Post on 26-Jul-2020
[Running Head]: ADHD and cognitive performance
Attention deficit hyperactivity disorder (adhd): Attention and working
memory performance in a school aged children
Célia G. Oliveira & Pedro B. Albuquerque1 (University of Minho, Braga, Portugal)
In press at The International Journal of Clinical and Health Psychology. Please do not quote
without permission.
1 Corresponding Author: Instituto de Educação e Psicologia da Universidade do Minho. Campus de Gualtar. 4710-057 Braga- Portugal. Email Address: pedro.b.albuquerque@iep.uminho.pt The present work was supported by the grant SFRH/BD/23277/2005 of Fundação para a Ciência e a Tecnologia (FCT).
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Attention Deficit Hyperactivity Disorder: Performance of a school aged
group in attention and working memory tasks
ABSTRACT: Attention Deficit/ Hyperactivity Disorder (ADHD) is one of the most commonly diagnosed problems in childhood and adolescence. However, the literature often presents contradictory data reflecting the lack of consensus about many aspects of the disorder. The current paper presents a study with a community sample of school aged children (7 to 11 years old) identified with ADHD according to a 100% of agreement between parents and teachers symptom ratings. The main goal of the study was to analyse the ADHD group performance on attention and working memory tasks and compare it with results of a matched control group. Data indicate the poorer performance of the hyperactive group in all tasks, with significant statistical differences in most of them. The exception was on verbal working memory tests, with no significant differences between groups. Results are discussed in terms of currently debated issues, such as the nature of the disorder and of the cognitive deficits associated to it. KEY WORDS: Attention Deficit Hyperactivity Disorder; Working Memory; Attention; “Ex post facto” Study.
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Attention Deficit Hyperactivity Disorder: Performance of a school aged group in
attention and working memory tasks
Introduction
The present study stands on the growing recognition of executive functioning contributions to
the comprehension of Attention Deficit/ Hyperactivity Disorder (ADHD) (Barkley, 1997,
1999; Biederman et al., 2004; Brocki & Bohlin, 2006). By definition, executive functions are
associated with a broad range of cognitive skills, such as working memory, sustained and
selective attention, response inhibition, visual search or verbal and auditory processing
(Brocki & Bohlin, 2004). In this paper, our focus was on attention and working memory
domains (Baddeley & Hitch, 1974, Baddeley, 2002).
The available research points out the inconsistence of data about the role of working memory
to the understanding of executive dysfunctioning in ADHD. When compared with reading
disabled groups, for example, some studies found that ADHD children have lower
performance on working memory tasks, particularly in those involving the central executive
(e.g. Roodenrys, Koloski, & Grainger, 2001). On the other hand, there are studies that do not
replicate such findings, indicating that both groups present verbal working memory deficits
along with other executive dysfunctions, but reading disabled children perform worst than
ADHD (e.g. Willcutt, Pennington, Olson, Chhabildas, & Hulslander, 2005). There are, also,
results that confirm the presence of executive dysfunction in ADHD groups but do not find
significant verbal working memory impairment, particularly when learning disabilities are
excluded (Breier, Gray, Klass, Fletchner, & Foorman, 2002; Rumsey, 2004; Saito, 2001).
The comparison with other clinical samples, such as groups with high functioning autism
(HFA), has been inconclusive too. A relevant study of executive function (EF) deficits in
ADHD and HFA children (6-12 years-old) showed that HFA group displayed deficits in most
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of the executive domains (inhibition, planning, cognitive flexibility, and verbal fluency) and
was particularly more impaired, than ADHDs, in cognitive flexibility and planning. ADHD
group performed worst in inhibition and verbal fluency, but both groups performed equal in
working memory domain, even when compared with normal controls. Moreover, in this
study, the profile of executive dysfunction was worst in HFA group than in ADHD (Geurts,
Verté, Oosterlaan, Roeyers, & Sergeant, 2004). In turn, another recent research contrasting
autism spectrum disorders and ADHD, ascertained that hyperactive children had a more
severe profile of executive dysfunctioning and were more impaired in planning/working
memory performance (Happé, Booth, Charlton, & Hughes, 2006).
