2000). Manual of the Scale for the Attention Deficit
Hyperactivity Disorder: Teachers´ version. São Paulo: House
of Psychology Editor.
Bisquerra, R., Sarriera, J.C., & Matínez, F. (2004). Introduction to
statistics: Computerized focus on the statistical package SPSS.
Porto Alegre, RS: Artmed.
Bush, G., Frazier, J.A., Rauch, S.L., Seidman, L.J., Whalen, P.J.,
Jenike, M.A., Rosen, B.R., & Biederman, J. (1999). Anterior
cingulate cortex dysfunction in attention-deficit/hyperactivity
disorder revealed by MRI and the Counting Stroop. Biological
Psychiatry, 45, 1542-1552.
Bush G., Luu P., & Posner M. (2000). Cognitive and emotional
influences in anterior cingulate cortex. Trends in Cognitive
Sciences, 4, 215-222.
Capovilla, A.G.S., Montiel, J.M., Macedo, E.C., & Charin, S.
(2005). Computerized Stroop Test. Itatiba, São Paulo: University
Carter, C. S., Krener, P., Chaderjian, M., Nortycutt, C., & Wolfe,
V. (1995). Asymmetrical visual-spatial attentional performance
in ADHD: Evidence for a right hemispheric deficit. Biological
Psychiatry, 37, 789-797.
Cozza, H.F.P (2005). Evaluation of executive function in children
and correlation with attention and hyperactivity. Doctoral
dissertation. University São Francisco, Itatiba, São Paulo.
Damásio, A.R. (1995). El error de Descartes [Descartes’ error].
Barcelona: Grijalbo Mondadori. (Translation: D. Vicente & G.
Segurado, São Paulo: Companhia das Letras, 1996.).
Freire, A.C.C., & Pondé, M.P. (2005). Pilot study of prevalence
in attention deficit hyperactivity disorder among school children
in the city of Salvador. Neuro-Psychiatric Archives, 63, 474-
Gazzaniga, M.S., Ivry, R.B., & Mangun, G.R. (2002). Cognitive
neuroscience: The biology of the mind. New York: Norton.
Gil, R. (1999). Neuropsicología [Neuropsychology]. Barcelona:
Masson. (Translation: M.A.A.S. Doria. São Paulo: Editora
Goldberg, E. (2001). The executive brain: Frontal lobes and the
civilized mind. Oxford, UK: Oxford University Press.
(Translation: R. Fiker & M.E. Fiker, Rio de Janeiro: Imago
Goyette C.H., Conners C.K., & Ulrich R.F. (1978). Normative data
in revised Conners Parent and Teacher Rating Scales. Journal
of Abnormal Child Psychology, 6, 221-36.
Houghton, S., Douglas, G., West, J., Whithing, K., Wall, M.,
Langsford, S., Powell, L., & Carroll, A. (1999). Differential
patterns of executive function in children with attention deficit
hyperactivity disorder according to gender and subtype. Journal
of Child Neurology, 14, 801-805.
Kerns, J. G., & Berenbaum, H. (2003). The relationship between
formal thought disorder and executive functioning component
processes. Journal of Abnormal Psychology, 112, 339-352.
Knapp, P., Rohde, L.A., Lyszkowski, L., & Johannpeter, J. (2002).
Cognitive therapy-behavior in attention deficit hyperactivity
disorder: Therapeutic manual. Porto Alegre, RS: Artmed.
Lezak, M.D. (1995). Neuropsychological assessment (3rded.). New
York: Oxford University Press.
Luria, A.R. (1973). Fundamentals of neuropsychology. (Translation:
J.A. Ricardo, São Paulo, Edusp, 1981).
Macedo, E.C., Capovilla, F.C., Diana, C., & Covre, P. (1998).
Development of computerized evaluation instruments for
cognitive functions in the WWW: The possible and the
necessary. In F. Capovilla, M.J. Gonçalves, & E.C. Macedo
(Eds.), Technology in Cognitive (Re)habilitation (pp. 21-32).
São Paulo: Sociedade Brasileira de Neuropsicologia e Edunisc.
