Development of executive functioning in school-age Tunisian children.

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Development of executive functioning in school-age Tunisian children ac

b

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Tarek Bellaj , Imen Salhi , Didier Le Gall & Arnaud Roy a

Psychology Program, Department of Social Sciences, College of Arts and Science, Qatar University, Doha, Qatar b

Faculty of Human and Social Sciences of Tunis, Tunis University, Tunisia c

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Psychology Laboratory of Pays de la Loire, EA4638, LUNAM, University of Angers, France d

Learning Disabilities Reference Center, Nantes University Hospital, France Published online: 11 Jul 2015.

To cite this article: Tarek Bellaj, Imen Salhi, Didier Le Gall & Arnaud Roy (2015): Development of executive functioning in school-age Tunisian children, Child Neuropsychology: A Journal on Normal and Abnormal Development in Childhood and Adolescence, DOI: 10.1080/09297049.2015.1058349 To link to this article: http://dx.doi.org/10.1080/09297049.2015.1058349

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Child Neuropsychology, 2015 http://dx.doi.org/10.1080/09297049.2015.1058349

Development of executive functioning in school-age Tunisian children Tarek Bellaj1,3, Imen Salhi2, Didier Le Gall3, and Arnaud Roy3,4


1

Psychology Program, Department of Social Sciences, College of Arts and Science, Qatar University, Doha, Qatar 2 Faculty of Human and Social Sciences of Tunis, Tunis University, Tunisia 3 Psychology Laboratory of Pays de la Loire, EA4638, LUNAM, University of Angers, France 4 Learning Disabilities Reference Center, Nantes University Hospital, France Research regarding executive functioning (EF) in children rarely focuses on populations in African or Middle-Eastern Arabic-speaking countries. The current study used a cross-sectional design to examine the developmental trajectories of school-age Tunisian children in three domains of executive control (inhibition of prepotent responses, cognitive flexibility, and working memory) as well as their mutual interactions and the effects of gender and parents’ education level. Inhibitory processes, cognitive flexibility, and working memory were assessed using the Stroop test, a version of the Hayling test adapted for children, simple and alternating tasks of verbal fluency, and verbal and visuospatial span tasks (forward and backward spans). The study population included 120 7- to 12-year-old Tunisian children (60 girls, 60 boys) who were grouped and matched for age, gender, and parents’ education level. The results revealed an overall effect of age on executive performance, whereas gender and parents’ education level showed non-significant effects. In addition, executive indices were significantly associated with fluid intelligence level. Partial correlation analyses (controlled for age) found significant links between indices that assessed the same executive process, except for inhibitory processes; the temporal indices for inhibitory processes showed relative independence. The correlations between indices that assessed distinct executive processes were weaker (but significant). Overall, the results suggest that executive components in school-age Tunisian children operate according to relatively homogeneous developmental trajectories, marked by peaks of maturity that differ according to the assessed index. A transcultural approach to EF is discussed in terms of the unity and diversity of its components. Keywords: Executive functioning; Development; Neuropsychology; Culture.

During development, children express a growing capacity to control their thoughts and actions (Diamond, 2002). This improvement of control capacities is associated with significant changes in the neuroanatomy and functioning of the brain, which takes the Many thanks to all the girls and boys and their families for their participation in the study. As well, we express appreciation to Tunisian and Angevin neuropsychological teams for their assistance in collecting, rating, and analyzing the data and to all who made the mutual constructive collaboration always possible. No potential conflict of interest was reported by the authors. Address correspondence to Arnaud Roy, Université d’Angers, Faculté des Lettres, Langues et Sciences Humaines, 11, boulevard Lavoisier, 49045 Angers Cedex 01 France. E-mail: [email protected]

© 2015 Taylor & Francis


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form of an increase in neuronal networks and brain volume (Dempster, 1992; Raz, 2000), especially in prefrontal areas (Adleman et al., 2002; Schroeter, Zysset, Wahl, & Von Cramon, 2004). The prefrontal cortex is known to play a crucial role in the planning, organization, and cognitive-behavioral regulation of tasks (Best, Miller, & Jones, 2009; Luria, 1966), which corresponds to executive functioning (EF). EF includes a variety of cognitive processes involved in problem-solving skills (Levin et al., 1991), planning (Shallice, 1982), task initiation (Burgess & Shallice, 1996), mental flexibility (reactive and spontaneous; Eslinger & Grattan, 1993), and inhibition (Denckla, 1996). All of these processes are activated when it is necessary to face new or difficult situations in which routine actions become insufficient (Meulemans, 2006). Several developmental studies devoted to EF in early childhood have revealed performance patterns close to the behavior of patients with frontal lesions (for example, Anderson, Anderson, & Lajoie, 1996; Lehto, Juujärvi, Kooistra, & Pulkkinen, 2003; Welsh, Pennington, & Groisser, 1991). This observation is in agreement with data that show an early and prolonged maturation of the prefrontal cortex and its involvement in the progressive development of EF (Diamond, 2002). However, the relations between the developmental trajectories of hypothetically different executive components and performance on the traditional tasks aimed at assessing them are still debated, in particular because of the problem of the “task impurity” of EF, which interferes with the fractionation of these high-level processes (Hughes & Graham, 2002; Kline, 1998; Miyake et al., 2000). Indeed, single indicators for a given construct (e.g., working memory) can rarely, if ever, be viewed as a pure measure of that construct. Most measures are contaminated by random and systematic error (see Kline, 1998). The task impurity problem is highly relevant to EF research, as the manifestation of EF components invariably involves other non-executive processes (e.g., Miyake et al., 2000). Neuropsychology recognizes that EF consists of separate but interrelated components. This approach to EF, which is simultaneously unitary and pluralist, claims that one or several common mechanisms underlie all executive processes (Miyake et al., 2000). Researchers that study pediatric populations agree with this conception (Anderson, Anderson, Northam, Jacobs, & Catroppa, 2001; Asato, Sweeney, & Luna, 2006; Bull & Scerif, 2001; Hughes, 1998; Lehto et al., 2003). Empirical studies based on the administration of a battery of executive tasks have shown weak correlations between different tasks and found many distinct factors by means of exploratory and confirmatory factor analysis (Anderson et al., 2001; Brocki & Bohlin, 2004; Klenberg, Korkman, & LahtiNuuttila, 2001; Lehto, 1996; Lehto et al., 2003; Levin et al., 1991; Senn, Espy, & Kaufmann, 2004; Sevino, 1998; Welsh et al., 1991). The fractionation of EF has also been supported by other empirical studies that identified differential developmental curves according to EF at pre-school (Hughes, 1998; Senn et al., 2004) and school ages (Huizinga, Dolan, & Van Der Molen, 2006; Lehto et al., 2003) as well as in adulthood (Fisk & Sharp, 2004; Miyake et al., 2000; Zelazo, Craik, & Booth, 2004). Moreover, the results of brain-imaging studies have argued for multifactorial and unitary conceptions of EF by showing that different prefrontal areas are differentially activated by a variety of executive tasks, including dorsolateral and ventromedial areas (Olson & Luciana, 2008) as well as the anterior cingulate cortex (Best et al., 2009). Finally, the different levels of involvement of components of the executive architecture in certain clinical pathologies, such as attention deficit hyperactivity disorder (ADHD), confirms the supposed independence of EF (Barkley, 1997; Shallice et al., 2002). The inhibition of prepotent responses, cognitive flexibility, and working memory constitute the three main components that are often postulated to reflect the functional


EXECUTIVE FUNCTION IN TUNISIAN CHILDREN

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architecture of the cognitive aspect of EF in adults (Miyake et al., 2000) and in children (Brocki & Bohlin, 2004; Lehto et al., 2003). For the inhibition of prepotent responses, a significant developmental progression has been observed in young people through the use of multiple paradigms, such as the day-night task and the knock-tap task (Balamore & Wozniak, 1984; Diamond, 2002; Gerstadt, Hong, & Diamond, 1994; Klenberg et al., 2001; Williams, Ponesse, Schachar, Logan, & Tannock, 1999). Many studies conducted in school-age children have shown a prolonged improvement of inhibition capacities with age using the Stroop test (Armengol, 2002; Comalli, Wapner, & Werner, 1962; Koenig, 1989; MacLeod, 1991; Sevino, 1998) and the Hayling task (Shallice et al., 2002). Cognitive flexibility abilities have been shown to follow the same progression for both the verbal and figural modalities of spontaneous shifting (Arán-Filippetti & Allegri, 2011; Brocki & Bohlin, 2004; Hurks et al., 2010; Kavé, 2006; Kavé, Kigel, & Kochva, 2008; Koren, Kofman, & Berger, 2005; Lehto et al., 2003; Levin et al., 1991; Riva, Nichelli, & Devoti, 2000; Sevino, 1998; Tallberg, Carlsson, & Lieberman, 2011; Welsh et al., 1991). However, the age of maturity with respect to verbal fluency is debated (Anderson et al., 2001; Kavé et al., 2008; Klenberg et al., 2001; Riva et al., 2000; Sauzéon, Lestage, Raboutet, N’Kaoua, & Claverie, 2004; Sevino, 1998; Welsh et al., 1991), and the dissociation of progressive profiles according to the suggested division, semantic or phonemic (Klenberg et al., 2001; Levin et al., 1991), is difficult to check because some studies did not distinguish between the two conditions (Brocki & Bohlin, 2004; Lehto et al., 2003) or they focused on only one method (Anderson et al., 2001; John & Rajashekhar, 2014; Sevino, 1998; Welsh et al., 1991). Verbal fluency development has been studied in different languages, including French (Sauzéon et al., 2004), Italian (Riva et al., 2000), Swedish (Tallberg et al., 2011), Hebrew (Kavé et al., 2008), Dutch (Hurks et al., 2010), Spanish (Arán-Filippetti & Allegri, 2011), and Malayalam (John & Rajashekhar, 2014). However, few studies have been performed in the Arabic language. For working memory, development continues until adolescence for both verbal and visuospatial components (Best et al., 2009; Dempster, 1992; Gathercole, Pickering, Ambridge, & Wearing, 2004; Hale, Bronik, & Fry, 1997; Klingberg, Forssberg, & Westerberg, 2002; Luciana, Conklin, Hooper, & Yarger, 2005; Nagy, Westerberg, & Klingberg, 2004; Scherf, Sweeney, & Luna, 2006). For example, Gathercole et al. (2004) found a linear increase in performance from age 4 to age 15 for a battery of working memory tasks of varying complexity (except for a visual patterns task, for which performance leveled off around age 11). Luciana et al. (2005) examined non-verbal working memory tasks of varying complexity, ranging from a non-complex task (face recognition) to a complex task (spatial self-ordered search). As predicted, the age-related changes in performance depended on the complexity of the particular task. For ages 9 to 20, there were no performance differences on the simple face recognition task; in contrast, there were steady improvements on the most difficult self-ordered search task until age 16. Thus, it is important to consider the complexity of the task when extrapolating a general trajectory of working memory development. Although developmental studies performed to date have shown that different aspects of EF develop in line with the progress of childhood, the developmental curves differed from one study to another. These differences probably result from the large variety of methodological choices and because the socio-demographic variables that may act as determining factors are not always considered or give rise to conflicting data, making it difficult to generalize the results. In addition to the influence of age, studies have also reported the influence of other demographic variables, such as gender