Finally, the comparison with normative samples is even scarcer and also with contradictory
results. Scheres and colleagues (2004), for instance, assessed the performance of a school
aged group of ADHD boys in a wide range of executive function tasks, including attention
and working memory domains. The authors found that differences between ADHD and
normative groups disappeared when age, intellectual performance and non-executive skills
were controlled. In contrast, other studies asserted that EF measures, particularly working
memory, inhibition and emotion regulation, differentiate the performance of ADHD and
normal controls, and results obtained by participants, in such measures, were good predictors
of group status (Berlin, Bohlin, Nyberg, & Janols, 2004).
Due to the compound data and the need of a better understanding about cognitive functioning
in ADHD, the present study aimed to explore the performance of a school aged ADHD group
in working memory and attention measures by comparison with a matched normative sample.
Method
Participants
Twenty four children integrated the sample, divided by two groups: ADHD (N=12) and
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normal control (NC) (N=12). Participants had between 7 and 11 years old, attended between
first and fourth grade, and were recruited from 7 different public schools (urban and rural)
totalizing a universe of 30 classes with 670 students. Normal controls had 5 girls and 7 boys,
with a mean age of 8 years old (SD = 1 year and 3 months). ADHD group had 3 girls and 9
boys with a mean age of 8 years and 6 months (SD = 1 year and 4 months). The groups were
equivalent in variables concerning to age [t(22) = .87, p > .05], school grade [t(22) = .22, p >
.05] and socioeconomic status [U = 55.0, N1 = 12, N2 = 12, p = .28].
Instruments
Behavioural assessment: All participants were screened through the parent and teacher forms
of the Achenbach System of Empirically Based Assessment (ASEBA). The Child Behaviour
Checklist (CBCL), for parents, and the Teacher Report Form (TRF) are multidimensional
instruments designed to assess a broad range of behavioural and emotional problems
(Achenbach, 1991). We used the second part of these rating scales consisting of 120 items
that can be rated in a 3 point likert scale. The items are descriptive of the child’s behaviour
and enable a score for 8 different scales corresponding to a profile of 8 empirically
determined syndromes, specifically: withdrawn, somatic complaints, anxious/depressed,
social problems, thought problems, attention problems, delinquent behaviour and aggressive
behaviour. To the present study it is particularly important the attention problems syndrome,
which has been associated with the categorical diagnostic of ADHD (Gonçalves & Simões,
2000).
Intellectual assessment: The Portuguese paper and pencil version of the Raven’s Coloured
Progressive Matrices (Simões, 2000) was used to rule out intellectual deficits. This instrument
has several advantages, such as the reduced influence of educational, cultural and academic
background. The non-verbal nature of the task reduces, also, the risks of misunderstanding
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instructions and the impact of eventual problems in verbalization of thoughts (Simões, 2000).
The administration was made individually.
Memory measures: Several tasks were used to assess working memory. In the present study
were analyzed three of the Baddeley’s model components: the phonological loop, the
visuospatial sketchpad and the central executive (Baddeley & Hitch, 1974; Baddeley, 2002).
Digit span
The digit span subtest (WISC-III, Wechsler, 1994) was chosen because it’s considered an
important measure of verbal working memory (Saito, 2001). It is assumed that the direct
condition of digit span can be used to assess the performance of phonological loop and the
backward form is an effective measure of the central executive ability (Gathercole &
Pickering, 2000; Hutton & Towse, 2001; Jeffries & Everatt, 2004). According to these
assertions, the test allowed us to evaluate two of the three working memory components
studied. The implementation procedure included two series of items and the administration
was stopped when children failed two series of the same items extension.
Non-word repetition
It was used the non-word repetition test of a Portuguese battery to the assessment of linguistic
skills (Viana, 1998), in order to evaluate phonological loop performance. The absence of
meaning in the non-word repetition allows a more direct measure of phonological skills. This
occurs because children do not suffer the differential effect that would be produced by
familiar words (Gathercole & Pickering, 2000).
