[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 Please do not quote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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25

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