The Role of Parental Education in the Relation Between ADHD Symptoms and Executive Functions in Children

Abstract

Objective: Using a population-based sample of 9-year-old children, this study examined whether the relation between symptoms of ADHD and executive functions (EFs) depended on socioeconomic status (SES; indexed by parental education). Method: Parents and teachers rated the children’s ADHD symptoms, and parents also indicated their educational level in a questionnaire. The children performed a comprehensive set of EF tasks. Results: Whereas working memory was similarly related to ADHD symptoms in the lower and higher parental education group, the relations of inhibition and mental set-shifting with ADHD symptoms were generally stronger in the higher educational group, a pattern that was supported by several significant group differences in correlations. Conclusion: This suggests that the EF pathway in contemporary multiple pathway models of ADHD etiology may be particularly relevant in higher SES parts of the population.

Keywords

executive function ADHD socioeconomic status parental education

One of the most common neuropsychological correlates of ADHD symptoms (i.e., inattention, impulsivity, and hyperactivity) is poor executive functioning (EF; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005), defined as poorer ability for goal-directed regulation of thought and action (Diamond, 2006). Key EFs include working memory (to hold information active in mind and mentally work with it to guide behavior), inhibition (the ability to override a habitual but incorrect response), and mental set-shifting (to flexibly switch between tasks or mental sets; Miyake et al., 2000). Although associations between ADHD symptoms and EFs are commonly found in the literature (e.g., Seidman, 2006; Willcutt et al., 2005), they are usually only moderate in strength, and contemporary views on ADHD etiology emphasize that not all children with this condition show poor EF (e.g., Nigg, Willcutt, Doyle, & Sonuga-Barke, 2005; Sonuga-Barke, 2005). This implicates that there could be moderating variables influencing the ADHD symptom-EF link.

One variable that is often used as a covariate in the literature on both ADHD-related behaviors and EF (as well as in many other areas) is socioeconomic status (SES). In part, SES is commonly indexed not only by educational attainment but also by occupation and income (e.g., the composite Hollingshead SES index; Hollingshead, 1975; also see Bradely & Corwyn, 2002, for a review). Covarying SES controls for the fact that ratings of ADHD symptoms are generally higher (Pineda et al., 1999) and EF is generally poorer (e.g., Hackman & Farah, 2009) in groups of lower SES. However, only a few studies have examined SES as a potential moderator of the effect of either ADHD symptoms or EF on outcomes (Deater-Deckard, Chen, Wang, & Bell, 2012; Rieppi et al., 2002). The moderating role of SES in the relation between ADHD symptoms and EF has not previously been studied.

Two alternative predictions could be derived based on the following line of reasoning. Both ADHD symptoms and EF show strong heritability (Chang, Lichtenstein, Asherson, & Larsson, 2013; Faraone et al., 2005; Friedman et al., 2008), and EF has been suggested as endophenotype for ADHD (e.g., Doyle et al., 2005; Gau & Shang, 2010), indicating that they share important genetic factors. Further support for genetic factors underlying the relation between ADHD and EF comes from studies showing that ADHD children with EF deficits had a much higher rate of familial ADHD than ADHD children without EF deficits (Crosbie & Schachar, 2001; Seidman et al., 1995). Importantly, although heritability of ADHD is generally found to be high, theories of gene–environment interactions suggest that the degree of heritability may depend on whether advantageous or disadvantageous environments are considered (see Pennington et al., 2009). Based on these ideas combined, the relation between ADHD and EF should hypothetically be stronger in contexts where the heritability of ADHD is higher.

On one hand, it could be argued that the heritability of ADHD symptoms is higher in samples with lower SES because the environmental stress associated with low SES activates relevant genetic predispositions. This idea is consistent with diathesis-stress models of gene–environment interactions, suggesting that a genetic vulnerability is expressed in behavioral symptoms especially when coupled with environmental stress (Rende & Plomin, 1992). This argument would thus lead to the prediction that the relation between ADHD symptoms and EF is stronger in samples with lower SES.