Corsi block-tapping test
The employ of the Corsi blocks (Corsi, 1972) was based on the evidence of its usefulness in
assessing visuospatial working memory (e.g., Towse & Houston-Price, 2001). It was used the
bi-dimensional arrangement and items were administered in direct and backward conditions at
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a pace of one item per second. The series of items progressively increased in extension and
complexity.
Attention measures: Two distinct tasks were used: a modified version of the Stroop test and a
reaction time task (simple and discriminative versions)
Stroop task
The Stroop task (Stroop, 1935) measures the ability to activate attentional skills to suppress
automatic responses. The use of this test was based on its well established validity to reveal
attention problems (e.g., van Mourik, Oosterlaan, & Sergeant, 2005). The original task has
been used in a great variety of versions and presentations. Similarly, it was conceived a
modified version to prevent the impact of different reading skills, due to the fact that
participants attended to primary school grades. Like in the original test, there was a control
and an interference condition. In the first one, participants counted series of circles that varied
between 1 and 9 items, in a total of 72 series and 370 circles. In experimental / interference
version children counted 72 series of numbers instead of circles, but should indicate the
quantity and not the numbers seen. For instance, when children saw the series composed by
the number “9” should answer “one”, because these series had only one number; to the series
composed by the sequence “3 3”, children should answer “two”. Scores relied on the total
number of commission and omission errors and on total time spent in the task.
Reaction time task
Reaction time data refers to the latency between the presentation of a stimulus and the
beginning of an answer. The mean value of reaction time is usually reported as processing
speed, while the number of correct responses and omission errors refer to sustained attention
abilities. Commission errors may reflect response inhibition skills (Avila, Cuenca, Félix,
Parcet, & Miranda, 2004). Several variants of this kind of tasks have been used in ADHD
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studies (Frazier, Demaree, & Youngstrom, 2004). A computerized presentation was
conceived based on the informatics application SuperLab Pro 2.0 (Cedrus, 1999) and
consisted of two different tasks: simple reaction time (SRT) and discriminative reaction time
(DRT). In each task were displayed 66 stimuli, with variable interstimulus intervals, and there
was a previous sequence of training trials. Results referred to reaction times, commission
(incorrect response) and omission (targets without response) errors.
Procedure
Assignment to diagnostic and control groups
Participants were screened, and assigned to groups, based on information of multiple sources
(parents and teachers). Children were admitted to the diagnostic group only when scores in
the attention problems scales of CBCL and of TRF were equal or above the 95th percentile
(cut-off point), equivalent to a T score equal or above 67 (Achenbach, 1991). This cut-off
point increases diagnostic validity of the participants assigned and has been used by other
studies (e.g., Nolan, Volpe, Gadow, & Sprafkin, 1999). Due to the high comorbidity
prevalence in ADHD (e.g., Burt, Krueger, McGue, & Iacono, 2001), it was decided that only
remained in the diagnostic group those subjects whose comorbid problem scales were not
higher, in severity, than attention problems scale. This criterion was also consonant with other
studies (e.g., Banaschewski et al., 2004). Once concluded this selection procedures,
participants were assessed with Raven’s Colored Progressive Matrices. This was done to
exclude children with possible intellectual impairment.
The assignment to control group was also based in inter-informants agreement and children
were admitted to this group when parents and teacher reported a significant absence of
problems, again by comparing to 95th percentile score. Selection criteria for both groups were:
the absence of severe sensory-motor deficits, not being medicated, the absence of two or more
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school retentions along with an IQ score equal or above average, the absence of known
linguistic problems and do not belong to an extremely low socioeconomic status. Based on
this criteria and procedures, we assessed one hundred children, previously indicated by
teachers, and we excluded 68 potential ADHD participants and 8 controls.
Collecting data procedures
It was first obtained the informed consenting of all children’s parents. Teachers and parents
also received feedback of their participation. The direct evaluation of children was divided in
two different moments for a total of six tasks previously reported. As already described, the
direct assessment always began with the Raven’s Colored Progressive Matrices and was
followed by two other tasks. Except for Matrices, all measures were counterbalanced.