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(Becker, Isaac, & Hynd, 1987; Berlin & Bohlin, 2002; Klenberg et al., 2001), intelligence (Ardila, Pineda, & Rosselli, 2000; Arffa, 2007; Arffa, Lovell, Podell, & Goldberg, 1998; Baron, 2003; Mahone et al., 2002), type of education (Ratcliff et al., 1998), socioeconomic level (Arán-Filippetti & Richaud De Minzi, 2012) and bilingualism (Pena, Bedore, & Zlatic-Giunta, 2002), with some reporting no effect or a weak effect (Armengol, 2002; Brocki & Bohlin, 2004; Welsh et al., 1991). For example, the very few studies that have investigated the effect of gender have reported contradictory results. Welsh et al. (1991) did not find a significant gender-related effect on various executive measures in a developmental study of 100 normal children between 3 and 12 years of age. In contrast, Brocki and Bohlin (2004) suggested that gender-related disparities would be higher beyond 12 years of age. Likewise, similar results were found in studies analyzing the effect of gender on changes in performance on specific executive tasks in pediatric populations from countries with different languages and cultures. Armengol (2002) evaluated 349 Spanish-speaking children, aged 6 years and 6 months to 12 years and 11 months, who were attending two different Mexican institutions (public and private) using a Spanish version of the Stroop test. The results showed an improvement in performance based on the time index score and number of errors. However, the gender differences were not found to be significant. These data are consistent with recent studies of Stroop test performance (Talarowska, Florkowski, Chamielec, & Galecki, 2013) that did not find a significant gender-related effect regardless of the age of the subjects. Riva et al. (2000) studied developmental changes in verbal fluency as a function of age and gender in 160 Italian children, aged 5 years and 11 months to 11 years and 4 months. The results indicated a significant effect of age but no gender effect. Other studies conducted in French, Dutch, Spanish, Hebrew and Indian pediatric populations also found similar results for semantic and phonemic verbal fluency tasks (Arán-Filippetti & Allegri, 2011; Hurks et al., 2010; John & Rajashekhar, 2014; Kavé et al., 2008; Sauzéon et al., 2004). In addition, a significant effect of age and a nonsignificant effect of gender have been highlighted in studies of working memory in pediatric school-age populations (Brocki & Bohlin, 2004; Welsh et al., 1991). In contrast, working with a sample of 52 subjects aged 7 to 15 years, Levin et al. (1991) showed higher performance on semantic fluency tasks in girls compared with boys. These results agree with those obtained in studies of the performance of populations of normal school children and children with certain pathologies on tasks that assess language skills and verbal fluency (Anderson et al., 2001; Ardila, Rosselli, Matute, & Guajardo, 2005; Klenberg et al., 2001). Other studies, however, have found superior performance for boys compared to girls of the same age (Capitani, Laiacona, & Barbarotto, 1999; Halpern, 2000). Regarding inhibition tasks, Berlin and Bohlin (2002) reported a higher level of inhibitory control in preschool girls compared to boys of the same age. Carlson and Moses (2001) reported better performance for 3- to 4-year-old girls compared to boys of the same age in tasks assessing inhibitory control. However, other studies have reported the opposite results, such as Becker et al. (1987), who noted faster reaction times on a Go/No-Go task for boys compared to girls. For working memory tasks, the same contradictions have been noted. Some authors do not find significant differences between boys and girls of the same age for working memory tasks, whereas others have identified differences at specific ages, generally in favor of boys (Brocki & Bohlin, 2004). In sum, the gender differences reported for executive performance are inconsistent. In some studies girls were favored and in other studies boys were favored, with many studies reporting a non-significant effect. The results seem to be highly



5

dependent on the nature of the task and the methods of analysis used (Brocki & Bohlin, 2004). For example, John and Rajashekhar (2014) have suggested that the conflicting results regarding the effect of gender on verbal fluency performance could be linked to the nature of the selected semantic category (e.g., animals and fruits and vegetable or vehicles). For example, Capitani et al. (1999) reported a benefit in favor of boys compared to girls of the same age for the semantic category “tools”, but this advantage favored girls for the semantic category “fruits”. It should also be noted that the effect of the general intellectual level of children on executive task performance was seldom taken into account, even if there was a tendency to show that fluid intelligence is involved in several domains of cognitive functioning. However, even when the authors did consider the influence of this factor, inconsistencies in the data from one study to another still emerge. Welsh et al. (1991) did not identify any significant correlations between IQ and all executive measures obtained for 6to 12-year-old children. Similar results have been previously noted by Golden (1978), who found low correlations between IQ and executive measures related to motor planning. Boone, Ghaffarian, Lesser, and Hill-Gutierrez (1993) and Ardila et al. (2000) obtained similar results for tasks assessing cognitive flexibility capabilities. In a study of 6- to 16-year-old children with ADHD, Riccio et al. (1994) found significant correlations between IQ (obtained via the Wechsler Intelligence Scale for Children-Revised (WISC-R)) and the indices of the Wisconsin Card Sorting Test (WCST) in children aged 9 years to 11 years and 11 months. However, in a study of 148 6- to 15-year-old children of different intellectual levels (average, above average, gifted youth), Arffa (2007) found significant correlations between IQ and certain executive tests (such as the Stroop, verbal fluency, Rey Complex Figure, underlining and figure design tests). Finally, very few studies have analyzed the effect of parents’ education level on the development of children’s EF, and these studies have yielded contradictory results as well. While some studies considered the effect of this factor on the performance of children on executive tasks (Ardila et al., 2005; Klenberg et al., 2001), others have reported a significant but non-predictive effect (Armengol, 2002). Ardila et al. (2005) conducted a study of 622 5- to 14-year-old children, who were matched for age, gender and type of school attended (public and private) in order to analyze the relationships between parental educational level and children’s performance on executive tests (phonemic and semantic fluency, figural semantic and non-semantic fluency). Most of the scores obtained were significantly correlated to parental educational level, especially the verbal tests. Klenberg et al. (2001) studied the development of EF in 400 3- to 12-year-old Finnish children. The data from 10 subtests, which measured impulse control and the inhibition of irrelevant responses, auditory and visual attention, visual search, planning, and verbal and visual fluency, were included. Significant relations between gender and development and between parental education and development were found for several of the subtests. Hoff (2003a, 2003b) has argued that parents with a high level of education offer their children a more intellectually and verbally stimulating environment (Ganzach, 2000; Teachman, 1987; White, 1982). Armengol (2002) used a multiple regression analysis on Stroop data from Mexican school-age children and found that the educational level of the father and the mother explained 3% of the variance of the results. The study of the universality and specificity of cognitive processes via emic or etic approaches is important for a better understanding of human cognition. Yet, hardly any studies of EF have been conducted among African or Middle Eastern Arabic-speaking


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populations. A search of the major academic databases (PsychINFO, Elsevier, ERIC, Medline, Psycharticles and PubMed) conducted on 21 March 2015 using the words “Children”, “Arabic” and “Executive functions” returned four publications. the first was a clinical study that did not give any developmental benchmark because it involved Schistosoma mansoni infection in three samples of 40 9- to 12-year-old children (Nazel, El-Morshedy, Farghaly, Shatat, & Barakat, 1999). The second and third studies investigated the effect of bilingualism on EF using, among other tests, the Stroop test, phonemic fluency tasks, the Trail Making Test (Abdelgafar & Moawad, 2014), and category fluency tasks (Soliman, 2014). Both studies did not give any descriptive data of the children’s performance on any of the tests used. The fourth study compared the development of attentional processes in Syrian and German children using a computerized battery called KITAP, the Test of Attentional Performance for Children (Sobeh & Spijkers, 2013). Although EF was not studied per se, some of the attentional processes examined in this study overlap with EF. However, the tests used were computer-based and the obtained data cannot be easily compared to classic EF tools such as the Stroop, Hayling, and Trail Making tests as well as the WCST and fluency tasks. Moreover, this battery was used without any research regarding the feasibility of its use with Syrian children and without any cultural adaptation of its European culture-based scenarios (Sad and the Happy Ghost, The Owls, The Witches’ Parade, The Ghosts’ Ball, etc.). Non-verbal tests are as culturally biased as verbal tests, and non-verbal stimuli are not necessarily “culture-free” (De Mattos Pimenta Parente & Lecours, 1988). The authors did not even mention the language in which the test was administered or describe any effort of item translation. Therefore, there is a strong need to expand research to include such sociocultural contexts, which may allow the analysis of the role of cultural factors in the development of EF. Therefore, the main aim of this study was to examine the developmental trends of the three basic components of EF, namely inhibition of prepotent responses, cognitive flexibility, and working memory among a sample of 7- to 12-year-old Tunisian children using traditional neuropsychological tasks and a cross-sectional design. The secondary objectives included assessment of the potential effects of two demographic factors (gender and parents’ education level) and of the effect of general intellectual level on the development of EF. A developmental study of EF in the Tunisian sociocultural context has the advantage of compensating for the lack of normative data. We expected (1) an improvement of executive control abilities in the different assessed domains between the ages of 7 and 12 years. We expected progress in inhibition, flexibility, and working memory skills to be evident in the Stroop and Hayling tasks, verbal fluency (semantic, phonemic, and alternating), and verbal and visuospatial spans, respectively (Brocki & Bohlin, 2004; Lehto et al., 2003; Sevino, 1998). Given the moreor-less established influence of the demographic factors (gender and parents’ education level) on the executive control abilities of school-age children, we also expected (2) a significant effect of parents’ education level (Ardila et al., 2005) and a non-significant effect of gender on executive performance (Ardila et al., 2005; Brocki & Bohlin, 2004; Welsh et al., 1991). Moreover, we expected (3) the fluid intelligence level (measured with the Raven Matrices test) to be significantly correlated to performance on the different executive tasks (Arffa, 2007; Blair, 2006). Finally (4), we expected to find weak but significant correlations between the different executive indices (after age adjustment), in agreement with studies that consider EF as a set of distinct yet interrelated components (Lehto et al., 2003; Miyake et al., 2000).