On the other hand, it could be reasoned that in high-SES contexts genetic influences to ADHD symptoms are more likely to “stand out,” that is, constitute a larger portion of the entire individual variation, because the variability in environmental risks may be restricted in such contexts. Low-SES contexts are more likely to involve a larger range of risk factors, and the diversity of environmental risks could potentially cloud the importance of more biologically inherent influences (Bronfenbrenner & Ceci, 1994). According to this line of reasoning, heritability should thus be higher in high-SES conditions (Rutter, Moffitt, & Caspi, 2006). These ideas are in line with bioecological models of gene–environment interactions, suggesting that enriched environments will enable underlying genetic differences to be actualized, whereas risk environments will mask the genetic differences (Bronfenbrenner & Ceci, 1994). Thus, although levels of ADHD symptoms are generally lower in enriched contexts (Pineda et al., 1999), when the symptoms do occur they are more likely to be biologically based because there are fewer potential environmental risks than in low-SES contexts. Based on the combined notions of high heritability of EF (Friedman et al., 2008) and a shared genetic predisposition for poor EF and high ADHD symptoms (Doyle et al., 2005; Gau & Shang, 2010), this argument thus predicts that the ADHD–EF relation should be stronger in higher SES contexts.

The present study contrasted these two predictions by investigating the role of parental education (as a proxy for SES) in the relation between ADHD and EF using a sample of 9-year-olds from the general population. Contemporary theories of ADHD, which have been supported empirically, view the disorder from a dimensional perspective, meaning that the symptoms and their neuropsychological bases lie in the extreme end of a normality continuum (e.g., Barkley, 1997; Larsson, Anckarsater, Rastam, Chang, & Lichtenstein, 2012; Levy, Hay, McStephen, & Wood, 1997). This perspective implicates that individual variation in ADHD symptoms in the general population could be valuable in studying issues related to the ADHD condition.

Method

Participants

One-hundred sixty-three 9-year-old children all attending regular school classes participated in the present study (50% girls). These children constituted the final sample in a recruitment process, in which 1,000 randomly selected parents of children born in 2002 and currently residing in or around a university town in Sweden were initially contacted via mail. Out of the 301 parents who responded positively (407 responded in total) to participation in a screening phase, the current sample was selected based on parent ratings of aggressive behaviors, in line with the purpose of a larger project. Because the sample was relatively well-functioning, with the majority of children having low scores, the selection was constituted by excluding a proportion of the children with the lowest aggression scores, and inviting the rest to continue participating in the study. This selection thus ensured maximization of the extant variability in the screened sample. Because aggressive behaviors correlated strongly with ADHD symptoms (rs > .50), this selection procedure should also have increased the variability in ADHD symptoms in this population-based sample of children. For five children (3%), the parents reported an ADHD-diagnosis in an open question about potential diagnoses of the child.

Measures and Procedures

Parental education

SES was indexed by parental education level. Educational level of the mothers’ and fathers’ was reported in a questionnaire with the five alternatives: mandatory school (9 years or less), 1-2 year vocational training, 2-4 year theoretical education on high school level, shorter post-high school education (not college/university), or college/university education. Educational level was generally high (as expected in the university town where the participants were recruited), with 70% of the mothers and 53% of the fathers having a college or university education. In 48% of the families, both mothers and fathers had such an education. For the analyses, the higher parental education group was constituted by the children whose both parents had a college/university degree (n = 78), whereas the rest constituted the lower parental education group (n = 85).

ADHD symptoms

Parents and teachers filled out the ADHD Rating Scale–IV (DuPaul, Power, Anastopoulos, & Reid, 1998), containing the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) items for ADHD scored from 0 to 3. Internal consistency of the inattention scale and hyperactivity/impulsivity scale was α = .90 and α = .89, respectively, for parents, and α = .95 and α = .96, respectively, for teachers. The mean score across items was used as the dependent measure. Both scales were strongly correlated between parents and teachers (rs = .62 and .63) and were averaged to form two composite variables of inattention and hyperactivity/impulsivity symptoms. Teacher data from 14 children were missing, and ADHD symptoms for these children were only assessed by parent reports.