Results
First will be described the results of the behaviour rating scales, than Raven’s Matrices scores
and at the end will be presented attention and memory data analysis.
Behaviour rating scales
Comparing with normal controls, ADHD participants displayed significant higher results in four CBCL
scales: attention problems [t(22) = 8.38, p < .001]; social problems [t(22) = 5.42, p < .001];
aggressive behaviour [t(22) = 2.53, p < .05]; and thought problems [t(22) = 2.28, p < .05]. The
prevalence of comorbidity, in ADHD group, was of 42% for social problems, 33% for
thought problems and 25% for aggressive behaviour. The severity of these comorbid
behaviours is indicated by the maximum and minimum values on respective scales and points
to higher absolute scores for social problems, followed by aggressive behaviour and, with the
lowest score, by thought problems.
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= TABLE 1 =
The TRF scores showed that teachers, as did the parents, indicated a higher prevalence of
comorbid problems in children with ADHD. Consequently, all syndrome scales presented
significant statistical differences between groups, except for somatic complaints. However,
for withdrawn and thought problems the maximum values only slightly suppressed the cut-off
point. Along with somatic complaints, withdrawn and thought problems were the least severe
and least prevalent syndromes (4 participants or 33%). Comorbid with attention problems,
teacher specified, by decreasing order of severity and prevalence, social problems and
aggressive behaviour (75% e 67% respectively), followed by delinquent behaviour and
anxious/depressed problems. We must emphasize that, although teachers scored 42% of
ADHD children with delinquent behaviour problems, this scale had higher scores than
anxiety/depressed problems which were found in 50% of the clinical group. An overview of
results shows that children scored by the teachers with more severe attention problems were
more likely to present higher results in the remaining scales.
Raven’s colored progressive matrices
Descriptive data to control group were M = 23.00, SD = 4.86 and SEM = 1.40. Results to
ADHD group were M = 20.00, SD = 4.02 and SEM = 1.16. The groups did not differed in
Raven’s results [t(22) = 1.65, p > .05] and all participants performed equal or above the
average.
Memory measures
In digit span measure, groups differed in backward condition [t(22) = 3.86, p < .001] and in
global results [t(22) = 3.98, p < .001], with the control group performing better. In the direct
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condition results followed the same pattern but differences were not statistically significant
[t(22) = 2.02, p > .05].
Corsi blocks task differentiated groups in direct condition and global results [t(22) = 2.26, p <
.05 and t(22) = 2.12, p < .05, respectively], with a poorer performance of ADHD group. In
backward condition average and standard deviation results point to a low number of correct
responses in both groups [t (22) = .96, p > .05].
In non-word repetition task there were no significant differences too [t(22) = 1.82, p > .05],
although control group performed better than ADHDs, with 54% of correct responses versus
38% .
= TABLE 2 =
Attention measures
Stroop task showed that groups did not differ in control condition. Results comparing groups
were as follow: for omission errors [t(22) = .26, p > .05] and for total number of errors, the
sum of omission and commission errors, [t(22) = 1.81, p > .05]. These results reveal that
groups were equivalent in pre-requisites for the performance of interference (experimental)
condition. In this last condition there were significant differences between groups in omission
errors [t(22) = 2.65, p < .05] and in total number of errors [t(22) = 2.60, p < .05], with ADHD
group performing worst. Despite that, we can conclude that differences between groups were
based on performance but not on time to complete the task [t(22) = 1.02, p > .05].
The analysis of the Reaction time tasks shows the absence of groups differences in all
parameters of the simple task, as indicated by results in omission errors [t(22) = 1.48, p > .05],
in commission errors (mean and standard deviation = 0 in both groups) and in time spent on
task [t (22) = 1.60, p > .05]. In discriminative task (DRT) there were dissimilar results as
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shown in table 3.
