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Cultural Differences in the Development of Processing Speed

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Title: Cultural Differences in the Development of Processing Speed
Language: English
Authors: Kail, Robert V., McBride-Chang, Catherine, Ferrer, Emilio, Cho, Jeung-Ryeul, Shu, Hua
Source: Developmental Science. May 2013 16(3):476-483.
Availability: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA/
Peer Reviewed: Y
Page Count: 8
Publication Date: 2013
Document Type: Journal Articles
Reports - Research
Descriptors: Cultural Differences, Cognitive Processes, Children, Longitudinal Studies, Child Development, Cognitive Development, Age Differences, Foreign Countries
Geographic Terms: California, China (Beijing), Hong Kong, Indiana, South Korea, United States
Assessment and Survey Identifiers: Woodcock Johnson Tests of Cognitive Ability
DOI: 10.1111/desc.12039
ISSN: 1363-755X
Abstract: The aim of the present work was to examine cultural differences in the development of speed of information processing. Four samples of US children ("N" = 509) and four samples of East Asian children ("N" = 661) completed psychometric measures of processing speed on two occasions. Analyses of the longitudinal data indicated that, although processing speed was comparable among US and East Asian children at the youngest age (4.5 years), it developed more rapidly in some but not all of the East Asian samples. Results are discussed in terms of factors that may promote more rapid development of processing speed in some East Asian cultures. (Contains 2 figures, 1 footnote, and 3 tables.)
Abstractor: As Provided
Number of References: 39
Entry Date: 2014
Accession Number: EJ1010864
Database: ERIC
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  Value: <anid>AN0086979782;5g501may.13;2018Jul02.08:52;v2.2.500</anid> <title id="AN0086979782-1">Cultural differences in the development of processing speed. </title> <p>The aim of the present work was to examine cultural differences in the development of speed of information processing. Four samples of US children (N = 509) and four samples of East Asian children (N = 661) completed psychometric measures of processing speed on two occasions. Analyses of the longitudinal data indicated that, although processing speed was comparable among US and East Asian children at the youngest age (~4.5 years), it developed more rapidly in some but not all of the East Asian samples. Results are discussed in terms of factors that may promote more rapid development of processing speed in some East Asian cultures.</p> <p>The aim of the present work was to examine cultural differences in the development of speed of information processing. Four samples of US children (N = 509) and four samples of East Asian children (N = 661) completed psychometric measures of processing speed on two occasions. Analyses of the longitudinal data indicated that, although processing speed was comparable among US and East Asian children at the youngest age (~4.5 years), it developed more rapidly in some but not all of the East Asian samples.</p> <p>The speed with which cognitive processes are executed increases substantially in childhood and less rapidly in adolescence (Kail & Ferrer, [<reflink idref="bib21" id="ref1">21</reflink>] ). This developmental change in processing speed or mental speed has been of interest for two reasons. First, it seems to reflect, at least in part, growth of a global mechanism. That is, the mechanism is not specific to particular tasks but instead is a fundamental property of the emerging information‐processing system (Cerella & Hale, [<reflink idref="bib5" id="ref2">5</reflink>] ; Kail, [<reflink idref="bib19" id="ref3">19</reflink>] ). Second, children's processing speed is correlated, often substantially, with measures of executive function, reasoning, and intelligence (Coyle, Pillow, Snyder & Kochunov, [<reflink idref="bib9" id="ref4">9</reflink>] ; Demetriou, Christou, Spanoudis & Platsidou, [<reflink idref="bib10" id="ref5">10</reflink>] ; Fry & Hale, [<reflink idref="bib14" id="ref6">14</reflink>] ; Kail, [<reflink idref="bib20" id="ref7">20</reflink>] ; Whitaker, Steele, Green, Bunge & Ferrer, [<reflink idref="bib38" id="ref8">38</reflink>] ).