Visual Attention,Orthographic Word Recognition,and Executive Functioning in Children With ADHD,Dyslexia,or ADHD + Dyslexia

Abstract

Objective: The current study examined the differences in visual selective attention, orthographic word recognition, and executive functioning. Method: One hundred and forty Ecuadorian children in third and fifth grades of elementary school (8-10 years old) participated in the study—35 with only dyslexia (DD), 35 with the combined type of attention deficit and hyperactivity disorder (ADHD-C), 35 with disorders (DD + ADHD-C), and 35 typical development children (TD). Results: The Ecuadorian children with DD and/or ADHD-C in this age range usually have difficulties in visual selective attention, and also in orthographic word recognition. The executive functioning results showed that such functioning was worse in the ADHD-C groups (with or without DD), but not in the DD group, supporting the dissociation between DD and ADHD-C in executive functioning in this population. Conclusion: The DD + ADHD-C comorbidity produces worse deficits compared to DD, but not compared to ADHD-C, supporting the idea that there are common factors in DD and ADHD-C.

Keywords

executive function dyslexia ADHD visual attention

In the American Psychiatric Association (APA) framework, developmental dyslexia (DD) is a neurodevelopmental disorder that specifically affects learning to read, and it is characterized by persistent difficulties in decoding written verbal stimuli, manifested as errors or slowness in reading (APA, 2013). In this same framework, ADHD is another neurodevelopmental disorder, characterized by attention and/or hyperactivity and impulsivity problems that lead to inappropriate behaviors in the context and, frequently, to difficulties relating to other people (Faraone, Sergeant, Gillberg, & Biederman, 2003). To diagnose either of these two disorders, the difficulties must not be related to a below-average intelligence level, sensory deficits, or insufficient schooling.

There is a serious lack of studies on dyslexia or ADHD in many developing countries and, in particular, in Ecuador, where Spanish is the official language. The prevalence of DD in Ecuador is unknown, but the studies conducted in other countries where Spanish is an official language suggest that about 3% of children aged 7 to 12 years would meet the criteria for this disorder (e.g., Jiménez, Guzmán, Rodríguez, & Artiles, 2009). As for ADHD, the results from a study in Ecuadorian students aged 14 to 18 years suggest that about 7.3% of this population would have the combined subtype of ADHD (Ramos Galarza, Bolaños Pasquel, & Ramos Galarza, 2015). As far as we know, no studies have aimed at estimating the prevalence of DD + ADHD in this country. Results from studies conducted in other countries indicate that such prevalence can be up to 3.7% in school population (Polanczyk, Willcutt, Salum, Kieling, & Rohde, 2014; Sexton, Gelhorn, Bell, & Classi, 2012), between 9% and 60% in children with DD, and between 11% and 52% in children with ADHD (DuPaul, Gormley, & Laracy, 2013). More importantly, research conducted on other countries also shows that children affected by DD and/or ADHD tend to have worse academic performance, which can affect their emotional well-being and academic and professional future (Fried et al., 2016; Sexton et al., 2012).

Thus, the motivation for the present work was to know if Ecuadorian children with DD or ADHD are at a disadvantage in a number of aspects relevant to academic performance, namely visual selective attention, orthographic knowledge, and executive functioning, which, accordingly to some related findings, could be impaired in these children. Moreover, we address the current debate around comorbidity of dyslexia and ADHD, that is, whether these two disorders are independent of each other, or they rather share some common causes.

The predominant opinion in the literature in recent decades has been that DD is an essentially phonological disorder, due to deficits in perceiving spoken sounds and in storing, manipulating, and recovering these sounds in the memory. The problems of children affected by DD on short-term verbal memory, phonological awareness, and rapid naming of visually presented stimuli seem to back up this opinion. However, there are also other points of view (see Pugh & Verhoeven, 2018, for an introduction to different theoretical perspectives about DD). Specifically, for some authors, the deficits in verbal information processing in DD would be produced by problems in basic attentional functions of orientation and selection of visual information, which would affect the detection, discrimination, and identification of graphemes and the order in which they appear –that is, in the initial stages of processing written language (Grainger, Dufau, & Ziegler, 2016). All of this would impede the formation of orthographic representations of words in the long-term memory, which, in turn, would be a disadvantage in reading and writing fluidly. Supporting this approach, there is evidence that children affected by DD have problems with the orientation and reorientation of visuospatial attention (Facoetti, Paganoni, Turatto, Marzola, & Mascetti, 2000; Franceschini et al., 2018; Hari & Renvall, 2001), the simultaneous processing of visual items (Bosse, Tainturier, & Valdois, 2007), the search for visual characteristics (Horowitz-Kraus, 2017), and dynamic, visual, or auditory sensory information processing (Collis, Kohnen, & Kinoshita, 2013; Facoetti et al., 2010; Gori, Cecchini, Bigoni, Molteni, & Facoetti, 2014; Judge, Knox, & Caravolas, 2013; Stein, 2018; Vidyasagar & Pammer, 2010). However, some studies have concluded that children with DD generally do not present selective visual attention problems (e.g., Lukov et al., 2015). In addition, for an increasing number of authors, DD has multiple causes (e.g., Carroll, Solity, & Shapiro, 2016; Peterson & Pennington, 2015).

Attentional deficits are an essential component of the ADHD diagnosis; however, specifically for the functions of orientation and selective attention, the studies are inconclusive. Although some studies have found that children with ADHD perform worse than the control group on tasks that involve these functions (Tsal, Shalev, & Mevorach, 2005), other studies have not found these differences (e.g., Booth et al., 2005; McAvinue et al., 2015), or if they have, the pattern of results has led the authors to conclude that the differences were due to other problems, for example, the tendency for ADHD children to answer too quickly (Mason, Humphreys, & Kent, 2003), operative memory problems (Mason, Humphreys, & Kent, 2005), or problems with the self-regulation of effort (Friedman-Hill et al., 2010; or Reijnen & Opwis, 2008), in other words, executive functioning problems (see the review by Wilding, 2005).

The executive functions (EFs) are attentional functions of control over cognition and behavior, and they allow us to guide our behavior and regulate ourselves to achieve goals for which we do not have a well-learned course of action. The EFs consist of a variety of mechanisms. There are inhibition mechanisms, by which we can block distracting information and cancel more or less automatic responses or responses guided by immediate reinforcements. Shift mechanisms operate to quickly and flexibly change the orientation of our mental or behavioral activity in order to select relevant information and adapt to changes in a situation according to our goals. Mechanisms of updating and monitoring the contents of the working memory act in order to reject contents that are no longer relevant for achieving our goals in a given situation and introduce and maintain the relevant contents active. There are also mechanisms involved in the planning and organization of complex tasks, ordering, initiative, and emotional control. All these mechanisms make it possible to optimize information processing (Posner & Rothbart, 2007), so that the person can plan, supervise, modulate, or coordinate the execution of the cognitive processes involved in achieving a certain aim. Children affected by ADHD usually have impairments in these types of functions (see, for example, Colomer, Berenguer, Roselló, Baixauli, & Miranda, 2017; Holmes et al., 2010; Kofler et al., 2018; Pennington & Ozonoff, 1996; Pievsky & McGrath, 2018; Roberts, Martel, & Nigg, 2017; Rubia, 2018).