EF tasks

In the test session, lasting around 90 min, children performed a comprehensive set of EF tasks in a private room at their respective schools. Working memory was measured by a composite score derived from a child-adapted version of the Keep Track task (Miyake et al., 2000), the Counting Span task (Case, Kurland, & Goldberg, 1982), and a version of the Children’s Size Ordering Task (McInerney, Hramok, & Kerns, 2005). In the Keep Track task, participants were to continuously update their memory representations according to which item from designated categories was presented last in a series of pictures. In the Counting Span task, the children were to remember the count totals from arrays of red circles, while at the same time counting aloud subsequent arrays. In the Children’s Size Ordering Task the children were to remember series of object-words (e.g., “cat-pen-train”) and repeat them in their order of size (“pen-cat-train”). The three working memory tasks were significantly correlated (rs = .32-.41; Cronbach’s α = .63) and were averaged after standardization, with higher scores indicating better performance.

Inhibition was measured by a composite score derived from a numerical version (see, for example, Kaufmann et al., 2005) of the Stroop task (Stroop, 1935) and a child-modified version of the Antisaccade task (Miyake et al., 2000). In the numerical Stroop task, the children were to indicate which of two digits was larger in value. On congruent trials, the digit of larger value was also larger in size, and on incongruent trials the digit that was larger in value was smaller in size, creating a conflict. In the Antisaccade task, the participants were to inhibit the reactive saccade to a small black square suddenly appearing either to the left or right on a computer screen, and instead look to the opposite side and indicate in which direction an arrow was pointing. The two inhibition tasks were significantly correlated (r = .20)¹ and were averaged after standardization, with higher scores indicating better performance.

Mental set-shifting was measured by a composite score derived from a version of the Trails Making task (McLean & Hitch, 1999) and a version of the Global Local task (Miyake et al., 2000). In the first condition on the Trails Making task, the participants were to connect numbers in numerical order by drawing lines, whereas in the second condition they were to alternate connecting numbers in numerical order and letters in alphabetical order (i.e., 1-A-2-B-3-C). In the first block of the Global Local task, the participants were to respond to whether the global (large) feature of a geometrical shape was a square or circle, regardless of the local (small) shapes making up the larger one. In the second block, they were to respond to the local feature of the shape. On every second trial in the third block, the participants were to alternate between responding to the local and global features (i.e., “global,” “global,” “local,” “local,” etc.). The two shifting tasks were significantly correlated (r = .20)¹ and were averaged after standardization, with higher scores indicating better performance.

Statistical Analyses

To study whether the strength of relation between ADHD and EF depended on the SES of participants, we first conducted bivariate correlations separately for the lower and higher parental education group. To statistically test potential group differences in correlation coefficients, r-to-z transformed coefficients were entered into the formula from Cohen and Cohen (1983). In addition, we also examined whether significant group differences in correlations survived in the more conservative method of testing statistical moderation using multiple regression. The models were tested separately for each of the three EFs, with inattention and hyperactivity/impulsivity, respectively, as outcome variables. Following recommendations by Aiken and West (1996), in each model, the EF was centered and entered together with the centered parental education group variable and an interaction term crossing these two variables.

Results

Preparatory Analyses

Univariate outliers, defined as values deviating more than 3 SD from the mean were replaced by the value representing 3 SD above/below the mean. All variables met standard criteria for univariate normality (Kline, 2005), with skewness < 1.91 and kurtosis < 3.26. Table 1 shows that although levels of ADHD symptoms in this population-based sample were generally low, there was a pattern of slightly higher ratings in children in the lower than higher parental education group, but neither of these differences reached significance (ts < 1.36). Working memory (t = 3.77, p < .001) and mental set-shifting (t = 2.11, p < .05) were significantly poorer in the lower compared with higher parental education group. As expected based on previous findings and theory (Miyake et al., 2000; Huizinga, Dolan, & van der Molen, 2006), the three EFs were significantly correlated with each other (rs range = .25-.44, ps < .01).

Table 1.

Descriptive Data on the ADHD Symptom Variables and the Executive Function Measures as a Function of Parental Education Group.