</p> <p>Most of the evidence concerning the nature and consequences of age‐related change in processing speed has come from children and adolescents living in North America and Europe. Cross‐cultural findings would be useful in documenting the robustness and universality of age‐related change in processing speed. That is, should differences emerge between samples from different countries, this would indicate that the underlying processes that drive growth of processing speed are not universal but are influenced by culturally specific factors.</p> <p>Relatively little work has been done on cultural differences in processing speed. Most published studies have involved comparing individuals from East Asian countries with individuals from Western countries and most bear on the simplest issue – cultural differences in processing speed at a single age. For young adults, the literature is inconsistent. Geary, Salthouse, Chen and Fan ([<reflink idref="bib15" id="ref9">15</reflink>] ) reported faster processing by US undergraduate students than Chinese undergraduates when the task involved searching for identical pairs of pictures but not when it involved comparing digit strings. In contrast, Hedden, Park, Nisbett, Ji, Jing and Jiao ([<reflink idref="bib17" id="ref10">17</reflink>] ) found the reverse pattern: Chinese undergraduates responded faster than US undergraduates on a digit comparison task but not when the task involved comparing figures consisting of line segments.</p> <p>More consistent findings have been reported in studies testing children: (a) 6‐year‐olds living in Hong Kong had higher scores on two psychometric measures of processing speed than US 6‐year‐olds (McBride‐Chang & Kail, [<reflink idref="bib28" id="ref11">28</reflink>] ); (b) Japanese and Chinese 9‐year‐olds responded more rapidly than British 9‐year‐olds on response‐time tasks (Lynn, Chan & Eysenck, [<reflink idref="bib25" id="ref12">25</reflink>] ; Lynn & Shigehisa, [<reflink idref="bib26" id="ref13">26</reflink>] ) and (c) Korean 10‐year‐olds responded more rapidly than US 10‐year‐olds on five different speeded tasks (Kail & Park, [<reflink idref="bib22" id="ref14">22</reflink>] ).</p> <p>We know of only two studies that have reported cultural comparisons of age differences in processing speed. On the one hand, Stevenson, Stigler, Lee, Lucker, Kitamura and Hsu ([<reflink idref="bib34" id="ref15">34</reflink>] ) found that US students responded faster than Japanese and Chinese students at first grade, and faster than Chinese students at fifth grade. However, interpretation of these age‐related patterns is not straightforward because different tasks were used in grades 1 and 5. On the other hand, Demetriou, Kui, Spanoudis, Christou, Kyriakides and Platsidou ([<reflink idref="bib11" id="ref16">11</reflink>] ) tested 8‐ to 14‐year‐olds and found that Chinese children generally responded more rapidly than Greek children but that the difference became smaller with age.</p> <p>Thus, the small extant literature provides some support for the hypothesis that children from East Asia process information more rapidly than children from North America and Europe. Strong conclusions are not possible, however, because most studies have focused on (a) a single age and thus do not provide the sort of longitudinal data that would allow cross‐cultural comparisons in overall processing speed as well as developmental change in processing speed, and (b) pairs of countries – one from East Asia and one from the West – which means that results may be specific to countries and not hold more broadly.</p> <p>The aim of the present work was to provide cross‐cultural data bearing on the question of cultural differences in the development of processing speed. Specifically, we report longitudinal data on processing speed in four samples from East Asia (two from China, two from Korea) as well as four samples from the United States. Most children were tested twice on two psychometric measures of processing speed. The resulting longitudinal data allowed us (a) to compare East Asian and American children in terms of overall processing speed as well as in the rate of age‐related change in processing speed from early to late childhood, and (b) to determine the extent to which differences were consistent across multiple samples within each cultural setting. Such comparisons would be useful in providing additional standards by which to evaluate theoretical accounts of the growth of processing speed. Should differences emerge across cultures, they would indicate the need to identify culturally specific factors that contribute to the growth of processing speed (i.e. variation across cultures in biological or cultural practices that facilitate speeded processing). Based on the handful of studies described previously, we anticipated that children from East Asia would have higher scores on measures of processing speed. But it was an open question whether such a difference would emerge in overall processing speed, age‐related change in speed, or both.