However, some researchers point out that not all children with ADHD present the executive deficits typically found in most children with ADHD (see, for example, Roberts et al., 2017; or Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). Moreover, in those who do present executive impairments, only one or some of the evaluated functions can be affected. For example, a recent study of 55 children with ADHD between 8 and 13 years old (Kofler et al., 2018) found that 11% of them did not present deficiencies in any of the three EFs assessed, that is, inhibition, shift, and operative memory, and in 54%, only one of these three functions was affected.

One of the open questions in the literature on ADHD and DD is whether these two disorders have independent origins or are produced by common factors (from de Jong et al., 2009; Mayes, Calhoun, & Crowell, 2000; Willcutt et al., 2001), and EF assessment and linguistic processing have been important elements in the studies carried out to resolve this issue. Researchers who defend the independence of these two disorders point to the results of studies showing that children who have ADHD without DD have executive deficits that children with DD without ADHD do not, and children with DD without ADHD have specific difficulties that children with ADHD without DD do not have, mainly in phonological processing (e.g., Landerl et al., 2013; Purvis & Tannock, 2000). In addition, some studies show that DD + ADHD comorbidity exhibits the typical ADHD deficits plus the DD deficits, thus being more severe than when only one of the two disorders is present (e.g., Gooch, Snowling, & Hulme, 2011; or Maehler & Schuchardt, 2016).

However, the hypothesis of the independence of ADHD and DD is being questioned by authors who consider these disorders to be the result of a convergence of multiple causes of a genetic and environmental nature. Some of these causes can influence both disorders, and they can also interact, producing specific results (e.g., Pennington, 2006). In fact, some studies show that children affected by DD, but not by ADHD, also present certain EF deficits, for example, displaying worse results than children with typical development (TD) on cognitive inhibition tests (Reiter, Tucha, & Lange, 2005) or shift tests (Moura, Simões, & Pereira, 2015). Thus, a study comparing EF performance tests found that the only test on which the results of the group of children with ADHD were worse than those of the group of children with DD was planning (Marzocchi et al., 2008). Furthermore, some studies have found significant differences between children with and without ADHD in reading and reading-related tasks. For example, in the study carried out by Lúcio et al. (2017), a group of Brazilian children with ADHD made more mistakes on reading isolated letters and spelled single words worse from dictation, and in the study by Johnels, Kopp, and Gillberg (2014), almost two thirds of a sample of Swedish girls with ADHD had problems with spelling, word recognition, and short-term verbal memory. However, the reports on the methods of the two studies just mentioned do not allow to rule out that the ADHD groups included some individuals with DD besides ADHD, that is, a subgroup of ADHD + DD children. In any case, the study conducted by Adi-Japha et al. (2007) focused on normally reading children with and without ADHD, using Hebrew children, and the results showed that ADHD children with no reading difficulties committed more spelling errors on words from dictation. In addition, there are studies showing that DD + ADHD comorbidity does not necessarily imply a set of more severe deficits than when only one of the two disorders is present (e.g., Moura et al., 2017).

The current study analyzed visual selective attention on a test of discriminating differences in figures, orthographic recognition of Spanish words, and EF in Ecuadorian children in third and fifth grades of elementary school (8-10 years old) with only DD, only ADHD-C (combined type), DD + ADHD-C, or TD. In the Ecuadorian educational system, children in the third grade of elementary school have already gone through the process of explicitly learning how to read in their official language, Spanish. The first objective was to evaluate whether the discrimination of differences in figures distinguished children with DD and children with ADHD-C at these ages. If deficits in orientation and visual selective attention are in any way associated with DD, but not with ADHD-C, children in the DD and DD + ADHD-C groups should show worse performance on the difference discrimination task than the ADHD-C or TD groups. A second objective was to evaluate the differences in orthographic recognition between the groups. Our hypothesis was that the DD and DD + ADHD-C groups would show worse performance on the orthographic recognition task than the ADHD-C or TD groups, given that DD would directly affect the storage of orthographic representations. Third, considering the previous literature, we expected that the EF of children with ADHD-C, alone or with DD, would be worse than that of children with TD, but our main objective regarding EF assessment was to determine whether there were EF aspects that specifically differentiated the DD and/or ADHD-C groups from the other groups. According to the additive hypothesis about cognitive and behavior problems in DD + ADHD-C, we would expect the DD + ADHD-C group to combine the deficits of the DD and ADHD-C groups and, therefore, show the worst results. However, our hypothesis was that the DD + ADHD-C group would present several deficits that could not be explained by a mere combination of the deficits of the DD and ADHD-C groups.

Method

Participants

In order to form the different groups, 1472 students in the third and fifth grades of elementary school from 15 schools (35.7% public and 64.3% private) in the city of Cuenca (Ecuador) were assessed. Of them, 140 students were chosen, 35 of whom would belong to the DD group, 35 to the ADHD-C group, 35 to the DD + ADHD-C group, and 35 to the TD group matched on age, IQ, and gender. The demographic data are shown in Table 1.

Table 1.

Means and Standard Deviations of Age and IQ, Percentage of Sex, and Percentage of Students in Their Third to Fifth Academic Year, by Group.

	DDn = 35	ADHDn = 35	DD + ADHDn = 35	TDn = 35
Age (months)
M	109.70	110.40	110.1	108.20
SD	12.40	12.10	10.40	11.50
IQ
M	98.80	101.60	99.30	100.10
SD	13.40	13.30	14.70	15.20
Sex
Female	48.57%	48.57%	48.57%	48.57%
Male	51.43%	51.43%	51.43%	51.43%
Year
Third year	48.57%	48.57%	51.57%	48.57%
Fifth year	51.43%	51.43%	51.43%	51.43%

Note. DD = children with developmental dyslexia; ADHD = children with attention deficit/hyperactivity disorder; DD + ADHD= comorbid group; TD = typically developing children.