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METHODS


Participants A total of 120 Tunisian children, between 7 and 12 years of age, participated in the study. All of the children were native Arabic speakers and were right-handed. None were authentic bilinguals who efficiently learned any other language before the age of 5. We made sure that all of the children were Arabic-dominant monolinguals based on information collected using the Language Experience and Proﬁciency Questionnaire (Marian, Blumenfeld, & Kaushanskaya, 2007). From each of the six primary education levels, 10 girls and 10 boys were selected randomly as long as they were healthy and not suffering from mental, sensory or motor disorders. None of the children had learning difficulties or neurological or psychiatric disorders. This information was gathered from interviews conducted with parents, teachers and educational staff and by consulting school and medical records. The recruitment of children was made in Habib Bourguiba’s public school, which is located in the northern suburbs of Tunis and is attended mostly by the children of families belonging to the middle socioeconomic class. Recruitment took place during the third quarter of the 2012 school year. Written parental consent and informed participant consent was obtained prior to testing. Among the 130 children who agreed to participate in the study, 5 quit the study and 1 child was excluded because of suspected mental deficiency. In addition, 2 other children were excluded because both parents were not Tunisian, and 2 others were excluded from the study because of their medical record (1 child was diagnosed with esotropia, another suffered from a behavioral disorder). The age groups were comparable in terms of the mother’s and father’s education level, respectively: F(5, 114) = 0.37, p = .867, ηρ2 = .0161; F(5, 114) = 1.38, p = .238, ηρ2 = .0569. Intelligence, as measured by the Raven Matrices Test (Raven, Court, & Raven, 1985), evolved from one age to another, F(5, 108) = 6.55, p < .0001, ηρ2 = .2327, but no significant difference was found between girls and boys, F(1, 108) = 0.58, p = .4473, ηρ2 = .0053 and the Age × Gender interaction was not significant, F(5, 108) = 1.11, p = .3568, ηρ2 = .0491. Table 1 shows the demographic and psychometric data for the study population. Table 1 Demographic and Psychometric Data. Education level

Age (years) Mother’s education (years) Father’s education (years) Raven Matrices test (raw score)

1st year (n = 20)

2nd year (n = 20)

3rd year (n = 20)

4th year (n = 20)

5th year (n = 20)

6th year (n = 20)

Total (n = 120)

M

M

M

M

M

M

M

SD

SD

SD

SD

SD

SD

SD

7.02 0.34 7.87 0.47 8.89 0.28 9.83 0.34 10.89 0.35 12.03 0.22 9.42 1.76 14.1 3.49 13.4 2.93 14.7 3.43 14.4 2.23 14.05 3.53 14.35 3.61 14.14 3.28

14.15 3.84 14.05 3.52 14.15 5.1

15.2

2.28 16.4

2.41 14.5

3.86 14.74 3.52

22.85 4.62 24.3

27.1

3.82 28.15 4.12 29.8

4.08 26.46 4.92

5.8

26.55 3.8

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Measures Tests evaluating EF are numerous, and there is no consensus regarding the relevance of a specific instrument and the executive processes recruited by each test. Nevertheless, we sought to categorize several executive tests on the basis of developmental and factor analysis data from Lehto et al. (2003) and Miyake et al. (2000). We therefore chose to assess inhibition, mental flexibility and working memory. For each of these functions, we decided to rely on two different tasks. Note that all tasks were administered in the local Tunisian Arabic dialect to control biases related to understanding of the instructions and were the subject of adaptation and validation studies using the Tunisian population (Romdhane, Bellaj, & Attia-Romdhane, 2008). Particular attention was paid to ensuring the equivalence of the construct (Bellaj, 2011). Compared to the original version, the changes did not affect the content of the instructions, the principles of scoring or the administration procedure (Bellaj & Seron, 2014). Considering the general concept of inhibition, which refers to different mechanisms (Bjorklund & Harnishfeger, 1995; Zacks & Hasher, 1997), we opted for the Stroop test, which evaluates the susceptibility to interference, and the Hayling test, which requires the deletion of dominant responses. Based on Eslinger and Grattan’s (1993) dichotomy between “spontaneous flexibility” and “reactive flexibility”, simple and alternate verbal fluency tasks, respectively, were used. Finally, based on Baddeley and Hitch’s (1974) working memory model, we analyzed the ability to passively store information using verbal (forward digit span) and visuospatial (forward block-tapping test) tasks before examining the ability to temporarily store and manipulate information via the backward verbal digit span and the backward visuospatial block-tapping tests.

The Stroop Test We used the Modern Standard Arabic version of the Stroop test (Bellaj, Bouaziz, & Bouaziz, 1995), which requires the child to inhibit the automatic reading process in favor of a less automatic naming process. The task includes three conditions, and each condition has a card containing 100 items randomly presented in a 10 × 10 matrix on a landscapeoriented A4 sheet. Time to completion for each of the three conditions was used as the dependent variable. From the first card, called the “Word” condition, the child read aloud a list of color names (red, green and blue) printed in black type; in the second condition, the “Color” condition, the child was asked to name the color of a series of dots; and in the third, the “Color/Word” condition, the color names were printed in a discordantly colored ink (e.g., the word “red” printed in blue), the child was asked to name the color of the printed words while inhibiting the automatic tendency to read the word. For each condition, the completion time and the number of two types of errors (errors corrected spontaneously and those that were not) were recorded. A handheld stopwatch was used to record the time to completion for each condition. This was the time that elapsed between the response to the first and last items in the list (the time was recorded in seconds). An interference score was calculated using the formula: Color / Word − Color. In this study, Stroop’s original measure of interference and inhibition is obtained by calculating a difference score based on subtracting the time it took to name the colors from the interference condition. Assuming an additive information-processing model, Golden (1978) developed “a predicted color-word score” based on the assumption that


9

subjects first read the word and then named the color. On the basis of a factor analysis of the various scores suggested as measures of “pure interference”, Golden found that the best measure was represented by either Color / Word (CW) − predicted (W) or by CW − C. In our study, we chose to use the latter formula based on the assumption that color identification and word reading can be processed in parallel rather than sequentially, with word reading occurring faster than color identification (MacLeod, 1991; Posner, 1978).


The Junior Hayling Test The Junior Hayling test (Shallice et al., 2002) consists of two parts, A and B. Each part has 10 sentences in which the final word is omitted. In both parts, the response is highly activated by context and there is a particularly high probability of one specific response. In part A (initiation), the word completing the sentences has to fit appropriately at the end of the sentence. In part B (inhibition), the child has to complete the sentence using a word that makes no sense at all within the context of the sentence, a word unrelated to the sentence in every way. If the child provided an answer that gave meaning to the phrase, this was indicated to him, and the task instructions were repeated. If the latency exceeded 60 seconds, we passed on to the next sentence, and a response latency of 60 seconds was recorded. In both parts, the response time (in seconds) and the produced word were recorded. For the response time, we calculated what Shallice called the “Additional Thinking Time” (ATT) as the difference between the latencies in parts A and B (B − A). For the produced words, each response in part B was scored according to its semantic relatedness to its stimulus sentence. One point was given if the child produced a word that was simply semantically related to the stimulus sentence, and a three-point penalty was applied for a word that was a straightforward completion of the sentence. If the word produced was not connected to the sentence, zero points were given (the error score could vary between a total of zero to thirty points). The sum is what Shallice called the “error score”. The Arabic version of the Junior Hayling test was not a simple translation of the items proposed by Burgess and Shallice (1997) but was developed based on empirical local data (Bellaj, 1999). In this sense, the content of 50 children’s sentences were selected and tested with a group of 30 6- to 12-year-old children. This pre-experimentation led to the selection of sentences in which the probability of giving the appropriate last word was over 90%. The individual phrases were randomly distributed over the two parts of the test so that the average frequency of missing words and the average length were equivalent in both parts of the test. Each of the two parts of the test included 10 sentences, and 4 more sentences were added to serve as a familiarization trial. Simple and Alternating Verbal Fluency In the verbal fluency tasks, the child was required to produce, within a fixed amount of time (120 seconds), as many different words as possible according to specific criteria, which included the exclusion of proper names and repetitions of the same word with different morphosyntactic structures (according to Spreen & Strauss, 1998). We examined verbal fluency via three conditions: a simple semantic word fluency task using the category “Animals”, a simple phonemic word fluency task using the letter “M”, and an alternating word fluency task in which the child was invited to alternate between phonemic and semantic criteria: “Clothing” and the letter

10

T. BELLAJ ET AL.


“H”. Note that the choice of categories and letters was tested in a pre-experiment to determine average production rates in Tunisian populations; the letters “M”, “H” and “K” were found to be the most frequently produced (Aguibi & Bouaziz, 1998). The categories “Animals” and “Clothing” and the letters “M” and “H” were produced at similar rates in Tunisian populations. They have also been widely used in previous studies of children in different cultures and languages (Spanish: Arán-Filippetti & Allegri, 2011; Italian: Riva et al., 2000; Dutch: Hurks et al., 2010; Finnish: Klenberg et al., 2001; Hebrew: Kavé et al., 2008; Swedish: Tallberg et al., 2011; India, Malayalam: John & Rajashekhar, 2014; and the letter “M” in Spanish: Ardila et al., 2005; Matute, Rosselli, Ardila, & Morales, 2004; Nieto, Galtier, Barroso, & Espinosa, 2008). The instructions given to the children were adopted from Spreen and Strauss (1998). For phonemic fluency, they were as follows: I will say a letter of the alphabet. Then, I want you to give me as many words that begin with that letter as quickly as you can in two minutes. For example, if I say “S” “‫”ﺱ‬, you might give me “‫ ﺳﻠﻄﺔ‬,‫ ﺳﺎﺣﺔ‬,‫ﺳﺒﻮﺭﺓ‬. . .”. I do not want you to use words that are proper names, such as “‫ ﺳﻮﺳﺔ‬,‫ ﺳﺎﻣﻲ‬or ‫”ﺳﻨﻴﺔ‬. Additionally, do not use the same word with different endings, such as “eat” and “eating”. Any questions? (pause). Begin when I say the letter. The first letter is “M”. Go ahead.