</p> <hd id="AN0086979782-2">Method</hd> <hd id="AN0086979782-3">Participants</hd> <p>All children were tested as part of extant longitudinal projects in which participants were administered a battery of psychometric measures that included at least one measure of processing speed. That is, the data reported here were not collected initially with the intent of comparing processing speed in different countries. However, the use of comparable testing protocols in different countries makes them ideal for that purpose. The findings reported here are derived from the first and second administrations of those processing‐speed measures.</p> <p>Table [NaN] shows, for all samples, the number of participating boys and girls as well as the mean age at both testing occasions. The ‘US National’ group consists of a nationally representative sample of individuals used to norm the WJ‐R tests. These individuals were then re‐tested as part of a longitudinal study (see McArdle, Ferrer, Hamagami & Woodcock, [<reflink idref="bib27" id="ref17">27</reflink>] ). All other samples are identified by the locale where they were tested. Children in the Indianapolis sample were recruited via newspaper articles and letters to university alumni; those in the San Francisco Bay and West Lafayette samples were recruited from schools and other institutions.</p> <p>Children in the Beijing and Hong Kong samples were recruited from maternal and child‐health centers to participate in the norming of Chinese versions of the Communicative Development Inventory (Tardif, Fletcher, Zhang & Liang, [<reflink idref="bib36" id="ref18">36</reflink>] ); these samples were demographically representative of the respective cities. Children in the Chongwon sample were recruited from local schools.</p> <p>In the Indianapolis, San Francisco Bay, and West Lafayette samples, all participants were native speakers of English. In the Beijing, Hong Kong, and Changwon samples, participants were native speakers of Mandarin, Cantonese, and Korean, respectively. The children in the East Asian samples ranged in age from 4.5 years of age to just under 11 years of age; consequently, we considered only data from US children who were in this range.</p> <p>Estimates of socioeconomic status are provided by maternal reports of education, obtained for three Asian samples and one US sample. In each sample, level of education ranged from only primary school to graduate school. However, in Beijing and Changwon (younger sample only), the median level of education was some college; in Hong Kong, it was advanced secondary education (i.e. matriculation course leading to advanced level examinations); and in the United States, an undergraduate degree.</p> <hd id="AN0086979782-4">Procedure</hd> <p>At all sites, children were administered a battery of cognitive tasks at regular intervals. All children were tested on Cross Out, from the Woodcock‐Johnson Tests of Cognitive Ability (Woodcock & Johnson, [<reflink idref="bib39" id="ref19">39</reflink>] ). In this task, each of 30 rows consists of a geometric figure at the left end of a row and 19 similar figures to the right. One row, for example, consists of a triangle enclosing a single dot; the 19 figures are triangles with various objects in the interior (e.g. a single dot, three dots, a plus, a square). The child places a line through the five figures of the 19 that are identical to the one on the left; the performance measure is the number of rows completed in 3 min.</p> <p>In addition, at all sites except San Francisco Bay, children were tested on the Visual Matching from the Woodcock‐Johnson Tests. In this task, each of 60 rows includes six digits, two of which are identical (e.g. 8 9 5 2 9 7); the child circles the identical digits. The performance measure is the number of rows completed correctly in 3 min.</p> <hd id="AN0086979782-5">Results</hd> <p>Figure [NaN] shows age‐related change in performance on Cross Out and Visual Matching, separately for the US and East Asian samples. Performance improved with age and this improvement seems to have been more pronounced in the Asian samples. To evaluate this statistically, we fitted a linear aged‐based mixed model to the data.