The participants’ inclusion in the groups with ADHD-C and/or with DD was carried out according to the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; APA, 2013) criteria. The inclusion criterion for the ADHD group was to fulfill the ADHD-C (combined type) diagnosis with no reading difficulties. For the DD group, the inclusion criterion was to fulfill the criteria for the dyslexia diagnosis in the absence of ADHD symptoms. The DD + ADHD-C group had to fulfill the criteria for both the ADHD diagnosis and the DD diagnosis, with approximately half of the students with ADHD-C showing comorbidity. The TD group was established by randomly selecting students who did not meet the criteria for the diagnosis of ADHD-C or DD. In all the groups, students with sensory or motor problems were excluded, as well as those who had an intelligence level (IQ) below 80 (measured by the Raven intelligence test, 1996) or those who had special educational needs. The objective was to obtain groups with a similar number of boys and girls and a similar number of students in the third and fifth grades. In the analysis of variance (ANOVA) for age, with Group as between-subject factor, no statistical differences were found between the groups, or in the ANOVA for IQ with the same design.

The groups with and without ADHD were formed taking into consideration the results of the inattention and hyperactivity-impulsivity scales (DSM 5; APA, 2013), and they were confirmed by the Conners-3 scale (Conners, 2008). The multivariate analysis of variance (MANOVA) of the scores on these subscales, with Group (ADHD-C/DD/DD + ADHD-C/TD) as between-subject factor, produced a significant effect of the Group, Wilks’ lambda = .258, F_{(12, 395)} = 19.59, p < .001, $η_{p}^{2}$ = .363. In addition, the confirmatory ANOVA for each subscale separately (see Table 2) indicated that, whether obtained from parents or teachers, the inattention scores were significantly higher in the two groups with ADHD-C (with or without DD) than in the other two groups (TD and DD, both without ADHD-C). Moreover, the hyperactivity and impulsivity scores were higher in the two groups with ADHD-C than in the TD group, thus supporting the correct composition of the groups.

Table 2.

Means, Standard Deviations, and ANOVA Results for the Inattention and Hyperactivity Reading Performance Measures, by Group.

	DDn = 35	ADHDn = 35	DD + ADHDn = 35	TDn = 35	F _{(3, 136)}	$η_{p}^{2}$	Group differences
Inattention (parent)
M	59.90	74.03	70.09	54.34	23.18**	.338	TD <DD, DD + ADHD, ADHD
SD	9.24	10.13	8.675	9.91			DD <DD + ADHD, ADHD
Inattention (teacher)
M	59.23	69.74	73.09	48.23	29.40**	.393	TD <DD, ADHD, DD + ADHD
SD	9.99	6.33	12.56	9.42			DD <ADHD, ADHD + DD
Hyperactivity (parent)
M	59.17	74.23	72.14	57.54	19.90**	.335	TD <DD + ADHD, ADHD
SD	11.88	10.16	10.13	13.73
Hyperactivity (teacher)
M	53.31	78.63	73.60	51.94	62.55**	.580	TD <DD + ADHD, ADHD
SD	11.72	8.53	10.84	9.68
Reading word (accuracy)
M	32.43	37.74	33.91	37.77	22.35**	.330	TD >DD, DD + ADHD
SD	3.18	2.70	4.58	2.77			ADHD >DD, DD + ADHD
Reading pseudoword (accuracy)
M	29.91	33.97	30.83	34.57	20.60**	.324	TD >DD, DD + ADHD
SD	6.62	3.55	4.93	4.51			ADHD >DD, DD + ADHD
Reading word (reading time)
M	115.60	50.37	104.17	57.69	33.12**	.422	TD <DD, DD + ADHD
SD	47.77	16.79	38.04	22.78			ADHD <DD, DD + ADHD
Reading pseudoword (time) (reading time)
M	140.77	71.74	129.06	80.06	39.46**	.465	TD <DD, DD + ADHD
SD	40.37	16.20	40.62	26.39			ADHD <DD, DD + ADHD

Note. ANOVA = analysis of variance; DD = children with developmental dyslexia; ADHD = children with attention deficit/hyperactivity disorder; DD + ADHD = comorbid group; TD = typically developing children.

p < .016—Bonferroni correction of critical p values when performing multiple comparisons.

It should also be taken into consideration that the inattention scores were higher in the DD group than in the TD group, but there were no differences between these two groups in their hyperactivity scores. These results are consistent with other studies (e.g., Berninger, Abbott, Cook, & Nagy, 2017), and they suggest that inattention difficulties can be present when there are reading difficulties.

Reading measures were administered to the subjects in order to determine whether they had dyslexia problems. Measures of accuracy (number of correct answers) and speed (total time reading) were obtained on the word reading test and the pseudo-word reading test from the PROLEC-R (Evaluación de los Procesos Lectores, Revisada), a battery of tests for assessing reading skills in Spanish, (Cuetos, Rodríguez, Ruano, & Arribas, 2007). Both tests are applied individually and have good internal consistency (Cronbach’s alpha for the words test = .74, and for the pseudo-words test = .689). For the word reading test, the participant has to read 40 words, 20 with high frequency of use and 20 with low frequency, composed of five to eight letters each. The pseudo-word reading test also contains 40 stimuli. A child was considered to have difficulties in the decoding of written verbal stimuli if he or she performed 1.5 standard deviations below the norm on both accuracy and speed on these tests. The MANOVA of the scores on these tests, with Group (ADHD/DD/DD + ADHD/TD) as between-subject factor, produced statistically significant results, Wilks’ lambda = .406, F_{(12, 352)} = 11.90, p < .001, $η_{p}^{2}$ = .259. The confirmatory ANOVA carried out separately with each measure (see Table 2) supported the correct composition of the groups in terms of their competence in reading words and pseudo-words.

Procedure

Written consent to carry out this study was obtained from the Coordination Area 6 of Ecuador’s Ministry of Education for municipal education and from the schools’ principals and parents, and the teachers and parents agreed to participate.

The data were obtained from the children, their parents, or their teachers. A team of five clinical psychologists went to each school to perform the interviews and administer the tests. The Conners-3 (Conners, 2008) parents’ version for ADHD detection was administered to the parents. The teachers’ version of the same test was administered to the teachers, as well as an academic information questionnaire and the BRIEF (Behavior Rating Inventory of Executive Function) questionnaire to evaluate EF (Gioia, Isquith, Guy, & Kenworthy, 2000). The parents or teachers were asked to give their ratings considering the child’s behavior when no medication for the disorder was used. In addition, for each student, his or her teacher was asked if the student was very slow or made mistakes when reading. The academic information questionnaire and the questions regarding speed or errors in reading were used to obtain the additional, relevant information we needed to know what children met the criteria for dyslexia, according to DSM-5 (APA, 2013). The following tests were administered to the students who were potentially eligible for the ADHD-C, DD + ADHD-C, or TD groups: the word and pseudo-word reading tests from the PROLEC-R Reading Processes Evaluation Battery, Renewed (Cuetos et al., 2007), the Raven intelligence test (Raven, 1996), with reliability ranging from .65 to .93, and a Spanish test adapted to Ecuador’s population, the Battery of General and Differential Aptitudes for Elementary School students (for those in third grade: BADyG-E2 Renewed, by Yuste & Yuste, 2011; and for those in fifth grade: BADyG-E3 Renewed by Yuste, Yuste, Galvez, & Martínez, 2011).