	Lower educational group (n = 85)		Higher educational group (n = 78)
	M	SD	M	SD
Executive functions
Working memory	−0.21	0.59	0.23	0.84
Inhibition	−0.07	0.83	0.08	0.70
Mental set-shifting	−0.12	0.84	0.13	0.68
ADHD symptoms
Inattention	0.69	0.59	0.55	0.47
Hyperactivity/impulsivity	0.43	0.52	0.33	0.38

Note. The measures of executive functions are standardized composite scores, whereas the ADHD symptom rating is the mean of raw scores on a scale from 0 to 3.

Main Analyses

The zero-order correlations (see Table 2) showed that working memory was significantly (and with similar strength) related to inattention and hyperactivity/impulsivity in both lower and higher parental education groups. In contrast, inhibition and mental set-shifting were generally more strongly linked to inattention and hyperactivity/impulsivity in the higher than lower parental education group. In the higher educational group, all correlations involving inhibition or mental set-shifting were significant, whereas in the lower educational group, only the correlation between shifting and inattention was significant.

Table 2.

Correlations Between ADHD Symptoms and Executive Functions as a Function of Parental Education Group.

	Inattention	Hyperactivity/impulsivity
Lower educational group (n = 85)
Working memory	−.28**	−.26*
Inhibition	.10^a	−.03^b
Mental set-shifting	−.24*	−.15^a
Higher educational group (n = 78)
Working memory	−.33**	−.26*
Inhibition	−.30*^a	−.31**^b
Mental set-shifting	−.41***	−.47***^a

Significant (p < .05) difference in correlation coefficient between parental education groups.

Difference in correlation coefficient between parental education groups approached significance (p = .08).

p < .05. **p < .01. ***p < .001.

Importantly, the correlation of inhibition with inattention was significantly stronger in the higher than lower parental education group (z = 2.57, p < .05), as was the correlation of mental set-shifting with hyperactivity/impulsivity (z = 2.25, p < .05). Furthermore, the group difference in the correlation between inhibition and hyperactivity/impulsivity approached significance (z = 1.82, p = .07). In the more conservative method of testing group differences using statistical moderation, only the strongest interaction effect—between inhibition and SES in the prediction of inattention—reached significance (β = .19, p < .05). The interaction between mental set-shifting and SES in the prediction of hyperactivity/impulsivity approached significance (β = .14, p = .08).

Discussion

This study showed that whereas working memory was similarly related to ADHD symptoms in both the lower and higher parental education group, there was a correlational pattern of stronger relations of inhibition and mental set-shifting with ADHD symptoms in the higher than lower educational group. This clear pattern was supported by two significant group differences in the correlations. One additional group difference approached significance. As expected, fewer group differences reached significance in the more conservative test of statistical moderation.

The predictions being contrasted in this study were both based on the idea that EFs show strong heritability (Friedman et al., 2008) and further that they share important genetic factors with ADHD (Doyle et al., 2005; Gau & Shang, 2010; also see Crosbie & Schachar, 2001; Seidman et al., 1995). Based on these assumptions, it could be argued that the higher heritability of ADHD symptoms, the stronger relations to EF should be. On the basis of contrasting models of gene–environment interactions (Bronfenbrenner & Ceci, 1994; Rende & Plomin, 1992), suggesting higher genetic influence in different parts of the SES distribution, two alternative predictions were suggested.

Our findings on the two EFs inhibition and mental set-shifting were more in line with the prediction of stronger ADHD symptom-EF relations in higher than lower SES groups than the opposite prediction. The supported prediction was based on the idea coming from bioecological models (Bronfenbrenner & Ceci, 1994) that genetic factors are more conspicuous in higher than in lower SES contexts, because in the latter context the larger variety of environmental risks obscures the importance of such biologically inherent factors. Although levels of ADHD symptoms are usually lower in advantageous environments (Pineda et al., 1999), when symptoms do occur the genetic contribution is probably larger, because it reaches the threshold for expression of behavioral symptoms without the influence of much environmental risks.