[<reflink idref="bib1" id="ref20">1</reflink>] This model can be expressed as</p> <p>Yit=β0i+β1i·ageit+eit</p> <p>In this model, Y<subs>it</subs> is the observed score on person i at measurement t, β<subs>0i</subs> represents the intercept for person i, β<subs>1i</subs> denotes the linear age‐related slope for person i, age<subs>it</subs> is the observed age of person i at measurement t, and e<subs>it</subs> is the error score of person i at measurement t. The intercept and slope, in turn, can be decomposed at a second level as β<subs>0i</subs> = μ<subs>0</subs> + ɛ<subs>0i</subs> and</p> <p>β1i=μ1+ε1i.</p> <p>Equation 2 indicates that the intercept and slope scores have group means (μ<subs>0</subs> and μ<subs>1</subs>) and residuals (ε<subs>0i</subs> and ε<subs>1i</subs>), and these residuals have zero means and variance components (σ<subs>0</subs><sups>2</sups>, σ<subs>1</subs><sups>2</sups>, and σ<subs>01</subs>). Similarly, the error term associated with the within‐person residual e<subs>it</subs> also has a zero mean and a variance term σ<subs>e</subs><sups>2</sups>. Having age as the basis allows this model to examine within‐person changes across measurement occasions expressed in terms of age. This feature is particularly useful for our data, which consisted of two data points per person, spread across varying ages and measurement intervals. Thus, inferences from this model pertain to within‐person changes across the age span together with differences in such changes across individuals (see Ferrer & McArdle, [<reflink idref="bib12" id="ref21">12</reflink>] ; McArdle et al., [<reflink idref="bib27" id="ref22">27</reflink>] ).</p> <p>To carry out the comparisons between the US and East Asian samples, we implemented a multiple‐group approach using structural equation modeling in Mplus (Muthén & Muthén, [<reflink idref="bib30" id="ref23">30</reflink>] ). In a multiple‐group approach, parameters of interest can be constrained or relaxed across groups in order to test for group differences. In our analyses, we used this logic and evaluated a number of increasingly restrictive models that evaluated whether US and Asian samples differed with respect to means and variances of intercept and slope parameters. In the most restrictive model (Model 1 in Table [NaN] ), all parameters were set to be equal for the US and East Asian samples. This model consisted of six parameters, including the intercept and slope means, their corresponding variances and covariance, as well as the residual variance. In the least restrictive model (Model 5), all parameters were free to vary, thus yielding a total of 12 parameters.</p> <p>Demographic information</p> <p> <ephtml> <table><tr><th align="left" /><th align="center">N</th><th align="center">Age (years)</th></tr><tr><th align="center">Boys</th><th align="center">Girls</th><th align="left">Time 1</th><th align="left">Time 2</th></tr><tr><td align="left">US samples</td></tr><tr><td align="left">US National</td><td align="char" char=".">77</td><td align="char" char=".">72</td><td align="left" char=" ">6.95 (1.46, 5.00–10.00) </td><td align="left" char=" ">8.83 (1.47, 5.42–10.92)</td></tr><tr><td align="left">Indianapolis</td><td align="char" char=".">86</td><td align="char" char=".">101</td><td align="left" char=" ">8.11 (1.26, 6.02–10.52)</td><td align="left" char=" ">8.61 (1.24, 6.49–10.96)</td></tr><tr><td align="left">San Francisco Bay </td><td align="char" char=".">46</td><td align="char" char=".">44</td><td align="left" char=" ">7.69 (1.46, 4.83–10.94) </td><td align="left" char=" ">8.70 (1.11, 6.46–10.93)</td></tr><tr><td align="left">West Lafayette</td><td align="char" char=".">46</td><td align="char" char=".">37</td><td align="left" char=" ">7.44 (1.30, 5.33–9.55)</td><td align="left" char=" ">9.87 (0.57, 8.83–10.83)</td></tr><tr><td align="left">Asian samples</td></tr><tr><td align="left">Beijing</td><td align="char" char=".">164</td><td align="char" char=".">129</td><td align="left" char=" ">6.43 (0.30, 5.83–7.00)</td><td align="left" char=" ">7.44 (0.30, 6.83–8.08)</td></tr><tr><td align="left">Hong Kong</td><td align="char" char=".">69</td><td align="char" char=".">94</td><td align="left" char=" ">5.15 (0.29, 4.52–5.73)</td><td align="left" char=" ">6.17 (0.30, 5.56–6.81)</td></tr><tr><td align="left">Changwon (younger)</td><td align="char" char=".">52</td><td align="char" char=".">53</td><td align="left" char=" ">6.95 (0.31, 6.42–7.50)</td><td align="left" char=" ">7.88 (0.87, 7.42–8.50)</td></tr><tr><td align="left">Changwon (older)</td><td align="char" char=".">47</td><td align="char" char=".">53</td><td align="left" char=" ">7.95 (0.34, 6.75–8.50)</td><td align="left" char=" ">9.96 (0.34, 8.75–10.55)</td></tr></table> </ephtml> Note</p> <p>1 Mean age (in years) is presented, with SD and range in parentheses.