The tests were administered in a noise-free room in the school. Once the tests had been corrected, the four resulting groups were balanced on number, gender, grade, age, and IQ. In order to give the tests to children who received medication, they suspended medication use 3 days before the test administration.

Measures

Visual attention

This was measured considering the number of correct answers on the difference discrimination test of the BADyG-E2 R (Yuste & Yuste, 2011) for the students in third grade of elementary school, and the BADyG-E3 R for those in fifth grade (Yuste et al., 2011). The difference discrimination test evaluated the ability to compare drawings quickly in order to find a different detail in each of them. The participant receives sheets of paper that contain, ordered from the first to the last, the 24 items on the task. Each item presents three drawings of the same object (e.g., a sock), presented in a row, two of which are identical, whereas the other one has or is missing a detail (e.g., one of the socks is missing a small bow that the other two have at the top right). The participant’s task is to compare the three drawings in the item and mark the one with the different detail. The participant has to give an answer for each item before going on to the next one, and he or she has a limited amount of time to answer as many items as possible (3 min for children in third grade and 5 min for children in fifth grade). All participants finished the test, and there were no omissions. Cronbach’s alpha is .90.

Orthographic word recognition

This is measured considering the number of correct answers on the Orthographic Visual Memory Test of the BADyG-E2 R (Yuste & Yuste, 2011) for the students in third grade and the BADyG-E3 R for those in fifth grade (Yuste et al., 2011). The participant receives sheets of paper that present, organized from the first to the last, the 24 items on the test. Each item presents three nonrelated words, written in capital letters, one of which has a spelling mistake (e.g., “vindow coat hug”). The participant is asked to circle the one written incorrectly (in the previous example, “vindow” should be “window”). The participant has to give an answer for each item before going on to the next one, and he or she has a limited time to answer as many items as possible (6 min for the children in third grade and 5 min for the children in fifth grade). All participants finished the test, and there were no omissions. Cronbach’s alpha is .85.

EF measures

They were obtained from the BRIEF (Gioia et al., 2000). This questionnaire is composed of 86 items, with two versions, one for parents and the other for teachers, designed to evaluate the EF in children and adolescents from 5 to 18 years old. The BRIEF consists of eight subscales: inhibition, shift, emotional control, initiative, working memory, planning-organization, ordering, and monitoring. In this study, we use the results from the teachers’ version, because we were mainly interested in the effects of dyslexia and ADHD-C on some aspects of the cognitive functioning that are particularly relevant in academic performance, such as orthographic recognition, or visual selective attention.

Analysis

The data were analyzed through ANOVA and/or MANOVA, with the Group (ADHD-C/DD/DD + ADHD-C/TD) as between-subject factor. Paired-comparison tests were performed when necessary to determine the groups that presented differences.

Results

Difference discrimination

The ANOVA of the scores on the visual attention test produced statistically significant differences (see Table 3). The analysis of the paired-comparison indicated that the TD group obtained better performance on the test than any of the other three groups, and the DD group obtained better performance than the two groups with ADHD-C (alone or comorbid). There were no other significant differences.

Table 3.

Means, Standard Deviations, and ANOVA Results for the Visual Attention Test and Orthographic Recognition, by Group.

	DDn = 35	ADHDn = 35	DD + ADHDn = 35	TDn = 35	F _{(3, 136)}	$η_{p}^{2}$	Group differences
Visual Attention
M	39.77	24.97	35.91	61.66	44.40**	.495	TD >DD, DD + ADHD, ADHD
SD	9.99	10.13	12.56	19.64			DD >DD + ADHD, ADHD
Orthographic Recognition
M	30.11	38.74	35.86	64.46	12.35**	.214	TD >ADHD, DD + ADHD, DD
SD	15.22	17.83	20.31	16.31

Note. ANOVA = analysis of variance; DD = children with developmental dyslexia; ADHD = children with attention deficit/hyperactivity disorder; DD + ADHD= comorbid group; TD = typically developing children.

p < .016—Bonferroni correction of critical p values when performing multiple comparisons.

Orthographic Recognition

The ANOVA of the scores on the orthographic recognition test produced statistically significant differences (see Table 3). The analysis of the paired-comparisons indicated that the TD group performed better than any of the other groups. There were no other significant differences.

Executive Functioning

The effect of the Group in the MANOVA of the scores obtained with the BRIEF teachers’ version was statistically significant, Wilks’ lambda = .557, F_{(24, 374)} = 4.17, p < .001, $η_{p}^{2}$ = .205. The ANOVA carried out for each measure separately produced the statistically significant differences indicated in Table 4. On all the measures, the children included in the two groups with ADHD-C (alone and comorbid with DD) were given worse scores by the teachers than the children in the TD group. The scores for the DD group were better than those for the ADHD-C group on all measures, and better than the scores for the DD + ADHD-C group on all the measures, except shift. There were no other significant differences (see Table 4).

Table 4.

Means, Standard Deviations, and ANOVA Results for the Executive Function Measures From the BRIEF Teachers Form, by Group.

	DDn = 35	ADHDn = 35	DD + ADHDn = 35	TDn = 35	F _{(3, 136)}	$η_{p}^{2}$	Group differences
INH
M	51.29	73.20	64.31	50.17	26.20**	.366	TD <DD + ADHD, ADHD
SD	9.47	15.98	13.60	10.95			DD <ADHD, DD + ADHD
SH
M	53.80	70.94	61.43	49.34	19.38**	.300	TD <ADHD, DD + ADHD
SD	11.83	14.03	12.60	12.29			DD <ADHD
EC
M	52.97	69.06	62.69	49.09	21.61**	.323	TD <DD + ADHD, ADHD
SD	10.91	13.43	12.55	8.82			DD <DD + ADHD, ADHD
INI
M	53.43	67.37	63.63	49.14	20.02**	.306	TD <DD + ADHD, ADHD
SD	11.05	11.63	11.64	10.78			DD <DD + ADHD, ADHD
WM
M	55.37	70.69	66.43	49.14	19.81**	.304	TD <DD + ADHD, ADHD
SD	14.47	12.65	13.67	12.59			DD <DD + ADHD, ADHD
PL
M	55.15	70.43	62.86	48.97	22.95**	.228	TD <DD + ADHD, ADHD
SD	11.63	12.65	13.68	10.27			DD <DD + ADHD, ADHD
OM
M	54.06	73.89	66.66	54.06	13.88**	.208	TD <DD + ADHD, ADHD
SD	14.71	17.98	14.93	14.71			DD <DD + ADHD, ADHD
MO
M	53.54	72.40	64.94	49.17	87.37**	.233	TD <DD + ADHD, ADHD
SD	12.86	14.35	11.55	9.37			DD <DD + ADHD, ADHD