= TABLE 3 =
In DRT groups differed in omission errors and in total reaction time, with ADHD group
presenting the worst results. About commission errors, a global analysis shows that there
were no group differences [t(22) = .50, p > .05]. However the examination of performance in
each quartile of the task (the division of the total task in four identical intervals with 16 trails
respectively) reveals a progressive decreasing of commission errors in control group, against a
significant statistic increasing number of errors in the last quartile for ADHD group [t(22) =
3.36, p < .01]. The same kind of analysis for reaction time points to similar results, with
ADHD group performing worst in the second half of the task, spending more time in 3rd [t(22)
= 2.09, p < .05] and 4th [t(22) = 2.28, p < .05] quartiles. The examination of results evolution
during the task allows a better understanding of the performance quality and is a procedure
adopted by other studies too (e.g., Collings, 2003).
Discussion
First we would like to concentrate in sample characteristics and in the matching of groups by
age, intelligence, school grade and socioeconomic status. It has been suggested that the
deficient control of these variables restricts the results comprehension in ADHD studies. Age
and socioeconomic status, for instance, may affect the dimensional evaluation of behaviour
problems because younger children and those from lower socioeconomic status tend to
present higher scores (more severe syndromes) when comparing to older children or to
children from higher socioeconomic status (Achenbach, 1991). Age can also improve
children’s performance in working memory tasks (e.g. Gathercole & McCarthy, 1994) and
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social deprivation can contribute to a poorer cognitive performance (cf. Seidman, Biederman,
Monuteaux, Weber, & Faraone, 2000). The sample of this study included diverse
socioeconomic levels, corroborating the heterogeneous origins of ADHD children (Barkley,
1999). Finally, matching the groups by Raven’s scores and school grades made possible to
control the potential impact of cognitive achievement and previous academic knowledge in
attention and memory performances (Decker, McIntosh, Kelly, Nicholls, & Dean, 2001). The
effort made to obtain equivalent groups aimed at increase the extension of results
interpretation.
The behavioural profile shows that groups were contrasted in attention problems. Differences
between groups in the remaining scales reflect the high prevalence of comorbidity in ADHD
children and the overall higher ratings, even when results are not clinically relevant. High
prevalence of comorbidity is in line with other findings in community samples of ADHD.
Kadesjö and Gillberg (2001) argued that a pure ADHD type would be the exception, not the
rule. Being so, studies based on pure ADHDs would correspond to atypical samples that do
not represent the common situations observed in general population. The most rated
comorbidities were social problems and aggressive behaviour. These results were obtained in
parents and teachers ratings. The significant prevalence of these problems, and the association
between them in ADHD, has also been addressed by other studies (e.g. Banaschewski et al.,
2004; Burt et al., 2001; Hinshaw, Zupan, Simmel, Nigg, & Melnick, 1997; Waschbusch,
2002). Internalizing problems appear with lower but, still, significant prevalence, especially
those related with withdrawn and anxious/depressed scales. For these last one, teachers rated
50% of ADHD children with this kind of problems, while parents indicated only 25%.
Considering overall affective problems (anxiety and depression), research points to
comorbidity values near to 70%, with a range between 10 to 40% in anxiety disorders and
between 20 to 30% for depressive problems (Barkley, 1999; Decker et al., 2001). It should be
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noted that in the present study results could not discriminate between anxiety and depression
because they were rated by the same scale. Still, we can conclude that comorbidity profile of
ADHD group is coherent with research in this area, pointing to the predominance of social
and externalized behaviour problems over internalized ones. Comorbidity ratings suffer a
significant decrease when applied inter-informant agreement criterion. This is expected
because of the usual low rates of concordance between parents and teachers [correlation
values around .30 (Burt et al., 2001) and .44 (Achenbach, 1991)]. Even so, our selection
procedure of ADHD participants was based on a 100% of teachers and parents agreement
about attention problems. This was done because the use of a unique source of information
considerably increases the probability of false positive findings (Kube, Peterson, & Palmer,
2002).
Results on working memory measures, such as in backward condition of digit span, indicate
possible group differences in central executive skills. These results can be due to the
attentional demands especially associated to backward order (Frazier, Demaree, &
Youngstrom, 2004; Hutton & Towse, 2001; Saito, 2001).