Timing began immediately. For semantic fluency, the instructions were as follows: “I am going to tell you the names of fruits: oranges, apples, bananas. Can you think of other fruits?”. After allowing the child to name other things, and correcting him or her if he or she produced an incorrect response, the task was explained once again. “Now, tell me the names of as many animals as you can. Name them as quickly as possible in two minutes”. For alternating fluency, the instructions were: Say as many words, alternating a word beginning with . . . and a name of . . ., avoiding words of the same family and proper names, and do this in two minutes. For example, if I have to give words that alternate between a word beginning with the letter “B” “‫ ”ﺏ‬and the name of a color, I will say “‫ﺃﺣﻤﺮ‬-‫ ﺑﺤﺮ‬،‫ﺑﺎﺏ—ﺃﺧﻀﺮ‬،. . .”. Can you continue alternating between a word beginning with B and a color, as many as you can?

After allowing the child to respond, and correcting him or her if he or she produced an incorrect response, the task was explained once again: Now, alternate between giving me a word beginning with the letter “H” and the names of as many clothes as you can. Say them by switching back and forth between the words starting with the letter “H” and the clothes as quickly as possible, for two minutes.

The responses were recorded verbatim. The number of correct responses, repetitions, and intrusions for each of these conditions were recorded. In the alternating fluency task, we considered each time a child provided an additional word of the same type (category or letter) to be perseveration. All of the subjects began with the simple task; whether the phonemic or semantic fluency task was administered next was


11

counterbalanced between the groups. Then, we administered the alternating verbal fluency task. A two-minute break was given between tasks.


Digit Span This task is an adaptation of the WISC-R Digit Span subtest (Wechsler, 1996), with seven levels containing three series of two to eight digits for each span. The examiner reads the sequence of digits aloud at a rate of one digit per second. In the forward digit span condition, the child is asked to immediately repeat the string of numbers in the order in which they were heard, starting with the repetition of a two-digit series. The length of each sequence of numbers is increased if the child responds correctly. The task was stops when the child fails to correctly reproduce two of the three sequences of numbers at any given length. In the backward condition, a different series of digits is presented, and the child has to repeat the digits in reversed order. The forward digit condition is completed prior to the backward digit condition. In each condition, the score is the highest number of digits that was repeated correctly. Corsi Block-Tapping Test The Corsi (1972) block-tapping test was to assess visuospatial span and visuospatial working memory. It utilizes a white wooden board (21 × 30 cm) in which nine identical and spatially separated blue blocks are arranged quasi-randomly. The child is invited to observe the sequence of blocks “tapped” by the finger of the examiner and to reproduce the sequence in the same order (forward visuospatial span condition, BTT-F) or in the opposite order (backward visuospatial span condition, BTT-B). The sequence starts with two blocks and gradually increases in length up to nine blocks. Each span contains three sequences, and the task stops when the child fails to correctly reproduce at least two sequences of a given length. For each of the two conditions, the score is the longest series that was correctly reproduced at least two out of three times. Procedure All participants were individually assessed outside of school hours in a calm room in their school or home environment. Two assessment sessions were needed. Depending on the age of the child, each session took approximately 20–30 minutes. The Stroop, the spans and the verbal fluency tests were presented in a fixed order during the first session, the Hayling test and the Raven Matrices during the second. All of the tests were administered in the local Arabic language by trained neuropsychologists using standardized instructions. Statistical Analyses The obtained scores in the various tests were subject to the application of descriptive and inferential statistical analysis. Examination of the normal distribution by the Kolmogorov-Smirnov test revealed that the indices coming from the fluency and Stroop tasks are distributed normally, while those of Hayling and spans deviate significantly from normality. Given the power of the analysis of variance (ANOVA; Abdi, 1987) and the importance of examining the effects of interactions, we opted for the use of parametric

12

T. BELLAJ ET AL.

statistical tools. For comparison, we used one-way, factorial, or repeated measures ANOVAs followed by an assessment of the weight of the effect with partial eta squared in order to study the developmental aspects and differential for each process. When the effects were significant, we used the Fisher post hoc test to refine the results. For the analysis of associations, we used Pearson’s correlation coefficient. Finally, partial correlations, controlling the variable age, were used to examine the relationships between processes regardless of the age effect. For all analyses, the significance level for p was set at .05. All analyses were performed by means of StatSoft, Inc. (2007).


RESULTS The descriptive data of the various executive measures for each of the different groups according to gender and age are summarized in Table 2. In what follows, we report the results analysis by process. Inhibition Development To test the effect of age and gender on changes in the Stroop interference score, we conducted a two-way ANOVA (Age × Gender: 6 × 2, see Figure 1). The analysis revealed a significant effect of age, F(5) = 4.53, p < .001, ηρ2 = .1735, whereas the gender effect was non-significant, F(1) = 0.14, p = .7099, ηρ2 = .0013. In addition, the age by gender interaction effect was also non-significant, F(5) = 1.44, p = .2169, ηρ2 = .0624. The post hoc Fisher’s least significant difference (LSD) test indicated that the interference score was significantly higher for the group of 7-year-old children in comparison to the 9-, 11- and 12-year-olds (p < .05). It is also higher for the 8-year-olds as compared to the 11- and 12-year-olds (p < .05) and, finally, for the 10-year-olds in comparison to the 11-year-olds (p < .05). The difference between the performance of the 9- and 11-year-olds showed a marginal effect (p = .055). All other comparison pairs were non-significant. To test age and gender effects on errors score variations at the Hayling, we conducted a two-way ANOVA (Age × Gender: 6 × 2). The analysis indicated a significant effect of age, F(5) = 3.45, p < .01, ηρ2 = .1379. Gender effect was significant in favor of boys, F(1) = 4.22, p < .05, ηρ2 = .0376. Age by gender interaction effect was nonsignificant, F(5) = 0.98, p = .4312, ηρ2 = .0435. Errors score progression according to age and gender are represented in Figure 2. The post hoc Fisher’s LSD test showed that Hayling errors scores were significantly higher (p < .01) for the 8-year-old children (M = 4.65; σ = 3.69) compared with the older children (9-year-olds, M = 2.35 and σ = 1.78; 10-year-olds, M = 2.40 and σ = 1.82; 11year-olds, M = 2.05 and σ = 1.28; 12 year-olds, M = 2.50 and σ = 2.14). Other pairs of comparison were non-significant. In addition, post hoc analyses showed a significant gender effect only between the group of 8-year-old girls (M = 3.40; σ = 2.32) and boys (M = 5.90; σ = 4.46) of the same age group (p < .05). The two-way ANOVA examining the effect of age and gender (6 × 2) on the Hayling ATT revealed a significant age effect, F(5) = 9.90, p < .0001, ηρ2 = .3143, a nonsignificant gender effect, F(1) = 0.03, p = .872, ηρ2 = .0002, and a non-significant age by gender interaction effect, F(5) = 2.13, p = .0697, ηρ2 = .0898 (see Figure 3).

48.77 19.5 63.35 61.05

0.88 1.51 1.61 2.49

0.67 0.63

1.06 1.03

4.72 3.07 3.8

158.29 141.05 251.46 110.41

1.3 2.99 1.69 3

4.7 3.2

4.7 3.2

14.5 9.1 10

SD

16.3 11.1 9.8

5.2 4

4.6 3.6

1.04 4.35 3.31 3.5

134.77 143.97 236.69 92.72

M

SD

3.65 3.25 2.25

0.63 1.15

0.97 1.07

0.81 2.85 2.54 2.76

32.63 35.44 55.64 58.8

Boys

17.3 9.4 8.1

4.6 3.5

5.1 3.3

0.46 3.38 2.92 3.4

99.2 121.12 228.87 107.75

M

3.5 3.02 1.85

0.97 0.97

0.32 0.48

0.36 2.31 2.11 2.32

33.37 32.67 58.33 49.94

SD

18 10.3 9.5

5.1 4.7

5 3.7

0.63 2.99 2.35 5.9

99.68 119.86 195.75 75.89

M

SD

2.94 3.65 2.76

0.32 0.67

0.82 0.82

0.73 1.56 1.44 4.46

32.86 24.64 46.69 47.92

Boys

2nd year (n = 20)

Girls

20.7 14.2 12

5.2 4.5

5.2 3.8

0.31 1.49 1.18 1.8

89 113.39 175.67 62.28

M

2.94 3.39 3.68

0.42 0.97

0.79 0.63

0.25 0.62 0.72 1.62

24.76 24.47 50.36 56.81

SD

21.1 15.3 12.1

5.8 4.8

5.2 3.8

0.27 1.27 0.99 2.9

86.92 102.6 185.68 83.08

M

SD

2.13 2.75 2.08

1.03 1.13

0.79 1.13

0.19 0.78 0.71 1.85

23.75 22.21 51.48 33.09

Boys

3rd year (n = 20) Girls

21.3 13.3 13.2

5.4 4.5

5.3 4.8

0.26 1.12 0.87 2.7

81.93 106.43 181.23 74.8

M

2.67 4.88 2.2

0.52 0.71

0.82 0.42

0.15 0.53 0.54 2.21

6.35 14.57 39.16 30.49

SD

22.7 14.5 12.5

5.7 4.8

5.4 4.6

0.19 1.01 0.82 2.1

76.09 92.86 170.35 77.48

M

SD

3.06 2.68 2.01

0.67 1.03

0.97 0.97

0.03 0.53 0.5 1.37

13.32 18.25 46.02 34.72

Boys

4th year (n = 20) Girls

21.7 15.7 14.5

6 4.6

5.7 4.7

0.17 1.46 1.29 1.8

77.66 101.23 137.22 35.99

M

4.67 3.74 2.91

0.82 0.7

1.42 0.82

0.02 0.61 0.62 0.92

38.68 26.29 19.35 40.59

SD

23.8 16 13.9

6.3 5.4

5.9 5

0.17 0.91 0.74 2.3

65.57 89.64 145.78 56.14

M

SD

3.73 3.23 4.09

0.67 0.52

1.2 0.67

0.02 0.64 0.64 1.57

7.34 21.99 35.59 20.84

Boys


26.1 19.9 17.6

5.7 5.2

6.1 5.2

0.18 0.88 0.7 1.9

59.7 81.41 127.91 46.49

M

6.23 4.58 4.25

0.48 0.79

0.57 0.63

0.02 0.48 0.48 1.73

9.6 15.89 29.56 23.58

SD

25 19.4 15.6

6.3 5.7

6 5.4

0.18 0.83 0.65 3.1

63.6 82.52 152.55 70.03

M

SD

4.94 4.45 4.4

1.16 0.67

0.67 0.7

0.02 0.42 0.43 2.42

8.57 15.29 47.17 35.74

Boys


Stroop test: Word = response time for the card “Word”; Color = response time for the card “Color”; Color/Word = response time for the card “Color/Word”; Int. Score = interference score; Time A = median response time for part A of the Hayling test; Time B = median response time for part B of the Hayling test; ATT = additional thinking time; Forward DS = forward digit span; Backward DS = backward digit span; BTT-F = forward visuospatial span; BTTB = backward visuospatial span; Animals = number of correct words produced for the category “Animals”; Letter M = number of correct words produced for the letter “M”; Clothing/H = number of correct words produced for alternating between the category “Clothing” and the letter “H”.