Note. ANOVA = analysis of variance; BRIEF = Behavior Rating Inventory of Executive Function; DD = children with developmental dyslexia; ADHD = children with attention deficit/hyperactivity disorder; DD + ADHD= comorbid group; TD = typically developing children; INH = inhibition; SH = shift; EC = emotional control; INI = initiative; WM = working memory; PL = planning; OM = organization of materials; MO = monitor.

p < .016—Bonferroni correction of critical p values when performing multiple comparisons.

Discussion

The groups with DD and/or with ADHD-C performed worse than the TD group on both the difference discrimination test and the orthographic recognition test. In the case of the difference discrimination test, these results show that both DD and ADHD-C can present deficits in the most basic attentional functions of orientation and selection of visual information. The differences between the groups with DD and the TD group in difference discrimination are consistent with results obtained in other studies (e.g., Franceschini et al., 2018; Gori et al., 2014; Horowitz-Kraus, 2017; Stein, 2018), and they suggest that, at the least, reading difficulties tend to co-occur with difficulties in selective attention, which can affect tasks and activities other than reading. Regarding the differences between the groups with ADHD-C and the TD group in difference discrimination, the results show that selective attention can also be affected in ADHD-C, coinciding with the results of studies such as Tsal et al. (2005), which also found worse performance on orientation and/or visual attention selection tasks in children with ADHD-C than in children without ADHD-C, unlike the null results obtained in other studies (e.g., Booth et al., 2005; McAvinue et al., 2015).

Furthermore, the performance on the difference discrimination test in the two groups with ADHD-C was not only worse than the TD group, but it was also worse than the DD group. However, considering that the instructions for the difference discrimination test were to answer as many items as possible in a limited amount of time, it cannot be ruled out that the differences between the groups with and without ADHD-C on the figure difference discrimination test would be due to EF differences between these groups. The teachers of the children with ADHD-C estimated that the EF of the children with ADHD-C, alone or with DD, was worse than that of the children with TD and the DD group. To explore the role of executive dysfunction on performance across the differences in the discrimination task, we conducted analyses of variance of the score in this task with Group as a between-subjects factor and the score on one of the EF subscales as a control variable (Analysis of Covariance [ANCOVA]). The groups with ADHD-C only, DD only, or both disorders were still worse than the controls despite controlling for the EF measures. As regard to the differences among the groups with either DD or ADHD-C, we could appreciate some differences. If EF measures are not considered, the group with only DD showed an advantage in this task over the two groups with ADHD-C.

However, the group with ADHD-C only performed the visual attention task worse than the DD group when controlling for the measure of emotional control, initiative, working memory, planning-organization, ordering, or monitoring, and also worse than the comorbid group when controlling for initiative, working memory, or planning-organization. Thus, these results support that EF did have a role on the performance of the visual attention task in the group with only ADHD-C, especially inhibition and shifting.

Regarding orthographic word recognition, our results suggest that both children with DD and children with ADHD face obstacles in this regard. The groups with DD, with or without ADHD, showed worse performance on this test than the TD group, as expected, considering that DD can directly affect the storage of orthographic representations (Suárez-Coalla, Villanueva, González-Pumariega, & González-Nosti, 2016). More interestingly, the group with only ADHD-C also showed worse performance than the TD group on the orthographic word recognition task. This result is consistent with that obtained in the study conducted by Adi-Japha et al. (2007) in Hebrew normally reading children with and without ADHD-C type, showing that the children with ADHD-C committed more spelling errors on words from dictation. Accordingly to Adi-Japha et al. (2007), children with ADHD-C and normal reading skills commit more spelling errors in writing because of nonlinguistic, attentional deficits, so it might be that such a type of deficits were behind the worse performance of the group with only ADHD-C on our orthographic word recognition task, as compared to controls.

Note also that the group with only ADHD-C performed on the orthographic word recognition task at a level similar than the group with only DD. We must admit that our expectation was rather that the groups with DD would perform on this task worse than the group with only ADHD-C. Therefore, accordingly to literature, and considering our own results regarding EF, we can speculate that different causes (reading vs. attentional difficulties) produced a similar performance on this task in the two groups. However, further research is needed to examine deeper this issue. For example, as a reviewer suggested, it might be that the children with dyslexia only and ADHD-C only do differ in the speed at which they do this kind of task, which we did not record. In any case, we also checked that this pattern of results concerning the orthographic word recognition task did not vary if the scores on one of the EF subscales were also considered in the analysis, as a control variable (ANCOVAs), that is, the groups with ADHD-C only, DD only, or both disorders were still worse than the controls despite controlling for the EF measures, and as regard to the differences among the groups with either DD or ADHD-C, no differences between the results from the ANOVA (without controlling for EF measures) and ANCOVAs were observed.

As for the comorbid group, we found that they also showed worse performance on the word recognition task than the TD group, which is consistent with the results from studies using participants with ADHD-C without considering reading level in the participants’ selection, using a task similar to ours (Johnels et al., 2014), or a dictation task (Lúcio et al., 2017). Finally, we did not find significant differences in our orthographic word recognition task between the comorbid group and the group with only DD, nor between the comorbid group and the group with only ADHD-C, which do not support the idea that children with dyslexia and ADHD-C symptoms present deficits more severe than the children suffering from only dyslexia or only ADHD-C.

As we expected, the children from the ADHD-C and DD + ADHD-C groups obtained worse ratings than the TD group on all the assessed aspects of EF: inhibition, shift, emotional control, initiative, working memory, organization-planning, order, and monitoring. By contrast, the children in the DD group not only received significantly better assessments than either of the two groups with ADHD-C (alone or with DD) on all the aspects evaluated—with the only exception of shift when comparing DD and DD + ADHD, where the differences were not significant—but they also received similar evaluations to those of the TD groups. Therefore, overall, these results support the dissociation between DD and ADHD-C in EF in children in the age group studied (8-10 years old), which is consistent with the results of some previous studies (e.g., Landerl et al., 2013; Purvis & Tannock, 2000), and diverge from studies indicating that children affected by DD, but not ADHD-C, also present certain EF deficits (Moura et al., 2015; Reiter et al., 2005), and from other results supporting the hypothesis of worse EF in children with ADHD-C than in children with DD only in some particular aspect (e.g., Marzocchi et al., 2008).