MacLeod, C., & Rutherford, E.M. (1992). Anxiety and the selective
processing of emotional information: Mediating roles of
awareness, trait and state variables, and personal relevance of
stimulus materials. Behavior Research and Therapy, 30, 479-
MacPherson, S.E., Phillips, L.H., & Sala, S.D. (2002). Age,
executive function, and social decision making: Dorsolateral
prefrontal theory of cognitive aging. Psychology and Aging,
Mattos, P. (2002). Neurological evaluation of ADHD. In E.C.
Macedo, M.J. Gonçalves, F.C. Capovilla, & A.L. Sennyey
(Eds.), Technology in cognitive (re)habilitation (pp. 321-328).
São Paulo: Edunisc.
Mattos, P., Saboya, E., Kaefer, H., Knijnik, M.P., & Soncini, N.
(2003). Neuropsychology of ADHD. In L.A. Rohde & P.
Mattos (Eds.), Principles and practices in ADHD (pp. 63-74).
Porto Alegre, RS: Artmed.
Mogg, K., Kentish, J., & Bradley, B.P. (1993). Effects of anxiety
and awareness on color identification latencies for emotional
words. Behavior Research and Therapy, 31, 559-567.
Raven, J. C. (1938). Progressive matrices. A perceptual test of
intelligence. London: Lewis. (Translation: A.L. Angelini, I.C.B.
Alves, E.M. Custódio, W.F. Duarte, & J.L.M. Duarte. Matrizes
Progressivas Coloridas de Raven: Escala Especial. Manual.
São Paulo: CETEPP, 1999).
Raven, J, Raven, J. C. & Court, J. H. (1998). Raven Manual.
General Overview.Oxford: Oxford Psychologists Press.
(Translation: A.L. Angelini, I.C.B. Alves, E.M. Custódio, W.F.
Duarte, & J.L.M. Duarte. Matrizes Progressivas Coloridas
de Raven: Escala Especial. Manual. São Paulo: CETEPP,
Regard, M. (1998). Cognitive rigidity and flexibility: A Download full-text
neuropsychological study. In O. Spreen & E. Strauss (Eds.),
A compendium of neuropsychological tests. Oxford, MA:
Oxford University Press.
Rosin, F.M. (2001). Some experimental contributions in the study
of the negative priming effect in selective attention tasks.
Doctorate Examination. University of São Paulo, Ribeirão
Preto, São Paulo.
Scheres, A., Oosterlaan, J., Geurts, H., Morein-Zamir, S., Meiran,
N., Achut, H., Vlasveld, L., & Sergeant, J.A. (2004). Executive
functioning in boys with ADHD: Primarily an inhibition deficit?
Archives of Clinical Neuropsychology, 19, 569-594.
Sergeant, J.A., Geurts, H., & Oosterlaan, J. (2002). How specific
is a deficit of executive functioning for attention-
deficit/hyperactivity disorder? Behavioral Brain Research, 130,
Silberstein, R. B., Farrow, M., Levy, F., Pipingas, A., Hay, D. A.,
& Jarman, F. C. (1998). Functional brain electrical activity
mapping in boys with attention-deficit/hyperactivity disorder.
Archives General Psychiatry, 55, 1105-1112.
Sternberg, R.J. (1996). Cognitive psychology. (Translation: M.R.B.
Osório, Porto Alegre, RS: Medical Arts, 2000).
Stroop, J.R. (1935). Studies of interference in serial verbal reaction.
Journal of Experimental Psychology, 18, 643-662.
Szobot, C., Eizirik, M., Cunha, R.D., Langleben, D., & Rohde,
L.A. (2001). Neuroimage in attention deficit hyperactivity
disorder. Brazilian Review of Psychology, 23, 32-35.
Willcutt, E.G., Pennington B.F., Boada, R., Ogline, J.S., Tunick,
K.R.A., Chhabildas, N.A., & Olson, R.K. (2001). A comparison
of the deficits in reading disability and attention-
deficit/hyperactivity disorder. Journal of Abnormal Psychology,
World Health Organization. (1992). The ICD-10 classification of
mental and behavioral disorders: Clinical descriptions and
diagnostic and guidelines. Geneva, Switzerland: Author.
(Translation, Porto Alegre, RS: Medical Arts, 1993).
Received April 13, 2006
Revision received July 31, 2006
Accepted January 17, 2007
ASSEF, CAPOVILLA, AND CAPOVILLA