Stroop Word Color Color/Word Int. Score Hayling Time A Time B ATT Error Score Verbal Span Forward DS Backward DS Visuospatial Span BTT-F BTT-B Verbal fluency Animals Letter M Clothing/H

M

Girls

1st year (n = 20)

Table 2 Descriptive Data for Executive Indices by Age and Gender.


EXECUTIVE FUNCTION IN TUNISIAN CHILDREN 13

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T. BELLAJ ET AL.

130 120

100 90 80 70 60 50 40 30 20 7

8

9

10

11

12

Age

Figure 1 Age effect on the Stroop interference score.

9 Girls Boys

8 7 Hayling B Mean Error Score


Mean interference score

110

6 5 4 3 2 1 0 –1

7

8

9

10

11

12

Age Figure 2 Age and gender effects on the Hayling errors score progression.


15

4.0 3.5


Additional Thinking Time

3.0 2.5 2.0 1.5 1.0 0.5 0.0 –0.5 7

8

9

10

11

12

Age Figure 3 Development of Hayling additional thinking time with age.

The post hoc Fisher test indicated a significantly (p < .001) higher ATT in the younger children (7- and 8-year-olds) compared to the older children (9- to 12-year-olds). Other differences were non-significant. Thus, compared to older children, younger children take more time to inhibit the dominant responses.

Cognitive Flexibility Development To examine the effects of age and gender on the number of correctly generated items according to the verbal fluency criterion (semantic, phonemic or alternating fluidity), we conducted a three-way ANOVA (Age × Gender × Criterion: 6 × 2 × 3). Our comparisons revealed a significant age effect, F(5) = 27.78, p < .0001, ηρ2 = .5626, a nonsignificant gender effect, F(1) = 0.79, p = .3763, ηρ2 = .0072, and a non-significant age by gender interaction, F(5) = 0.45, p = .8113, ηρ2 = .0205. In addition, we found a significant criterion effect, F(2) = 324.87, p < .0001, ηρ2 = .7505, which significantly interacted with age, F(10) = 2.01, p < .05, ηρ2 = .0852, but not with gender, F(2) = 1.98, p = .14, ηρ2 = .018. Lastly, the age by gender by criterion interaction was not significant, F(10) = 0.39, p = .9478, ηρ2 = .018 (see Figure 4). The post hoc Fisher’s LSD test revealed that the number of words correctly produced in semantic fluency condition was significantly lower for the 7-year-old children in comparison to the older children (p < .05). The 8-year-old children also had lower scores than the older children (p < .01); a similar marginal effect was observed between the 9- and 12-yearolds (p < .0001), 10- and 12-year-olds (p < .01), and 11- and 12-year-olds (p < .05).

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T. BELLAJ ET AL.

30 28 26

Semantic fluency Letter fluency Alternate fluency


Mean words production

24 22 20 18 16 14 12 10 8 6 4

7

8

9

10

11

12

Age Figure 4 Progression of number of correctly generated items according to age and criterion on fluency tests.

In phonemic fluency, the number of words correctly generated was lower in the group of 7-year-old children in comparison to the 9- to 12-year-olds (p < .001). The differences were also significant between the 8-year-olds and the older age groups (p < .001). The 9-year-olds produced fewer words than the 12-year-olds (p < .0001), the 10-year-olds less than the 12year-olds (p < .0001), and the 11-year-olds less than the 12-year-olds (p < .001). In alternating fluency, the number of words correctly generated was significantly lower for the 7-year-olds in comparison to the 10-, 11- and 12-year-olds (p < .01). Their performance also tended to be less successful than the 9-year-olds (p = .056). The 8-yearolds correctly generated less words than the older children (p < .01). The 9-year-olds tended to perform less successfully than the 11-year-olds (p = .056) and deviated significantly from the performance of the 12-year-olds (p < .0001). Finally, the 10- and 11-year-olds correctly generated less words than the 12-year-olds (p < .001 and p < .05, respectively). In addition, the post hoc analysis confirmed a significant criterion effect. Regardless of age, children produce more words in semantic fluency (SVF) than in phonemic (PVF) and alternating fluency (AVF) (SVF: M = 20.71 and σ = 5.07; PVF: M = 14.02 and σ = 4.87; AVF: M = 12.40 and σ = 4.02; with p < .0001). On the other hand, better scores in phonemic fluency as compared with alternating fluency were significant only at the age of 9 (PVF: M = 14.75 and σ = 3.06; AVF: M = 12.05 and σ = 2.91; with p < .01) and 12 years (PVF: M = 19.65 and σ = 4.40; AVF: M = 16.60 and σ = 4.33; with p < .001). At the age of 11, this difference is on the border of statistical significance (p = .0504). In addition, the performance of children in semantic fluency is regularly high regardless of age, compared with the performance of children in the phonemic and alternating verbal fluency tasks (p < .05).


17

Working Memory Development To test age and gender effect on the word digit span task in its two conditions (forward vs backward), we conducted a three-way ANOVA (Age × Gender × Condition: 6 × 2 × 2). A significant age effect was observed, F(5) = 18.36, p < .0001, ηρ2 = .4594, while gender effect and its interaction with age were non-significant, respectively F(1) = 0.56, p = .455, ηρ2 = .0053 and F(5) = 0.14, p = .983, ηρ2 = .0061. Moreover, the condition effect has proved to be significant, F(1) = 149.51, p < .0001, ηρ2 = .5806, as well as interaction with age, F(5) = 2.77, p < .05, ηρ2 = .1136. Other interactions were non-significant: condition by gender, F(1) = 1.05, p = .3068, ηρ2 = .0097, and condition by age by gender, F(5) = 0.51, p = .7678, ηρ2 = .0231 (see Figure 5).

7.0 6.5

Forward digit span Backward digit span

6.0 5.5 Mean digit span


A non-significant age effect was noted for the fluency perseveration number, F(5) = 0.92, p = .473, ηρ2 = .0407. Gender effect was also non-significant, F(1) = 0.72, p = .398, ηρ2 = 0.0066, as well as the interaction between these two variables, F(5) = 1.01, p = .4149, ηρ2 = .0447. However, a significant effect of criterion was found, F(2) = 23.26, p < .0001, ηρ2 = .1772, while interaction with age was no-significant, F(10) = 0.43, p = .9316, ηρ2 = .0195, as well as the interaction with gender, F(2) = 0.04, p = .9552, ηρ2 = 0.0004. Lastly, the interaction effect of criterion by age by gender was nonsignificant, F(10) = 1.63, p = .1004, ηρ2 = .07. Post hoc analysis indicated that the mean number of perseverations in semantic fluency was higher than that of the other criteria (p < .001), while this number in alternating fluency was higher than that of the phonemic fluency (p < .05).

5.0 4.5 4.0 3.5 3.0 2.5 7

8

9

10

11

12

Age Figure 5 Progression of word digit span by age, gender, and condition.

T. BELLAJ ET AL.

Post hoc analysis revealed that the forward digit span was significantly lower for the 7-year-old children in comparison to the 9- to 12-year-olds (p < .05). This difference was also significant between the 8-year-olds and the 11- and 12-year-olds (p < .01), and between the 9-year-olds and the 11- and 12-year-olds (p < .05). These differences are again significant between the 10- and 12-year-olds (p < .01). The backward digit span improves between the ages of 7 and 10 years and between the ages of 11 and 12 years (p < .0001). The scores of the 8-year-old children were also lower than those of the 10- to 12-year-olds (p < .0001). Differences were again significant for the 9-year-olds compared to the older age groups (p < .001). The same results were found for the 10-year-olds compared with the 12-year-olds (p < .05). Regarding the condition effect, regardless of age, forward digit span was higher than reverse ones (p < .01). A three-way ANOVA (Age × Gender × Condition: 6 × 2 × 2) for the visuospatial span revealed a significant effect of age, F(5) = 14.08, p < .0001, ηρ2 = .3946, and gender, F(1) = 19.77, p < .0001, ηρ2 = .1548, while the interaction effect was non-significant, F(5) = 0.36, p = .8722, ηρ2 = .0166. The condition effect was significant, F(1) = 116.11, p < .0001, ηρ2 = .5181, unlike the interactions with age, F(5) = 1.85, p = .1094, ηρ2 = .0788, gender, F(1) = 1.14, p = .288, ηρ2 = 0.004, and both age and gender, F(5) = 0.82, p = .5355, ηρ2 = .04367 (see Figure 6). The Fisher LSD showed an overall better performance for boys than for girls in visuospatial spans (p < .001). It also indicated that, at all ages, performance in the forward

7.5 Girls Boys

7.0 6.5 Mean visuospatial span


18

6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 Age

8 7

10 9

Forward

12 11

Age

8 7

10 9

12 11

Backward

Figure 6 Progression of visuospatial span by age, gender, and condition (direct vs reverse).