However, regarding the question of whether DD + ADHD-C implies worse or different deficits from those of DD or ADHD-C separately, our results generally do not support this. Certainly, compared with the DD group, the DD + ADHD-C group obtained worse teacher assessments of all the EF skills, except shift, and the DD + ADHD-C group also obtained worse scores than the DD group on the figure difference discrimination test. By contrast, the DD + ADHD-C group obtained similar results to the ADHD group on EF, as well as on figure difference discrimination and orthographic recognition. We observed some data trends suggesting that the comorbid group performed better on the visual attention task and obtained better EF ratings from teachers than the group with only ADHD-C, though such differences were not supported by the paired-comparison tests. If further research supported some significant advantages for individuals with comorbid symptoms in these aspects, it would be worthy to explore if such differences are related to differences in the severity of the ADHD-C symptoms between the comorbid group and the ADHD-C group. Note that in our study there were also some trends in the inattention and hyperactivity/impulsivity scores from the Conners-3 scale, suggesting that the children in the ADHD-C group suffered from more intense symptoms of ADHD-C than children in the DD + ADHD-C group, though these differences did not reach the significance level either.

Certainly, compared with the DD group, the DD + ADHD-C group obtained worse teacher assessments of all the EF skills, except shift, and the DD + ADHD-C group also obtained worse scores than the DD group on the figure difference discrimination test. By contrast, though there seemed to be some differences in EF scores between the DD + ADHD-C group and the group with only ADHD-C, suggesting that the teachers rated EF in the comorbid group better, the differences did not reach the significance level. In addition, the DD + ADHD-C group also obtained similar results to the ADHD-C group on figure difference discrimination and orthographic recognition. Therefore, our results suggest that DD + ADHD-C is worse than DD, but it is not worse than ADHD-C. However, this conclusion is obviously limited to the case of EF, selective visual attention, and orthographic recognition. Regarding EF, the differences between the DD + ADHD-C group and the DD group (in favor of the DD group), and between the ADHD-C and DD groups (in favor of the DD group), along with the lack of significant differences between the DD + ADHD-C group and the ADHD-C group, and between the DD and TD groups, suggest that the EF deficits in the comorbid group would be closely linked to ADHD-C, supporting the dissociation between DD and ADHD-C in EF, as mentioned above.

Regarding difference discrimination in figures, the final composition was different. As pointed out above, the groups with DD and/or ADHD-C obtained worse results than the TD group, suggesting that both DD and ADHD-C involve selective visual attention deficits, and the ADHD-C group had worse results than the DD group, suggesting that these deficits could be even worse in ADHD-C than in DD. However, we should recall that, in our opinion, the EF deficits found in the groups with ADHD-C could also contribute to these results. Nonetheless, the DD + ADHD-C group had worse results on this test than the DD group, but there were no differences between the comorbid group and the ADHD-C group. Therefore, we do not find support for the hypothesis that the effects of these two disorders are additive, at least in the case of difference discrimination in figures. In addition, the results of the DD + ADHD-C group on the orthographic recognition test were similar to those of the DD and ADHD-C groups. Overall, these results agree with those from other studies showing that deficiencies present in DD + ADHD-C are not always more severe than those found in samples affected by only one of these disorders, for example, in the case of phonological awareness, rapid naming of visual stimuli, or short-term visuospatial memory (Moura et al., 2017), supporting the idea that there are common factors in DD and ADHD-C.

In conclusion, the relationship between the different groups under study is defined based on difference discrimination, orthographic recognition, and EF. Whereas the DD + ADHD-C group does not present significant differences from the ADHD-C group in any of the analyzed aspects, it can be observed that the results of the DD group are more favorable than those of the ADHD-C group and the comorbid group, both for EF and selective attention. The three clinical groups obtained worse results than the TD group on orthographic recognition. Therefore, DD + ADHD-C involves worse deficits compared to DD, but not compared to ADHD-C.

The contribution of this study must be viewed with a series of limitations in mind. Although the number of participants is sufficient to represent a clinical sample, the results were obtained using samples of children from 7 to 9 years old. Therefore, it would be advisable to replicate the findings, broadening the age range of the sample at least to students from 12 to 16 years old. In addition, although various factors such as attention and memory were assessed in children, other factors such as shift, planning, or working memory were only considered from the teacher’s point of view. Future studies should also broaden this perspective to include emotional regulation, which can be considered a behavioral manifestation of EF ability. Another limitation for the generalizability of the results from the present study is that our ADHD-C groups only had children with the combined type of ADHD-C, and not children with other types of ADHD-C, that is, hyperactive-impulsive type, or inattentive type, as in most research in the field. Further research is needed to test whether the results would vary with the ADHD-C type.

The results from this study suggest that formal, early interventions in the school context are needed to prevent the effects of these disorders in academic performance. These abilities can be also learned, and we think that the school context is the optimal context for these purposes (e.g., for a review of studies on interventions to foster children’s EFs, see Takacs & Kassai, 2019). In addition, teachers should be provided with the adequate resources to detect deficiencies in cognitive processing, such as visual attention, orthographic knowledge, or EFs in children.

Footnotes

Author’s Note

Dr. Inmaculada Fernández-Andrés deceased on October 29, 2019.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Senescyt (Ecuador): predoctoral No 018-CIBAE.

ORCID iDs

M. Inmaculada Fernández-Andrés

Pilar Tejero

Author Biographies

M. Inmaculada Fernández-Andrés, PhD, is an associate professor at the University of Valencia in the department of developmental and educational psychology. She is a principal investigator of research programs of children with ADHD and ASD. She research focuses on autism spectrum disorders, ADHD and learning disabilities.

Pilar Tejero, PhD, is an associate professor at the University of Valencia (Spain), in the Psicología Básica department. She joined as a researcher to the ERI Lectura (Interdisciplinary Research Structure on Reading) in 2012, where she has been studying perception in reading, and also the difficulties of individuals with dyslexia in attention-demanding tasks.

Ximena Vélez-Calvo, PhD, is an associate professor at the University of Azuay (Ecuador). Her research focuses on cognitive neuroscience and learning disabilities and ADHD.

References

Adi-Japha

Landau

Y. E.

Frenkel

Teicher

Gross-Tsur

Shalev

R. S.

(2007). ADHD and dysgraphia: Underlying mechanisms. Cortex, 43, 700-709.

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing.