The combination of results in direct condition of digit span and in non-word repetition seem
to indicate the absence of differences between groups in phonological loop abilities. These
findings are coherent with other studies that found an association between ADHD and central
executive dysfunctions, but did not find phonological loop disabilities (Breier et al. 2002;
Saito, 2001), especially when learning problems are excluded (Jonsdottir, Bouma, Sergeant,
& Scheder, 2005). In present study, the exclusion of participants with more than one grade
retention and/ or with a poor school performance (rated by teachers as significantly lower than
average), may explain these verbal working memory results. Moreover, the consistence of
data in both measures referred to the same ability (direct digit span and non-word repetition)
increases the results confidence (Jeffries & Everatt, 2004).
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Global and direct results in Corsi blocks point to a disadvantage of ADHD group in
visuospatial memory tasks, as found by other researches (e.g. Martinussen, Hayden, Hogg-
Johnson, & Tannock, 2005). In backward condition the absence of differences may be due to
the low number of correct responses observed in both groups, although control group had
twice accurate responses. Still, the overall results are low, possibly reflecting a high degree of
task difficulty and an insufficient discriminative power.
In attention measures the nonappearance of group differences in control condition of Stroop
excluded possible influence of previous knowledge in experimental (interference) condition
results. About this topic, a meta-analysis of studies with Stroop task concluded that the lack of
significant results with ADHD groups, in interference condition, was probably related with
the failing in controlling important variables, such as the word’s length or the colour naming
ability. Particularly in school aged children, this kind of variables might compete with
attention abilities (Van Mourik, Oosterlaan, & Sergeant, 2005). Similarly, it was our aim that
experimental/ interference condition assessed only attentional skills. So, the worst
performance of ADHD group seems to corroborate data that validate the Stroop task as a
sensitive measure of divided attention. Identical results were obtained by other studies with
modified versions of the task (Nigg, Butler, Huang-Pollock, & Henderson, 2002). The
absence of group differences in time spent on experimental/ interference condition may be
due to the group’s homogeneity on pre-requisites to performing the counting task, or even to
its modification. In general, the number of commission errors is considered the main indicator
of attentional performance and time tends to reflect the influence of other variables, such as
the reading skills in original task. In ADHD research a poor performance in Stroop task has
been associated to a response inhibition deficit (Rosa, Oosterlaan, & Sergeant, 2005; Seidman
et al., 2000). Accordingly, Nigg and colleagues (2002) reported the sensitivity of this measure
to attention and executive problems.
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In discriminative reaction time ADHD group presented a worse performance, with a higher
number of omission errors and more time spent to complete the task, suggesting poorer skills
of sustained attention and visual processing speed (Avila et al., 2004). Results on commission
errors may indicate that a longer task could evidence a clearer group distinction because
control group presents a progressive decreasing of errors along the task, by contrast to ADHD
group that presented statistical significant improvement of errors in the last quartile. Taken
together, this pattern of results is relevant because the attention deficit, revealed by this kind
of tasks, is characterized by a gradual increasing in number of errors and in reaction time
(Börger et al., 1999). However, the worst performance of ADHD group in errors cannot be
generalized because it was obtained only in the last quartile of the task.
Considering the diversity and large amount of data about cognitive performance in ADHD,
our results seem to corroborate those that indicate the disadvantage of ADHD groups in
attention and non-verbal working memory performance (e.g., Berlin et al., 2004; Jeffries &
Everatt 2004; Tsal, Shalev, & Mevorach, 2005).