19


visuospatial span condition was better than that in the backward visuospatial span (p < .05). Age effect is observed almost linearly along different age groups. For BTT-F, visuospatial span improvement with age was significant between the 7-year-old children and all other age groups (p < .05) except for the 8-year-olds, who were less efficient than the older age groups (p < .01). Likewise, the visuospatial span of the 9-year-olds was lower than that of the 11- and 12-year-olds (p < .05). In addition, the 10-year-olds were less efficient than the 11-year-olds (p < .05). The visuospatial span did not significatly differ between the 11- and 12-year-olds. For BTT-B, the visuospatial span evolvement with age was significant between the 7-year-old children and all other age groups (p < .001), and between the 8-year-olds and the older groups (p < .01). The performance was less efficient for the 9- and 10-year-olds compared to the 12-year-olds (both values of p < .01). No significant difference was identified between the performance of the 11- and 12-year-olds.

Correlation Analyses Correlation analyses between the educational level of the father and executive scores were generally non-significant, with the exception of the backward digit span (p < .01) and Hayling ATT (p < .05). All correlations between executive scores and educational level of the mother were non-significant. Table 3 contains the whole set of correlations. The correlations analysis between the executive scores and the overall intellectual level identified significant correlations for all measures (see Table 3). After controlling for age, previously correlations became moderate and non-significant for the forward digit span, Stroop interference score, and alternating fluidity index (p > .05).

Table 3 Correlations between Executive Scores and Educational Level of Parents.

Variables Forward digit span Backward digit span Visuospatial forward span Visuospatial backward span Stroop interference score Hayling additional thinking time Simple semantic fluency Simple letter fluency Alternating fluency

Intelligence (Raven Matrices) r = .22 p < .05 r = .51 p < .0001 r = .41 p < .0001 r = .46 p < .0001 r = −.24 p < .01 r = −.45 p < .0001 r = .42 p < .0001 r = .46 p < .0001 r = −.39 p < .0001

Mother’s education level r = −.099 p = .284 r = .01 p = .884 r = .08 p = .376 r = .04 p = .65 r = −.103 p = .261 r = −.09 p = .303 r = .03 p = .703 r = .03 p = .743 r = −.04 p = .63

Father’s education level r = .09 p = .328 r = .24 p < .01 r = .15 p = .094 r = .07 p = .423 r = −.17 p = .07 r = −.199 p < .05 r = .135 p = .142 r = .036 p = .700 r = .049 p = .596


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Partial correlational analyses showed that inhibitor measures (interference score and ATT) proved to be independent (r = .07, p = .44). On the other hand, verbal fluency scores appeared, on the whole, significantly correlated: semantic fluency and phonemic fluency (r = .4, p < .0001), semantic fluency and alternating fluency (r = .42, p < .0001), phonemic fluency and alternating fluency (r = .54, p < .0001). Forward and backward verbal digit spans were also significantly correlated (r = .28, p < .01), as well as corresponding scores of visuospatial span (r = .43, p < .0001). Correlations between backward digits and visuospatial spans were also significant (r = .26, p < .01). Partial correlations analysis between the various executive processes showed that the Stroop interference score was significantly correlated with the backward visuospatial span (r = −.18, p < .05). The Hayling ATT was significantly correlated with alternating verbal fluency (r = −.22, p < .05) and visuospatial spans (BTT-F, r = −.26, p < .01; BTT-B, r = −.26, p < .01). In addition, semantic fluency was significantly correlated with backward digit span (r = .28, p < .01) and visuospatial spans (BTT-F, r = .3, p < .01; BTT-B, r = .18, p < .05). The same result was found for the phonemic fluency (span-E, r = .25, p < .01; BTT-F, r = .18, p < .05; BTT-B, r = .25, p < .01). Alternating fluency was correlated with most of the executive scores: backward digit span (r = .37, p < .0001), visuospatial spans (BTT-F, r = .23, p < .05; BTT-B, r = .18, p < .05), and Hayling ATT (r = .22, p < .05). DISCUSSION The main aim of the current study was to use a cross-sectional design to examine the developmental trend of three domains of executive control (prepotent response inhibition, cognitive flexibility, and working memory) and to investigate their mutual interactions in school-age Tunisian children (Brocki & Bohlin, 2004; Lehto et al., 2003). Secondary objectives included the assessment of the effects of gender and parents’ education level because of their potential roles in the development of executive control abilities (Ardila et al., 2005; Armengol, 2002; Brocki & Bohlin, 2004; Klenberg et al., 2001; Levin et al., 1991). Finally, we analyzed relations between fluid intelligence and the development of executive processes based on the hypothesis of possible links between these two domains of competence (Arffa, 2007). The study population was matched for age, gender, parents’ education level, and fluid intelligence level obtained by Raven Matrices. This methodological control was necessary to avoid socio-demographic biases and to ensure the homogeneity of the groups because individual differences among the children may contribute to variance (Hughes & Graham, 2002; Kline, 1998; Miyake et al., 2000). For the Hayling test, the Tunisian children’s results are consistent with the data obtained by Shallice et al. (2002), who found an overall improvement in ATT and the Error index between 6 and 12 years of age. Furthermore, it is important to note that with regard to the evolution of the interference index in the Stroop test, the performance of the Tunisian children was relatively close to the performance reported for Spanish-speaking children attending private schools. Other work has found similar comparisons (Armengol & Méndez, 1999; Comalli et al., 1962). However, qualitative analysis of the performance of the Tunisian children on various cards of the Stroop test revealed some peculiarities. Based on the average number of corrected errors on the various cards, it was possible to note a trade-off effect: children balanced response quality and response speed. Indeed, the



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percentage of 12-year-old children who committed four or more corrected errors was approximately equal to the percentage of 7-year-old children who committed the same number of corrected errors, regardless of the proposed Stroop card (for example, on the interference card, 65% of 12-year-old children committed more than four corrected errors, whereas this percentage was only 55% for the 7-year-olds). Regarding the variation in error scores on the Hayling test with respect to the age of the Tunisian children, our results confirm the data obtained by Shallice et al. (2002) in a study of the executive profiles of children with attention deficit disorder with hyperactivity. As in our study, Shallice et al. found an overall improvement in the index between 6 and 12 years of age. Other studies based on different paradigms have also found an overall improvement of performance in school-age children on tasks that assess different aspects of inhibitory processes. For example, working with a sample of 52 7- to 15-year-old subjects, Levin et al. (1991) observed improved indices of errors of commission and errors of omission in a Go/No-Go task, with a particularly noticeable improvement between the older children (7- to 8-yearolds and 9- to 12-year-olds; no significant differences were found in the 13- to 15-yearolds). Using a stop-signal task, Williams et al. (1999) also observed an increase in the rate of predominant response inhibition between 6- to 8-year-olds and 9- to 12-year-olds. Based on a study of 92 6- to 13-year-old children that evaluated the development of different dimensions of EF, Brocki and Bohlin (2004) observed improved inhibitory capacity using a battery of tests, including the Stroop test. However, they noted that the most striking improvement occurred between 7 years and 6 months and 9 years and 5 months, and between 9 years and 6 months and 11 years and 5 months. Finally, working with a sample of 170 8- to 12-year-old children, Sevino (1998) found improved performance on the Stroop test, with an attenuated level of maturity at 12 years of age compared to adults. Moreover, developmental data resulting from comparisons carried out for tasks of simple and alternating verbal fluency revealed significant overall improvement in spontaneous and reactive flexibility skills between 7 and 12 years of age, in accordance with data reported in the literature. Using similar semantics and phonemic verbal fluency tasks to assess spontaneous flexibility (Anderson et al., 2001; Brocki & Bohlin, 2004; Klenberg et al., 2001; Lehto et al., 2003; Levin et al., 1991; Sevino, 1998; Welsh et al., 1991) and an alternating fluency task to assess reactive flexibility (Sevino, 1998), an overall improvement with age was observed in school-age populations, albeit with differing maturity peaks from one study to another. A steady improvement of cognitive capacities was recorded using other tasks, such as figural fluency (Klenberg et al., 2001; Levin et al., 1991), the WCST (Welsh et al., 1991), the Brixton test (Shallice et al., 2002) and the Trail Making Test (Sevino, 1998). By analyzing the behavioral profiles of Tunisian children with respect to semantic and phonemic fluency, it is clear that the strategies used for word retrieval depend on the nature of the task and on the child’s age. In addition, the performance of Tunisian children with respect to verbal semantic fluency seems to pass through three successive developmental stages: from 7 to 9 years of age, improvement; from 10 to 11 years, stagnation; from 11 to 12 years, further improvement in the average number of correctly produced words. With respect to phonemic fluency, we observed two developmental stages: 7 to 8 years of age and 9 to 12 years. Subsequent qualitative analysis would be needed to better understand these variations and to learn about the underlying processes. Furthermore, regarding the evolution of the number of words produced in an alternating influence task, few studies have been conducted on this subject. In a study of 170 8- to