Berninger

Abbott

Cook

C. R.

Nagy

(2017). Relationships of attention and executive functions to oral language, reading, and writing skills and systems in middle childhood and early adolescence. Journal of Learning Disabilities, 50, 434-449.

Booth

J. R.

Burman

D. D.

Meyer

J. R.

Lei

Trommer

B. L.

Davenport

N. D.

. . . Marsel Mesulam

(2005). Larger deficits in brain networks for response inhibition than for visual selective attention in attention deficit hyperactivity disorder (ADHD). Journal of Child Psychology and Psychiatry, 46, 94-111.

Bosse

M. L.

Tainturier

M. J.

Valdois

(2007). Developmental dyslexia: The visual attention span deficit hypothesis. Cognition, 104, 198-230.

Carroll

J. M.

Solity

Shapiro

L. R.

(2016). Predicting dyslexia using prereading skills: The role of sensorimotor and cognitive abilities. Journal of Child Psychology and Psychiatry, 57, 750-758.

Collis

N. L.

Kohnen

Kinoshita

(2013). The role of visual spatial attention in adult developmental dyslexia. The Quarterly Journal of Experimental Psychology, 66, 245-260.

Colomer

Berenguer

Roselló

Baixauli

Miranda

(2017). The impact of inattention, hyperactivity/impulsivity symptoms, and executive functions on learning behaviors of children with ADHD. Frontiers in Psychology, 8, Article 540.

Conners

C. K.

(2008). Conners (3rd ed.). Toronto, Ontario, Canada: Multi-Health Systems.

10.

Cuetos

Rodríguez

Ruano

Arribas

(2007). PROLEC-R. Evaluación de los procesos lectores - Revisada (Battery of tests for assessing reading skills - Renewed). Madrid, Spain: TEA Editions.

11.

de Jong

C. G. W.

Van De Voorde

Roeyers

Raymaekers

Oosterlaan

Sergeant

J. A

. (2009). How distinctive are ADHD and RD? Results of a double dissociation study. Journal of Abnormal Child Psychology, 37, 1007-1017.

12.

DuPaul

G. J.

Gormley

M. J.

Laracy

S. D.

(2013). Comorbidity of LD and ADHD: Implications of DSM-5 for assessment and treatment. Journal of Learning Disabilities, 46, 43-51.

13.

Facoetti

Paganoni

Turatto

Marzola

Mascetti

G. G.

(2000). Visual-spatial attention in developmental dyslexia. Cortex, 36, 109-123.

14.

Facoetti

Trussardi

A. N.

Ruffino

Lorusso

M. L.

Cattaneo

Galli

. . . Zorzi

(2010). Multisensory spatial attention deficits are predictive of phonological decoding skills in developmental dyslexia. Journal of Cognitive Neuroscience, 22, 1011-1025.

15.

Faraone

S. V.

Sergeant

Gillberg

Biederman

(2003). The worldwide prevalence of ADHD: Is it an American condition? World Psychiatry, 2, 104-113.

16.

Franceschini

Mascheretti

Bertoni

Trezzi

Andreola

Gori

Facoetti

(2018). Sluggish dorsally-driven inhibition of return during orthographic processing in adults with dyslexia. Brain and Language, 179, 1-10.

17.

Fried

Petty

Faraone

S. V.

Hyder

L. L.

Day

Biederman

(2016). Is ADHD a risk factor for high school dropout? A controlled study. Journal of Attention Disorders, 20, 383-389.

18.

Friedman-Hill

S. R.

Wagman

M. R.

Gex

S. E.

Pine

D. S.

Leibenluft

Ungerleider

L. G.

(2010). What does distractibility in ADHD reveal about mechanisms for top-down attentional control? Cognition, 115, 93-103.

19.

Gioia

G. A.

Isquith

P. K.

Guy

S. C.

Kenworthy

(2000). The Behavior Rating Inventory of Executive Function. Odessa, FL: Psychological Assessment Resources.

20.

Gooch

Snowling

Hulme

(2011). Time perception, phonological skills and executive function in children with dyslexia and/or ADHD symptoms. Journal of Child Psychology and Psychiatry, 52, 195-203.

21.

Gori

Cecchini

Bigoni

Molteni

Facoetti

(2014). Magnocellular-dorsal pathway and sub-lexical route in developmental dyslexia. Frontiers in Human Neuroscience, 8, Article 460.

22.

Grainger

Dufau

Ziegler

J. C.

(2016). A vision of reading. Trends in Cognitive Neuroscience, 20, 171-179.

23.

Hari

Renvall

(2001). Impaired processing of rapid stimulus sequences in dyslexia. Trends in Cognitive Sciences, 5, 525-532.

24.

Holmes

Gathercole

S. E.

Place

Alloway

T. P.

Elliott

J. G.

Hilton

K. A.

(2010). Assessments in the identification of ADHD in children. Child and Adolescent Mental Health, 15, 37-43.

25.

Horowitz-Kraus

(2017). Familial history of reading difficulty is associated with diffused bilateral brain activation during reading and greater association with visual attention abilities. Annals of Dyslexia, 67, 281-298.

26.

Jiménez

J. E.

Guzmán

Rodríguez

Artiles

(2009). Prevalence of specific learning difficulties: Dyslexia in Spanish. Anales de Psicología, 25, 78-85.

27.

Johnels

J. A.

Kopp

Gillberg

(2014). Spelling difficulties in school-aged girls with attention-deficit/hyperactivity disorder: Behavioral, psycholinguistic, cognitive, and graphomotor correlates. Journal of Learning Disabilities, 47, 424-434.

28.

Judge

Knox

P. C.

Caravolas

(2013). Spatial orienting of attention in dyslexic adults using directional and alphabetic cues. Dyslexia, 19, 55-75.

29.

Kofler

M. J.

Irwin

L. N.

Soto

E. F.

Groves

N. B.

Harmon

S. L.

Sarver

D. E.

(2018). Executive functioning heterogeneity in pediatric ADHD. Journal of Abnormal Child Psychology, 28, 1-14.

30.

Landerl

Ramus

Moll

Lyytinen

Leppänen

P. H.

Lohvansuu

. . . Kunze

(2013). Predictors of developmental dyslexia in European orthographies with varying complexity. Journal of Child Psychology and Psychiatry, 54, 686-694.

31.

Lúcio

P. S.

Salum

Swardfager

Mari

J. D. J.

Pan

P. M.

Bressan

R. A.

. . . Cogo-Moreira

(2017). Testing measurement invariance across groups of children with and without attention-deficit/hyperactivity disorder: Applications for Word recognition and spelling tasks. Frontiers in Psychology, 8, Article 1891.

32.