</p> <p>Goodness‐of‐fit indices – linear age‐based model</p> <p> <ephtml> <table><tr><th align="left">Model</th><th align="char">‐2LL</th><th align="char">#par.</th><th align="char">BIC</th><th align="char">ΔBIC</th><th align="char">w(BIC)</th><th align="char">AIC</th><th align="char">ΔAIC</th><th align="char">w(AIC)</th></tr><tr><td align="left">Cross Out </td></tr><tr><td align="left">M1: Full Invariance</td><td align="char" char=".">5723</td><td align="char" char=".">6</td><td align="char" char=".">11488</td><td align="char" char=".">94</td><td align="char" char="."><.01</td><td align="char" char=".">11458</td><td align="char" char=".">119</td><td align="char" char="."><.01</td></tr><tr><td align="left">M2: Means Different</td><td align="char" char=".">5669</td><td align="char" char=".">8</td><td align="char" char=".">11394</td><td align="char" char=".">0</td><td align="char" char=".">.950</td><td align="char" char=".">11354</td><td align="char" char=".">15</td><td align="char" char="."><.01</td></tr><tr><td align="left">M3: + Variances </td><td align="char" char=".">5668</td><td align="char" char=".">10</td><td align="char" char=".">11406</td><td align="char" char=".">12</td><td align="char" char="."><.01</td><td align="char" char=".">11355</td><td align="char" char=".">16</td><td align="char" char="."><.01</td></tr><tr><td align="left">M4: + Covariance</td><td align="char" char=".">5667</td><td align="char" char=".">11</td><td align="char" char=".">11411</td><td align="char" char=".">17</td><td align="char" char="."><.01</td><td align="char" char=".">11355</td><td align="char" char=".">16</td><td align="char" char="."><.01</td></tr><tr><td align="left">M5: + Residuals </td><td align="char" char=".">5657</td><td align="char" char=".">12</td><td align="char" char=".">11400</td><td align="char" char=".">6</td><td align="char" char=".">.047</td><td align="char" char=".">11339</td><td align="char" char=".">0</td><td align="char" char=".">.999</td></tr><tr><td align="left">Visual Matching</td></tr><tr><td align="left">M1: Full Invariance</td><td align="char" char=".">6067</td><td align="char" char=".">6</td><td align="char" char=".">12175</td><td align="char" char=".">56</td><td align="char" char="."><.01</td><td align="char" char=".">12146</td><td align="char" char=".">79</td><td align="char" char="."><.01</td></tr><tr><td align="left">M2: Means Different</td><td align="char" char=".">6032</td><td align="char" char=".">8</td><td align="char" char=".">12119</td><td align="char" char=".">0</td><td align="char" char=".">.791</td><td align="char" char=".">12079</td><td align="char" char=".">12</td><td align="char" char="."><.01</td></tr><tr><td align="left">M3: + Variances </td><td align="char" char=".">6027</td><td align="char" char=".">10</td><td align="char" char=".">12122</td><td align="char" char=".">3</td><td align="char" char=".">.176</td><td align="char" char=".">12073</td><td align="char" char=".">6</td><td align="char" char=".">.046</td></tr><tr><td align="left">M4: + Covariance</td><td align="char" char=".">6026</td><td align="char" char=".">11</td><td align="char" char=".">12128</td><td align="char" char=".">9</td><td align="char" char="."><.01</td><td align="char" char=".">12074</td><td align="char" char=".">7</td><td align="char" char=".">.028</td></tr><tr><td align="left">M5: + Residuals </td><td align="char" char=".">6022</td><td align="char" char=".">12</td><td align="char" char=".">12126</td><td align="char" char=".">7</td><td align="char" char=".">.024</td><td align="char" char=".">12067</td><td align="char" char=".">0</td><td align="char" char=".">.924</td></tr></table> </ephtml> Note</p> <ulist> <item>2 M1 =  All parameters equal across eight samples. M2 =  Intercept and linear slope means free across groups (US vs. East Asian samples). M3 =  Intercept and linear slope means and variances free across groups (US versus East Asian samples). M4 =  Model 3 plus intercept and slope covariance free across groups (US versus East Asian). M5 =  Model 4 plus residuals free across groups (US versus East Asian samples).</item> <item>3 = Analyses for Visual Matching include all samples except San Francisco Bay.