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12-year-old children, Sevino (1998) found a gradual improvement in the index with age, with a peak of maturity at the age of 12; these results are broadly consistent with the data we obtained. However, the evolution of this index remains relatively low compared to those of semantic and phonemic fluency. Additional qualitative analyses will be needed to highlight the processes underlying this index. In addition, phonemic fluency performance remained significantly lower than performance for semantic fluency, regardless of age. Kavé (2006) attributes these differences in performance on the phonemic and semantic fluency tasks to the fact that during development, children tend to use different strategies for word retrieval. Weaker performance on the phonemic and alternating fluency tasks compared to the semantic fluency task may also be because: (1) they require more cognitive resources; (2) they are less routine than the semantic fluency task, which is a more ecological task based on natural categorizations that are available from an early age; (3) they are more sensitive to shifting capacities dependent on maturation of the frontal lobes (Martin, Wiggs, Lalonde, & Mack, 1994; Mummery, Patterson, Hodges, & Wise, 1996); and (4) they are in part dependent on the level of literacy (Kremin & Dellatolas, 1996), whereas semantic fluency skills are based on the natural tendency to categorize that is present from an early age (Kavé, 2006; Kavé et al., 2008). Additionally, the number of errors, especially repetitions, increased more or less constantly with age and was relatively high in the semantic fluency task. Children seemed to compromise between the speed and accuracy of their responses (trade-off effect). Developmental data from comparisons of working memory revealed a relatively constant increase in performance on the verbal and visuospatial digit span tasks in 7to 12-year-old children. This increase was more obvious when we compared the performances of the oldest and youngest children. Moreover, higher performances were observed for the forward-order span tasks compared to the backward-order tasks. Indeed, the backward-order verbal and visuospatial span tasks are working memory tasks because they require the child to perform two consecutive tasks: to temporarily retain the digit sequence in the presented order (verbal or visuospatial working memory), then recall the sequence in the reverse order. This requirement represents a cognitive load for the child. A reduction of this difference with age is revealed by the verbal and visuospatial working memory improvement that has been largely described in school-age children (Brocki & Bohlin, 2004; Hale et al., 1997). Brocki and Bohlin (2004) noted a gradual and steady improvement in performance on the forward and backward digit span tasks between 6 and 13 years of age, with better performance on the forward span than the backward. Hale et al. (1997) also found a significant and sustained improvement in span between different child and adolescent age groups, with better performance for verbal than visuospatial span (see Gathercole et al., 2004; Luciana et al., 2005). Our results are consistent with other studies that have used different paradigms, such as the motor sequence subscale of the Kaufman Assessment Battery for Children, a duration reproduction lighting a torch task (Brocki & Bohlin, 2004), the Cambridge Neuropsychological Test Automated Battery (Lehto et al., 2003), the Tic-Tac-Toe Task, the mental counting task, and the mental running task (Huizinga et al., 2006), all of which have indicated that working memory capacities improve, but have suggested different peaks of maturity. Differences in the developmental schedules and trajectories of executive indices could be explained by differing population characteristics and by task effects. All executive tasks involve complex and effortful tasks that require both executive and



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non-executive processes (Chan, Shum, Toulopoulou, & Chen, 2008). Further studies will be needed to elucidate the contribution of these non-executive processes to the variance and to analyze the strategic behavior of children when they are performing executive tasks. Concerning the effect of gender on the variation of the performance of executive tasks in children, the comparisons did not reveal significant differences, except for the visuospatial span task, for which boys had an overall higher span than girls. These results are consistent with those obtained by Klenberg et al. (2001) in their study of developmental differences in attentional and executive processes in a sample of 400 3- to 12-yearold Finnish children. They also found a significant effect of gender on working memory capacity. It is important to note that the literature regarding the effect of gender on the variation of the performance of school-age children on tasks assessing EF is not uniform. Some studies have found either a non-significant effect (Arán-Filippetti & Allegri, 2011; Armengol, 2002; Brocki & Bohlin, 2004; Hurks et al., 2010; Welsh et al., 1991) or, conversely, a significant effect in favor of girls (Anderson et al., 2001; Ardila et al., 2005; Berlin & Bohlin, 2002; Carlson & Moses, 2001; Klenberg et al., 2001; Levin et al., 1991) or in favor of boys (Becker et al., 1987; Capitani et al., 1999; Halpern, 2000). Our results support the idea of global behavior equivalence between girls and boys with respect to executive competencies (Brocki & Bohlin, 2004; Lehto et al., 2003; Welsh et al., 1991). Similarly, the correlations between the parents’ education level and the children’s performance on the executive tasks were not significant. Only the education level of the father was significantly correlated with the mean inhibitory processing time and with the backward digit span. While the limited effect of parental level of education observed in our study contradicts certain previous results (Ardila et al., 2005; Klenberg et al., 2001), it is consistent with the results of other studies which showed that this factor has little influence on executive development (e.g., Armengol, 2002; see Gioia, Isquith, Guy, & Kenworthy, 2000). It is important to note that the correlations observed by Ardila et al. (2005) between parents’ level of education and executive performance were not all significant. The strongest correlation did not exceed the significance level of .05, even though the study population included approximately 622 children. The uniquely robust differences found for the EF test scores were between private and public schools. For Armengol (2002), the educational level of the father and mother predicted only 3% of the variance of the interference index. Moreover, this relative independence should not mask the potential influence of parental educational practices as well as the role of sociolinguistic and cultural factors (Bindman, Hindman, Bowles, & Morrison, 2013; Hoff, 2003a, 2003b) in the general cognitive development of the child. The analysis of the correlations between the executive indices and the general intellectual level (fluid intelligence, Raven Matrices) supports the existence of at least one common process between these two domains of competence. The significant correlations between the g factor and the executive indices confirm the inclusion of EF in a larger intellectual system (Arffa, 2007), even after controlling for age. These data support the idea of a conceptual overlap between fluid intelligence and EF (see Blair, 2006). After controlling for age, the analysis of inter-task correlations showed significant moderate links between overall intra-procedure scores for both flexibility and working memory (median correlations of r = .41 and .27, respectively). Only the analysis of the inhibitory processing indices (interference score and ATT) showed non-significant correlations (median correlation = .07), which could reflect the dissociations previously


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reported in the literature (Friedman & Miyake, 2004). According to these dissociations, inhibition would be a multifactorial process including three fundamental functions: filtering, suppression, and blocking. The Stroop test reflects the filtering function, whereas in the Hayling task, the participant is required to simultaneously activate the functions of suppression and blocking (Chiappe, Siegle, & Hasher, 2000; Lorsbach, Katz, & Cupak, 1998). Moreover, the grouped data of different tasks that assess cognitive flexibility and working memory would be in favor of dissociable functions independent of content (Baddeley, 1986; Shallice, 1988). Concerning the relations between the indices that assessed distinct executive processes (inter-process), weaker but significant correlations were found, with a median correlation lower than that observed between tasks that call on the same process (r = .16). These results support the presence of one or many common mechanisms underlying EF (Miyake et al., 2000), the nature of which remains to be elucidated (Best et al., 2009; Huizinga et al., 2006). Thus, the results of the partial correlation analyses seem to be in accordance with contemporary data obtained among children and adults that supports the idea of separate but interrelated functions. Indeed, using exploratory and confirmatory analyses performed on executive measures, Lehto et al. (2003) have identified three relatively independent but interrelated factors of working memory (combining the scores for WISC-III Mazes; spatial working memory; spatial span; NEPSY Auditory Attention and Response Set), inhibition (Matching Familiar Figures Test; Tower of London) and finally, shifting (verbal fluency; Trail Making Test-B). Working with an adult population, Miyake et al. (2000) considered the separability of three EF: mental set shifting (“Shifting”), information updating and monitoring (“Updating”) and the inhibition of prepotent responses (“Inhibition”) and their roles in complex “frontal lobe” or “executive” tasks in 137 college students. Confirmatory factor analysis indicated that the three target EF are moderately correlated with one another, but are clearly separable. From this point of view, the presence of many EF, partially independent but closely related and in permanent interaction, can gain legitimacy based on the concept that EF involves the coordination of mental processes. Because one of the motivators of this study was the lack of empirical data on the development of EF in African and Arab countries, and even though we did not perform a classic cross-cultural comparative study, it is interesting to compare our results to studies that tested EF in children from European, North American, Latin American, and Asian cultures. In fact, many comparisons were possible because the studies used similar age intervals, selected participants according to similar criteria, and utilized the same tests with similar procedures. For the Stroop test, comparing the Tunisian results to Spanishspeaking 6- to 12-year-old Mexican children showed concordance regarding the effect of age on cognitive inhibition; very similar scores on the Stroop interference index were found for the different ages. For cognitive flexibility, most of the work conducted with populations of children using different languages and of different cultures have also found a gradual improvement in verbal fluency skills (phonemic and semantic) during childhood and adolescence, albeit with lower performance on the phonemic fluency task compared to the semantic fluency task. For example, working with a population of Hebrew children, Kavé (2006) noted a steady and gradual improvement in the average number of words correctly generated in three tasks of semantic fluency (animals, fruits and vegetables, vehicles) and phonemic fluency (B, G, S). As expected, performance in semantic fluency was superior to that in phonemic fluency. Arán-Filippetti and Allegri (2011) also



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evaluated the performance of fluent Spanish-speaking 8- to 11-year-old children on a semantic fluency task (animals) and three phonemic fluency tasks (F, A, S). They observed an increased number of words generated in the semantic fluency task relative to the task of phonemic fluency. In addition, all of the evidence found was significantly stronger for the group of older children compared to the younger group. In a recent study, John and Rajashekhar (2014) used a task of semantic fluency (animals) to assess capacitybuilding recovery and the generation of words in 5- to 15-year-old Malayalam-speaking Indian children. The data obtained also showed a gradual improvement in the number of words correctly produced. Furthermore, regarding the evolution of the number of words produced in an alternating influence task, few studies have been conducted on this subject. However, Sevino (1998) found a gradual improvement in the index with age, with a peak of maturity at 12 years of age, which is broadly consistent with the data we obtained. However, the evolution of this index was relatively low compared to the previous two (the indices of semantic and phonemic fluency). Additional qualitative analyses will be needed to highlight the processes underlying this index. In accordance with our results, the effects of gender on EF performance observed in other cultures have been rare and weak. In contrast, a correlation between performance on executive tests and IQ have consistently been found significant and of similar magnitude (Ardila et al., 2000). The influence of culture on EF performance and development has been primarily discussed according to two themes: the impact of bilingualism and the effects of social models and experience. Concerning the impact of bilingualism on EF, Bialystok (1999, 2001) found that bilingual children who are simultaneously exposed to two languages from an early age have more developed executive skills than monolingual children. These findings have been reinforced by the work of Green (1998), Carlson and Meltzoff (2008), Luo, Luk, and Bialystok (2010), and Morales, Calvo, and Bialystok (2013), who observed generally higher executive performance in bilingual children compared to monolingual children with respect to concept formation, mental reorganization, abstract and divergent thinking, mental flexibility, cognitive inhibition and working memory. Working with Arabic bilinguals, Soliman (2014) and Abdelgafar and Moawad (2014) confirmed these conclusions in part and noted a lack of consensus regarding these effects (see Baker, 2011). In fact, the debate is still open due to the many methodological pitfalls possible when dealing with the effects of bilingualism on cognition (differences in or a lack of control over parents’ socioeconomic and socio-educational level, the degree of participants’ proficiency in each language, frequency of use of each language, and the language of assessment). Regarding the effect of social models and experience on EF, Lewis, Koyasu, Ogawa, Short, & Huang (2009) believe that EF has a basis in social interaction and described studies that show more developed self-control in northeast Asian children compared to their North American peers (Sabbagh, Xu, Carlson, Moses, & Lee, 2006). It is well known that Chinese educators and parents emphasize the importance of respect and self-control in everyday conduct (Chao & Tseng, 2002). Empirical results have shown an advantage of Korean children on inhibition (conflict inhibition and delay inhibition tests) and switching measures (alternating fluency and the Dimensional Change Card Sort tests), but not in working memory measures (backward span) compared to English children (Oh & Lewis, 2008). According to Lewis et al. (2009), the Confucian culture widely adopted in Chinese, Korean, and Japanese societies may contribute to the improved performance on executive tasks. What about the specifics of EF development in Tunisian and Arabic cultures in general? Because there have not been any cross-cultural studies that directly target EF and that compare populations of children from such cultural