The fact that working memory appeared to be more consistently correlated with inattention and hyperactivity/impulsivity across educational levels may support a particularly strong role of this EF in ADHD symptoms. This interpretation is consistent with the idea of poorer working memory being a potential specific endophenotype of ADHD symptoms (Castellanos & Tannock, 2002). Interpreted in line with accounts viewing ADHD as a developmentally dynamic condition (Barkley, 1997; Nigg, 2006), it could also perhaps be that another EF component (e.g., inhibition; see Brocki & Bohlin, 2006; Tillman, Brocki, Sørensen, & Lundervold, 2013) may be more consistently correlated with ADHD symptoms in younger children.

SES was indexed by parental education in this study. Although this is a common proxy for SES in the literature (see Bradely & Corwyn, 2002; Friend, DeFries, & Olson, 2008), future studies need to examine whether the present results hold also for other indicators, such as income or occupation. Due to the relatively high parental education level and general high functionality of the present sample, the variability in primarily SES but also ADHD may have been somewhat restricted. As a consequence, the estimated strength of relations, and perhaps more importantly, the differences in relations depending on parental education probably represent lower boundaries. Studying these issues in more socioeconomically diverse samples would potentially yield more significant moderation effects. However, this is partly an empirical question, and before it is answered by future studies, some caution is warranted in generalizing the moderating effect of SES to all levels of SES in the population. Furthermore, the slightly limited variability in ADHD symptoms prevents us from drawing more firm conclusions about the clinical population. We recommend future studies to consider the generalizability of the present findings by using both population-based and clinical samples.

Based on multiple pathway models of ADHD (Castellanos, Sonuga-Barke, Milham, & Tannock, 2006; Nigg et al., 2005; Sonuga-Barke, 2005), a prediction derived from the present study, which could be readily tested in future research, is that the EF pathway may be particularly relevant in higher SES parts of the population. However, because the other two pathways in this model (delay aversion and poor state-regulation) are also cognitive/neuropsychological in nature, it is possible that they will also be more relevant in this part of the population. Consequently, still other pathways may need to be identified for lower SES samples.

In conclusion, the results of the present study indicate that the ADHD symptom-EF link may not be as robust as previously suggested (e.g., Barkley, 1997). The findings also highlight the need for research trying to “unpack” the parental education variable, which is only an index for a variety of environmental factors, and examine which more specific factors account for the moderation effects. The amount of psychosocial risks associated with ADHD, such as marital conflict or stressful life events (e.g., Counts, Nigg, Stawicki, Rappley, & von Eye, 2005) may be such contributing factors.

Footnotes

Acknowledgements

We thank the families that participated in the project, and we also appreciate the valuable comments made by the members of the Uppsala Child and Baby Lab on an earlier version of this article.

Authors’ Note

This study was approved by the regional Ethical Review Board in Uppsala.

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 study was financially supported by a grant (2010-0202) to Carin Tillman from the Research Council for Working Life and Social Research in Sweden.

Notes

Author Biographies

Carin Tillman is an associate professor in psychology. She conducts developmental research with a focus on executive functions and ADHD.

Viktor Granvald is a PhD candidate in psychology. His research interests primarily concern executive functions in children.

References

Aiken

L. S.

West

S. G.

(1996). Multiple regression: Testing and interpreting interactions. London, England: SAGE.

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.

Barkley

R. A.

(1997). ADHD and the nature of self-control. New York, NY: Guilford Press.

Bradely

R. H.

Corwyn

R. F.

(2002). Socioeconomic status and child development. Annual Review of Psychology, 53, 371-399.

Brocki

K. C.

Bohlin

(2006). Developmental change in the relations between executive functions and symptoms of ADHD and co-occurring behavior problems. Infant and Child Development, 15, 19-40.

Bronfenbrenner

Ceci

S. J.

(1994). Nature-nurture reconceptualized in developmental perspective: A bioecological model. Psychological Review, 101, 568-586.

Case

Kurland

D. M.

Goldberg

(1982). Operational efficiency and the growth of short-term memory span. Journal of Experimental Child Psychology, 33, 386-404.

