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عنوان فارسی مقاله:
اثر متفاوت عوامل موثر بر رشد شناختی در سن 5 تا 6 سال
عنوان انگلیسی مقاله:
Differential effects of factors influencing cognitive development at the age of 5-to-6 years
سال انتشار : 2016
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مقدمه انگلیسی مقاله:
1. Introduction
Research on intelligence has established that performance in a wide variety of cognitive tasks is positively correlated (across individuals), such that it is possible to explain a substantial part of variance in all tests by one factor, called g for general intelligence (Spearman, 1904; Sternberg & Grigorenko, 2002). Several models have proposed breaking down intelligence into different dimensions such as the verbal/nonverbal model(Ramsden et al., 2011),the crystallized/fluid intelligence model (Cattell, 1971) or even more complex ones (e.g., Carroll’s three-stratum (Carroll, 1997) and the Cattell–Horn–Carroll models (Flanagan, Ortiz, & Alfonso, 2013) models). Although the effects of many environmental factors on general intelligence have been extensively studied (Belfort et al., 2013; W. Eriksen, Sundet, & Tambs, 2010; Poulsen et al., 2013; Tong,Baghurst,Vimpani, & McMichael, 2007), little attention has been paid to the role of specific environmentalfactors on different dimensions of intelligence. Prior research suggests that many environmental factors, when examined independently, are associated with children’s cognitive skills. The level of parental education, parental income and the home environment are strong contributors to children’s cognitive development (Crosnoe, Leventhal, Wirth, Pierce, & Pianta, 2010; Tong et al., 2007). Preterm birth (gestational age (GA) < 37 weeks) (Poulsen et al., 2013) and/or low birth weight (<2.5 kg) (Elgen, Sommerfelt, & Ellertsen, 2003) also have deleterious cognitive effects, and even studies conducted among children with birth weight within the normal range have reported a significant association between birth weight corrected for GA and cognitive skills (Eriksen et al., 2010; W. Matte, Bresnahan, Begg, & Susser, 2001). Breastfeeding and its duration (Belfort et al., 2013; Bernard et al., 2013; Kramer et al., 2008; Leventakou et al., 2013; Tozzi et al., 2012), as well as birth rank (Kristensen & Bjerkedal, 2007) are also linked with cognitive skills in some studies, but not all (Der, Batty, & Deary, 2006; Jacobson, Chiodo, & Jacobson, 1999; Wichman, Rodgers, & Maccallum, 2007). Because all these environmental factors seem to be very general, it may be expected that they have effects on all cognitive functions. A similar idea emerges from genetic research, with the generalist genes hypothesis (Kovas & Plomin, 2007). According to that hypothesis, most genes influencing one cognitive ability tend to influence other abilities as well. Interestingly, the same authors have also proposed that, while genes are generalists, environments may be specialists, i.e., leading to greater differentiation among cognitive functions (Kovas & Plomin, 2007). However, this hypothesis concerns mostly nonshared environmental factors, i.e., those that differentially affect one twin and not the other in behavior genetic studies. Such environmental factors are more likely to be factors outside the home environment, related to peers and schooling. Within that framework, the environmental factors mentioned above and usually recorded in epidemiological studies tend to be shared environmental factors, and are thus more likely to have general effects on cognition. Nonetheless, there are some suggestions from the literature that some of these may have more specific effects than expected (Belfort et al., 2013; Eriksen et al., 2010; Gustafsson, Duchén, Birberg, & Karlsson, 2004; Sommerfelt, Ellertsen, & Markestad, 1995; Taylor, Espy, & Anderson, 2009; Tozzi et al., 2012). In the present study, we aim to address the question whether certain general environmental factors have an influence that is specific to one or several cognitive functions (as opposed to all of them), or a greater influence on some cognitive functions than on others. Results of epidemiological studies have suggested that the level of parental education and the amount of cognitive stimulation from parents have a greater influence on children’s verbal than nonverbal skills (Eriksen et al., 2010; Sommerfelt et al., 1995). Some studies have suggested that preterm children (GA< 37 weeks) and/or those born with a low birth weight (<2.5 kg) may have particular problems with visual or spatial skills and mathematical skills (W. Klein, Hack, & Breslau, 1989; Rickards et al., 1993; Taylor et al., 2009), but most studies have argued for a generalized cognitive deficit (Breslau et al., 1994; Breslau, Chilcoat, DelDotto, Andreski, & Brown, 1996; Johnson et al., 2009; Wolke & Meyer, 1999). For example, Breslau et al. (1996) reported that the gradient relationship of birth weight with IQ applies equally to verbal and performance IQ. Contrariwise, findings from Sommerfelt et al. (Sommerfelt et al., 1995) suggested that performance IQ was more influenced by birth weight than verbal IQ. Some studies reported effects of breastfeeding on both verbal and performance IQ (Belfort et al., 2013; Kramer et al., 2008; Leventakou et al., 2013; Mortensen E, Michaelsen K, Sanders SA, & Reinisch J, 2002) whereas others have reported a significant association of breastfeeding (Tozzi et al., 2012) or breastfeeding duration (Gustafsson et al., 2004; Horwood, Darlow, & Mogridge, 2001; Oddy et al., 2003) solely with verbal IQ. Belfort et al. (2013) also reported that breastfeeding duration may not be associated with better visual memory skills. No differential effect on verbal and performance IQ has ever been reported concerning the ‘birth order effect’, which is known to be in favour of first-born siblings (Kristensen & Bjerkedal, 2007). Thus, some studies have suggested differential cognitive effects of some factors, but the literature remains largely inconsistent. Most importantly, none of these previous studies has tested this hypothesis in a statistically rigorous manner. Indeed, testing the difference between the regression parameters of a single determinant on two different cognitive measures raises methodological problems and cannot be performed by the statistical methods most widely used in epidemiology; for details see Supplementary methods). Our framework is based on a distinction between proximal and distal processes, a distinction that originates from ecological models of development (Bronfenbrenner, 1979, 1986). Distal factors includes aspects of the child’s background that are correlated with developmental outcomes. Proximal factors can be thought of as mediating pathways, or interceding mechanisms through which the distal factor exerts an influence on the outcome. Proximal factors are closer to the lived experience of the child and impact directly on attainment, such as the nature of day-to-day parent-child interactions. For example, parental education can be considered as a distal factor influencing cognitive skills through a range of proximal factors such as the child’s cognitive environment, breastfeeding duration or birth weight. Structural equation modeling (SEM) allows the estimation of the total effect of a distal factor on cognitive measure. This total effect can be then decomposed into an indirect effect (i.e., effect that is mediated by more proximal factors) and a direct effect (i.e., effect that is not mediated by other factors) (Kaplan, 2000).
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