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backgrounds to others, no conclusions can be drawn. Nevertheless, it is important to note that the results obtained for the Tunisian population cannot be generalized to other Arabic or African countries unless future research supports it. In fact, even if Tunisia recognizes “Islam as its religion and Arabic as its language” according to the Tunisian Constituent Assembly (2014, p. 3), it is important to note that it chose to be a republic based on the rule of “civil” law. It is also a culture that integrates Berber, Punic, African, Roman, Arab, Othman and French influences, in addition to a long history of Judaic, Christian and Islamic belief systems. Polyglotism is the main feature of the Tunisian language; the Arabic Tunisian dialect is the mother tongue, followed by standard Arabic, French, English and Italian, each with a specific grammar and vocabulary. However, the culture is open to modernity while remaining attached to its traditional roots. Tunisians have a unique culture seeped in Arabic culture and Islamic rituals and heritage. The country is deeply rooted in the African continent and very much influenced by French philosophy and literature and the French style of life. This is especially obvious in the everyday practices of Tunisians, seen and observed only by insiders and those who deeply immerse themselves in Tunisian society. This is similar in some respects to South Korean culture, in which increasing Westernization conflicts with the daily practice of collective traditions and in which traditional beliefs are being intellectually influenced by Western philosophies (Kwon, 2002). However, it seems interesting to conduct crosscultural studies of the efficiency of EF by comparing different gradients of cultural similarities and differences. Limitations The current study has some limitations, so the results must be interpreted with caution. The first issue is that of low construct validity, which refers to the possibility of executive tasks mapping onto a multitude of cognitive processes, executive as well as non-executive, which makes the development of individual elements of the processes difficult to identify. In addition, because there is not an agreed-upon task or set of tasks used to assess each executive domain, it is likely that the developmental change reflected in specific tasks represents different aspects of certain executive domains (Welsh, 2002). This means that previous evidence of developmental changes in EF based on single tests runs the risk of being task dependent. Because these processes are essential to control, we tried to compare each process with a more basic task that requires less executive control resources (naming and word reading for the Stroop and verbal initiation for the Hayling and forward digit span) and evaluate each component based on two executive tasks (Inhibition for Stroop and Hayling; cognitive flexibility for simple semantic and simple phonemic alternating fluency; and working memory for verbal and visuospatial forward and backward span). Moreover, it should be noted that many response strategies are possible for nonroutine tasks, so it is sometimes difficult to estimate performance differences between subjects because of differences in the strategies implemented in response to the proposed tasks. This is particularly true when the subjects’ difficulties are expressed as a decrease in reaction time and/or an increase in the number of errors. In the current study, the verbal fluency tasks in children were characterized by a compromise between the quantity of words named in 120 seconds and the quality of the words produced, with a high number of repetitions. The children did not develop a relevant strategy for retrieval and encoding in long-term memory; instead, they focused on the speed of word production. However,



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these findings are not definitive, and further research is needed to highlight the nature of the strategies used by children for the recovery of words from long-term memory, such as the work that has previously addressed the issue (Arán-Filippetti & Allegri, 2011; John & Rajashekhar, 2014; Kavé et al., 2008; Sauzéon et al., 2004). Qualitative analyses based on the evolution of switching indices and clustering are therefore necessary. Another aspect that was not considered in this work was the influence of sociodemographic variables such as the parents’ socioeconomic status (SES), the potential effects of which on the variation in cognitive performance of children have already been highlighted in previous work. Indeed, links have been suggested between cognitive development and socioeconomic level in different countries (Arán-Filippetti & Richaud De Minzi, 2012; Kohen, Gunn, Leventhal, & Hertzman, 2002). For example, Hoff (2003b) found that children from high-SES families have larger productive vocabularies than children from mid-SES families. The influence of environmental factors has also been shown for the development of other cognitive abilities (e.g., Ardila & Rosselli, 1994). Turkheimer, Haley, Waldron, D’Onofrio, & Gottesman (2003) suggested that the proportion of IQ variance attributable to familial background and environment vary nonlinearly with SES. Thus, further work will be needed to assess the influence of this factor on the development of executive skills among Tunisian school children. Another limitation of this study was the use of a cross-sectional design to document the development achieved with respect to the ages examined. However, although longitudinal studies offer a more valuable design for studying development, cross-sectional studies offer the opportunity to study and compare wide domains of development (Korkman, 2001). In addition, several studies have used this design to study EF development in children (Anderson et al., 2001; Brocki & Bohlin, 2004; Klenberg et al., 2001; Lehto et al., 2003; Levin et al., 1991; Sevino, 1998; Welsh et al., 1991). Moreover, only children between 7 and 12 years of age were included. Because the development of EF is not confined to the ages studied, it is not feasible to precisely document the age at which adult performance on tasks of EF is reached using this method of analysis. Nevertheless, previous studies (Brocki & Bohlin, 2004; Lehto et al., 2003; Sevino, 1998) have used an age range similar to that of our study, strengthening the ability to generalize the results of the ages evaluated here. Finally, the relatively small sample size of each age group makes it difficult to perform adequate statistical analysis to examine the influence of the two primary variables, age and gender. However, this limitation is not insurmountable as the statistical power could be considered sufficient due to the multiple data points collected for each group. CONCLUSION The results of this first study focusing on EF development in Tunisian school-age children demonstrate a dynamic progression. This development is heterogeneous and marked by peaks of maturity, which vary according to the type of process assessed and sometimes according to the task used. This diversity of behavioral profiles strengthens the developmental dynamics that motivate the behavior of school-age children and supports the results of international studies conducted on non-African, non-Arabic-speaking children (Brocki & Bohlin, 2004; Klenberg et al., 2001; Lehto et al., 2003; Levin et al., 1991; Welsh et al., 1991). Moreover, while gender and parents’ education level seem to have little impact on the development of EF in Tunisian children, the interdependence between

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EF and fluid intelligence confirms the involvement of executive control in child cognitive development.


Future Perspectives From a prospective point of view, the current study of Tunisian children suggests a certain universality of the development of EF. This concern represents an important theoretical and clinical challenge. Only a truly cross-cultural study which identified the different sources of bias, such as construct bias, method bias, and item bias, and which ensured structural equivalence, measurement unit equivalence, and full-scale equivalence (Van Devijver & Tanzer, 2004) would allow clarification of this issue. An ecological perspective should be integrated in order to obtain a more contextual perspective of the development of EF in children in different cultures. One way of achieving this goal is through the use of everyday questionnaires such as the Behavior Rating Inventory of Executive Function (Gioia et al., 2000). Moreover, it would also be useful to consider a cross-cultural study of EF in terms of “hot and cool executive functions” (Brock, Rimm-Kaufman, Nathanson, & Grimm, 2009; Hongwanishkul, Happaney, Lee, & Zelazo, 2005). To study the relationship between culture and psychological variables, two fundamental aspects would need to be addressed: (a) the degree of interaction between the psychological and cultural aspects of each process; and (b) how changes in psychological processes are affected by the influence of specific cultural contexts. Then it would be possible to understand the nature of the relationship that could exist between human behavior and the cultural context in which it developed. This understanding would require a conceptual redefinition and delimitation of the psychological construct of EF and a readjustment of the ecological footprints and features that characterize the sociocultural environment of Tunisia. A final issue to overcome is that of task impurity. Miyake et al. (2000) presented one way of addressing this problem. They proposed using multiple tasks to measure each EF component and adopting the latent variables approach to extract the variance common to those tasks. Latent variables (as incorporated in structural equation models) refer to what is shared among tasks that are assumed to tap into a given EF. The latent variable approach minimizes the task impurity problem and is therefore especially informative in developmental studies (e.g., Nunally & Bernstein, 1994). Further work based on analyses of this type would be interesting to highlight the underlying mechanisms of EF. Original manuscript received 17 June 2014 Accepted manuscript 29 May 2015 First published online 7 July 2015

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Executive functioning in children with developmental dyslexia.

Executive Functioning in Children and Adolescents With Perinatal HIV Infection.

Executive functioning skills in preschool-age children with cochlear implants.

Improving executive functioning in children with fetal alcohol spectrum disorders.

Fathers' sensitive parenting and the development of early executive functioning.

[Development of executive functions in preterm children].

Everyday psychological functioning in children with unilateral cerebral palsy: does executive functioning play a role?

Executive functioning in highly talented soccer players.

Unraveling Executive Functioning in Dual Diagnosis.

Characteristics of Executive Functioning in a Small Sample of Children With Tourette Syndrome.

Stability of executive functioning measures in 8-17-year-old children with unilateral cerebral palsy.

Preliminary functional MRI neural correlates of executive functioning and empathy in children with obstructive sleep apnea.

Cross-sectional study of executive functioning in children with developmental coordination disorders.

Rule-based category learning in children: the role of age and executive functioning.

Differences in Executive Functioning in Children with Attention Deficit and Hyperactivity Disorder (ADHD).

The Association between Parenting Behavior and Executive Functioning in Children and Young Adolescents.

Longitudinal and concurrent links between memory span, anxiety symptoms, and subsequent executive functioning in young children.

Attention skills and executive functioning in children with Noonan syndrome and their unaffected siblings.

Associations between daily physical activity and executive functioning in primary school-aged children.

Sleep, executive functioning and behaviour in children and adolescents with type 1 diabetes.

Development and preliminary reliability of a multitasking assessment for executive functioning after concussion.

Executive functioning of 4 children with hyperphenylalaninemia from childhood to adolescence.

Executive Functioning in Subtypes of Attention Deficit Hyperactivity Disorder.

Self-Awareness of Executive Functioning Deficits in Adolescents With ADHD.