Castellanos

F. X.

Sonuga-Barke

E. J. S.

Milham

M. P.

Tannock

(2006). Characterizing cognition in ADHD: Beyond executive dysfunction. Trends in Cognitive Sciences, 10, 117-123.

Castellanos

F. X.

Tannock

(2002). Neuroscience of attention deficit/hyperactivity disorder: The search for endophenotypes. Nature Reviews, 3, 617-628.

10.

Chang

Lichtenstein

Asherson

P. J.

Larsson

(2013). Developmental twin study of attention problems. JAMA Psychiatry, 70, 311-318.

11.

Cohen

(1983). Applied multiple regression/correlation analysis for the behavioral sciences. Hillsdale, NJ: Erlbaum.

12.

Counts

C. A.

Nigg

J. T.

Stawicki

J. A.

Rappley

M. D.

von Eye

(2005). Family adversity in DSM-IV ADHD combined and inattentive subtypes and associated disruptive behavior problems. Journal of the American Academy of Child & Adolescent Psychiatry, 44, 690-698.

13.

Crosbie

Schachar

(2001). Deficient inhibition as a marker for familial ADHD. American Journal of Psychiatry, 158, 1884-1890.

14.

Deater-Deckard

Chen

Wang

Bell

M. A.

(2012). Socioeconomic risk moderates the link between household chaos and maternal executive function. Journal of Family Psychology, 26, 391-399.

15.

Diamond

(2006). The early development of executive functions. In Bialystock

Craik

F. I. M.

(Eds.), Lifespan cognition (pp. 70-95). Oxford, UK: Oxford University Press.

16.

Doyle

A. E

Willcutt

E. G.

Seidman

L. J.

Biederman

Chouinard

V.-A.

Silva

Faraone

S. V.

(2005). Attention-deficit/hyperactivity disorder endophenotypes. Biological Psychiatry, 57, 1324-1335.

17.

DuPaul

G. J.

Power

T. J.

Anastopoulos

A. D.

Reid

(1998). ADHD Rating Scale IV: Checklists, norms, and clinical interpretation. New York, NY: Guilford Press.

18.

Faraone

S. V.

Perlis

R. H.

Doyle

A. E.

Smoller

J. W.

Goralnick

J. J.

Holmgren

M. A.

Sklar

(2005). Molecular genetics of attention-deficit/hyperactivity disorder. Biological Psychiatry, 57, 1313-1323.

19.

Friedman

N. P.

Miyake

(2004). The relations among inhibition and interference control functions: A latent-variable analysis. Journal of Experimental Psychology: General, 133, 101-135.

20.

Friedman

N. P.

Miyake

Young

S. E.

DeFries

J. C.

Corley

R. P.

Hewitt

J. K.

(2008). Individual differences in executive functions are almost entirely genetic in origin. Journal of Experimental Psychology: General, 137, 201-225.

21.

Friend

DeFries

J. C.

Olson

R. K.

(2008). Parental education moderates genetic influences on reading disability. Psychological Science, 19, 1124-1130.

22.

Gau

S. S.

Shang

C. Y.

(2010). Executive functions as endophenotypes in ADHD: Evidence from the Cambridge Neuropsychological Test Battery (CANTAB). Journal of Child Psychology and Psychiatry, 51, 838-849.

23.

Hackman

D. A.

Farah

M. J.

(2009). Socioeconomic status and the developing brain. Trends in Cognitive Sciences, 13, 65-73.

24.

Hollingshead

(1975). Four factor index of social status. New Haven, CT: Department of Sociology, Yale University.

25.

Huizinga

Dolan

C. V.

van der Molen

M. W.

(2006). Age-related change in executive function: Developmental trends and a latent variable analysis. Neuropsychologia, 44, 2017-2036.

26.

Kaufmann

Koppelstaetter

Delazer

Siedentopf

Rhomberg

Golaszewski

. . . Ischebeck

(2005). Neural correlates of distance and continuity effects in a numerical Stroop task: An event-related fMRI study. NeuroImage, 25, 888-898.

27.

Kline

R. B.

(2005). Principles and practice of structural equation modeling (2nd ed.). New York, NY: Guilford Press.

28.