Lukov

Friedmann

Shalev

Khentov-Kraus

Shalev

Lorber

Guggenheim

(2015). Dissociations between developmental dyslexias and attention deficits. Frontiers in Psychology, 5, Article 882.

33.

Maehler

Schuchardt

(2016). Working memory in children with specific learning disorders and/or attention deficits. Learning and Individual Differences, 49, 341-347.

34.

Marzocchi

G. M.

Oosterlaan

Zuddas

Cavolina

Geurts

Redigolo

. . . Sergeant

J. A.

(2008). Contrasting deficits on executive functions between ADHD and reading disabled children. Journal of Child Psychology and Psychiatry, 49, 543-552.

35.

Mason

D. J.

Humphreys

G. W.

Kent

(2005). Insights into the control of attentional set in ADHD using the attentional blink paradigm. Journal of Child Psychology & Psychiatry, 46, 1345-1353.

36.

Mason

D. J.

Humphreys

G. W.

Kent

L. S.

(2003). Exploring selective attention in ADHD: Visual search through space and time. Journal of Child Psychology and Psychiatry, 44, 1158-1176.

37.

Mayes

S. D.

Calhoun

S. L.

Crowell

E. W.

(2000). Learning disabilities and ADHD: Overlapping spectrum disorders. Journal of Learning Disabilities, 33, 417-424.

38.

McAvinue

L. P.

Vangkilde

Johnson

K. A.

Habekost

Kyllingsbæk

Bundesen

Robertson

I. H.

(2015). A componential analysis of visual attention in children with ADHD. Journal of Attention Disorders, 19, 882-894.

39.

Moura

Pereira

Alfaiate

Fernandes

Nogueira

. . . Simões

M. R.

(2017). Neurocognitive functioning in children with developmental dyslexia and attention-deficit/hyperactivity disorder: Multiple deficits and diagnostic accuracy. Journal of Clinical and Experimental Neuropsychology, 39, 296-312.

40.

Moura

Simões

M. R.

Pereira

(2015). Executive functioning in children with developmental dyslexia. The Clinical Neuropsychologist, 28, 120-141.

41.

Pennington

B. F.

(2006). From single to multiple deficit models of developmental disorders. Cognition, 101, 385-413.

42.

Pennington

B. F.

Ozonoff

(1996). Executive functions and developmental psychopathology. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 37, 51-87.

43.

Peterson

R. L.

Pennington

B. F.

(2015). Developmental dyslexia. Annual Review of Clinical Psychology, 11, 283-307.

44.

Pievsky

M. A.

McGrath

R. E.

(2018). The neurocognitive profile of attention-deficit/hyperactivity disorder: A review of meta-analyses. Archives of Clinical Neuropsychology, 33, 143-157.

45.

Polanczyk

G. V.

Willcutt

E. G.

Salum

G. A.

Kieling

Rohde

L. A.

(2014). ADHD prevalence estimates across three decades: An updated systematic review and meta-regression analysis. International Journal of Epidemiology, 43, 434-442.

46.

Posner

M. I.

Rothbart

M. K.

(2007). Research on attention networks as a model for the integration of psychological science. Annual Review of Psychology, 58, 1-23.

47.

Pugh

Verhoeven

(2018). Introduction to this special issue: Dyslexia across languages and writing systems. Scientific Studies of Reading, 22, 1-6.

48.

Purvis

K. L.

Tannock

(2000). Phonological processing, not inhibitory control, differentiates ADHD and reading disability. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 485-494.

49.

Ramos Galarza

C. A.

Bolaños Pasquel

M. F.

Ramos Galarza

D. A

. (2015). Prevalence of attention deficit hyperactivity disorder in Ecuadorian students. Revista Científica y Tecnológica UPSE, III, 13-19.

50.

Raven

(1996). Standard Progressive Matrices. Madrid: Paidos

51.

Reijnen

Opwis

(2008). Visual search in children with ADHD: The influence of feedback on selective attention. Journal of Vision, 8, 774.

52.

Reiter

Tucha

Lange

K. W.

(2005). Executive functions in children with dyslexia. Dyslexia, 11, 116-131.

53.

Roberts

B. A.

Martel

M. M.

Nigg

J. T.

(2017). Are there executive dysfunction subtypes within ADHD? Journal of Attention Disorders, 21, 284-293.

54.

Rubia

(2018). Cognitive neuroscience of attention deficit hyperactivity disorder (ADHD) and its clinical translation. Frontiers in Human Neuroscience, 12, Article 100.

55.

Sexton

C. C.

Gelhorn

H. L.

Bell

J. A.

Classi

P. M.

(2012). The co-occurrence of reading disorder and ADHD: Epidemiology, treatment, psychosocial impact, and economic burden. Journal of Learning Disabilities, 45, 538-564.

56.

Stein

(2018). What is developmental dyslexia? Brain Sciences, 8(2), 26.

57.

Suárez-Coalla

Villanueva

González-Pumariega

González-Nosti

(2016). Spelling difficulties in Spanish-speaking children with dyslexia. Infancia y Aprendizaje, 39(2), 275-311.

58.

Takacs

Z. K.

Kassai

(2019). The efficacy of different interventions to foster children’s executive function skills: A series of meta-analyses. Psychological Bulletin, 145, 653-697. doi:10.1037/bul0000195

59.

Tsal

Shalev

Mevorach

(2005). The diversity of attention deficits in ADHD: The prevalence of four cognitive factors in ADHD versus controls. Journal of Learning Disabilities, 38, 142-157.

60.

Vidyasagar

T. R.

Pammer

(2010). Dyslexia: A deficit in visuo-spatial attention, not in phonological processing. Trends in Cognitive Sciences, 14, 57-63.

61.

Wilding

(2005). Is attention impaired in ADHD? British Journal of Developmental Psychology, 23, 487-505.

62.

Willcutt

E. G.

Doyle

A. E.

Nigg

J. T.

Faraone

S. V.

Pennington

B. F.

(2005). Validity of the EF theory of ADHD. Biological Psychiatry, 57, 1336-1346.

63.

Willcutt

E. G.

Pennington

B. F.

Boada

Ogline

J. S.

Tunick

R. A.

Chhabildas

(2001). A comparison of the cognitive deficits in reading disability and attention-deficit/hyperactivity disorder. Journal of Abnormal Psychology, 110, 157-172.

64.

Yuste

(2011). BADyG-E2, Battery of General and Differential Aptitudes for Elementary School students, E2, Renewed. Madrid, Spain: CEPE Editorial.

65.

Yuste

Galvez

J. L.

Martínez

(2011). BADyG-E3, Battery of General and Differential Aptitudes for Elementary School students, E3, Renewed. Madrid, Spain: CEPE Editorial.