Finally, and although it overcomes the limits of the present study, it is important to note the
role that has been attributed to cognitive measures, such as attention and working memory, to
the understanding of executive functioning in ADHD children (Roodenrys, Koloski, &
Grainger, 2001). This issue has been considered central in the study of the disorder and seems
to assume an important role for future developments in the area. An example of such direction
is the development of recent research about executive functioning in pre-school and adult
samples (e.g., Barkley et al., 2002, Nigg et al., 2002; Nolan et al., 1999). Associated to this, is
another salient issue regarding the developmental conceptualization of the disorder, pointing
to an early beginning of symptoms expression and with considerable probability of becoming
chronic (e.g. Brophy, Pastor, Pallarés, & Llandrich, 2007; Turner, Blackwell, Dowson,
McLean, & Sahakian, 2005). Accordingly, different studies corroborate the preservation of
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executive deficits in ADHD adults. Hervey, Epstein and Curry (2004), in a meta-analysis of
33 papers, found a dysfunctional neuropsychological profile in ADHD adults characterized,
mainly, by attentional, response inhibition and memory deficits. The study of cognitive
functioning has also contributed to the development of neurogenetic models that examine the
incidence of neuropsychological deficits in ADHD relatives. Exploration of this topic will
probably help to clarify the aetiology and nature of the disorder (Nigg et al., 2002).
Despite the usefulness of cognitive skills research, it should be noted that the high prevalence
of comorbidity and the heterogeneous profile of ADHD children make difficult the
systematization of results. Considering this limits and the diversity of methods (and data) of
research, more studies are needed to understand the cognitive functioning of ADHD children
and, as a result, to better comprehend the disorder (Biederman et al., 2004; Nigg et al., 2002).
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Table 1: Mean (M), standard-deviation (SD) and statistical differences (t) between groups in CBCL and TRF scales.
CBCL TRF
Scales Groups M SD t M SD t
Withdrawn ADHD
NC 60.33 56.33
9.90 5.55
1.22 62.58 52.92
6.93 3.83
4.23**
Somatic complaints
ADHD NC
53.67 53.58
6.53 5.44
0.03 57.25 55.08
7.15 6.83
0.76
Anxious / depressed
ADHD NC
62.75 57.58
6.92 6.43
1.90 65.08 55.58
9.36 5.16
3.08**
Social problems
ADHD NC
66.83 51.92
8.55 4.23
5.42* 69.92 53.25
5.81 4.16
8.08**
Thought problems
ADHD NC
61.17 55.00
7.28 5.88
2.28** 60.58 50.67
8.49 2.31
3.90**
Attention problems
ADHD NC
73.58 54.17
5.90 5.44
8.38* 76.08 52.33
9.81 3.20
7.97**
Delinquent behaviour
ADHD NC
60.50 55.50
8.95 5.42
1.66 65.92 52.50
11.18 3.21
4.00**
Aggressive behaviour
ADHD NC
62.25 54.83
8.54 5.52
2.53** 70.25 55.17
13.59 6.24
3.49**
*p < .01; **p < .05
NC – Normal Control Group
26
Table 2: Mean (M), standard-deviation (SD) and statistical differences (t) between groups in digit span, non-word repetition and Corsi blocks tasks.
Measures Groups M SD t
Digit Span direct
ADHD NC
3.58 4.13
0.47 0.80
2.02
Digit Span backward
ADHD NC
1.63 3.04
1.15 0.54
3.86**
Digit Span total
ADHD NC
5.21 7.17
1.34 1.05
3.98
Non-words ADHD
NC 1.50 2.17
0.85 0.94
1.82
Corsi direct
ADHD NC
2.63 3.79
1.69 0.58
2.26*
Corsi backward
ADHD NC
0.21 0.50
0.72 0.77
0.96
Corsi total
ADHD NC
2.83 4.29
2.06 1.20
2.12*
*p < .01; **p < .05
27
Table 3: Mean (M), standard-deviation (SD) and statistical differences (t) between groups in reaction time task.
Reaction Time Groups M SD t
commission errors
ADHD NC
0 0
0 0
-
omission errors
ADHD NC
0.17 0
0.39 0
1.48 Simple
time
(milliseconds) ADHD
NC 467 339
138 115
1.60
commission errors
ADHD NC
3.00 2.58
1.71 2.31
0.50
omission errors
ADHD NC
1.08 0.00
1.62 0.00
2.32**
total errors ADHD
NC 4.08 2.58
2.88 2.31
1.41
Discriminative
time (milliseconds)
ADHD NC
731 603
110 149
2.41**
*p < .01; **p < .05