Larsson

Anckarsater

Rastam

Chang

Lichtenstein

(2012). Childhood attention-deficit hyperactivity disorder as an extreme of a continuous trait: A quantitative genetic study of 8,500 twin pairs. Journal of Child Psychology and Psychiatry, 53, 73-80.

29.

Levy

Hay

D. A.

McStephen

Wood

(1997). Attention-deficit hyperactivity disorder: A category or a continuum? Genetic analysis of a large-scale twin study. Journal of the American Academy of Child & Adolescent Psychiatry, 36, 737-744.

30.

McInerney

R. J.

Hramok

Kerns

K. A.

(2005). The children’s size-ordering task: A new measure of non-verbal working memory. Journal of Clinical and Experimental Neuropsychology, 27, 735-745.

31.

McLean

J. F.

Hitch

G. J.

(1999). Working memory impairments in children with specific arithmetic learning disabilities. Journal of Experimental Child Psychology, 74, 240-260.

32.

Miyake

Friedman

N. P.

Emerson

M. J.

Witzki

A. H.

Howerter

Wager

T. D.

(2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41, 49-100.

33.

Nigg

J. T.

(2006). What causes ADHD? Understanding what goes wrong and why. New York, NY: Guilford Press.

34.

Nigg

J. T.

Willcutt

Doyle

A. E.

Sonuga-Barke

E. J. S.

(2005). Causal heterogeneity in attention deficit hyperactivity disorder: Do we need neuropsychologically impaired subtypes? Biological Psychiatry, 57, 1224-1230.

35.

Pennington

B. F.

McGrath

L. M.

Rosenberg

Barnard

Smith

S. D.

Munroe

H. B.

. . . Olson

R. K.

(2009). Gene × environment interactions in reading disability and attention-deficit/hyperactivity disorder. Developmental Psychology, 45, 77-89.

36.

Pineda

Ardila

Rosselli

Arias

B. E.

Henao

G. C.

Gomez

L. F.

. . . Miranda

M. L.

(1999). Prevalence of attention-deficit/hyperactivity disorder symptoms in 4- to 17-year-old children in the general population. Journal of Abnormal Child Psychology, 27, 455-462.

37.

Rende

Plomin

(1992). Diathesis-stress models of psychopathology: A quantitative genetic perspective. Applied & Preventative Psychology, 1, 177-182.

38.

Rieppi

Greenhill

L. L.

Ford

R. E.

Chuang

Davies

. . . Wigal

(2002). Socioeconomic status as a moderator of ADHD treatment. Journal of the American Academy of Child & Adolescent Psychiatry, 41, 269-277.

39.

Rutter

Moffitt

T. E.

Caspi

(2006). Gene-environment interplay and psychopathology: Multiple varieties but real effects. Journal of Child Psychology and Psychiatry, 47, 226-261.

40.

Seidman

L. J.

(2006). Neuropsychological functioning in people with ADHD across the lifespan. Clinical Psychology Review, 26, 466-485.

41.

Seidman

L. J.

Biederman

Faraone

S. V.

Milberger

Norman

Seiverd

. . . Kiely

(1995). Effects of family history and comorbidity on the neuropsychological performance of children with ADHD: Preliminary findings. Journal of the American Academy of Child & Adolescent Psychiatry, 34, 1015-1024.

42.

Sonuga-Barke

E. J. S.

(2005). Causal models of attention-deficit/hyperactivity disorder: From common simple deficits to multiple developmental pathways. Biological Psychiatry, 57, 1231-1238.

43.

Stroop

J. R.

(1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643-661.

44.

Tillman

Brocki

K. C.

Sørensen

Lundervold

A. J.

(2013). A longitudinal examination of the developmental executive function hierarchy in children with externalizing behavior problems. Journal of Attention Disorders. Advance online publication. doi: 10.1177/1087054713488439

45.

Willcutt

E. G.

Doyle

A. E.

Nigg

J. T.

Faraone

S. V.

Pennington

B. F.

(2005). Validity of the executive function theory of attention-deficit/hyperactivity disorder: A meta-analytic review. Biological Psychiatry, 57, 1336-1346.