</item> </ulist> <p>Model fit statistics from the first set of analyses are presented in Table [NaN] , separately for Cross Out and Visual Matching. Table [NaN] includes two measures of goodness of fit – Akaike's information criterion (AIC) and the Bayesian information criterion (BIC) – that differ in their assumptions (see Wagenmakers & Farrell, [<reflink idref="bib37" id="ref24">37</reflink>] ). Both are derived from the log‐likelihood ratio that characterizes the fit of the model to the data; each adjusts the degree of fit based on the number of parameters in the model and, in both cases, smaller values indicate a better fit. Outcomes for both are presented here because they yielded a somewhat different pattern of results.</p> <p>For both tasks and both criteria, a fully invariant model (i.e. all parameters equal across samples) fit the data less well than a model allowing differences between the US and East Asian samples in intercept and slope means. In addition, according to the AIC – but not the BIC – fit was improved further by allowing all parameters to vary freely across the US and East Asian samples (Model 5).</p> <p>To compare the models formally, we first computed the difference in fit (ΔAIC, ΔBIC) by subtracting the values for AIC and BIC for the best‐fitting model from the AIC and BIC values for each of the other models. Next, we computed a weight, w, for each model, which was defined as</p> <p>w=e −1/2ΔX/∑e−1/2ΔX</p> <p>where X = AIC or BIC; w indicates the relative likelihood of the model given the data and other models under consideration (Burnham & Anderson, [<reflink idref="bib3" id="ref25">3</reflink>] ; Wagenmakers & Farrell, [<reflink idref="bib37" id="ref26">37</reflink>] ). Values for w, shown in Table [NaN] , reveal the pattern described previously: the data are best fit by Models 5 and 2, according to the AIC and BIC criteria, respectively. In other words, the findings provide strong evidence for differences across groups in slope and intercept means, and more tentative evidence for differences in variances, covariances, and overall fit.</p> <p>Parameter estimates from Model 5 are displayed in Table [NaN] and the best‐fitting linear functions are shown in Figure [NaN] . These data show that Asian children had higher scores than US children on both tasks at the youngest age (i.e. 4.52 years) and that this difference increased with development. The variance and covariance parameters were less consistent: The intercept variance was smaller for the US samples than the Asian samples on the Cross Out task but greater on the Visual Matching task. In contrast, the slope variance was greater for the US samples than the Asian samples on the Cross Out task but smaller on the Visual Matching task. Similarly, the covariance parameter for Cross Out was positive for the US samples but negative for the East Asian samples. Finally, for both tasks the residual variance was greater in the Asian samples than in the US samples, indicating differences in the explained variance across samples.</p> <p>Parameter estimates – linear age‐based model</p> <p> <ephtml> <table><tr><th align="left">Parameter</th><th align="char">Estimate</th><th align="char">SE</th><th align="char">t‐value</th></tr><tr><td align="left">Cross Out</td></tr><tr><td align="left">US Samples</td></tr><tr><td align="left">Mean Intercept</td><td align="char" char=".">5.32</td><td align="char" char=".">.256</td><td align="char" char=".">20.77</td></tr><tr><td align="left">Mean Slope</td><td align="char" char=".">2.67</td><td align="char" char=".">.067</td><td align="char" char=".">39.87</td></tr><tr><td align="left">Variance Intercept</td><td align="char" char=".">3.75</td><td align="char" char=".">1.465</td><td align="char" char=".">2.56</td></tr><tr><td align="left">Variance Slope</td><td align="char" char=".">.19</td><td align="char" char=".">.092</td><td align="char" char=".">2.03</td></tr><tr><td align="left">Covariance Int‐Slp</td><td align="char" char=".">.21</td><td align="char" char=".">.332</td><td align="char" char=".">.63</td></tr><tr><td align="left">Variance Error</td><td align="char" char=".">3.98</td><td align="char" char=".">.332</td><td align="char" char=".">11.99</td></tr><tr><td align="left">East Asian Samples</td></tr><tr><td align="left">Mean Intercept</td><td align="char" char=".">5.99</td><td align="char" char=".">.238</td><td align="char" char=".">25.18</td></tr><tr><td align="left">Mean Slope</td><td align="char" char=".">3.03</td><td align="char" char=".">.090</td><td align="char" char=".">33.90</td></tr><tr><td align="left">Variance Intercept</td><td align="char" char=".">4.19</td><td align="char" char=".">1.009</td><td align="char" char=".">4.15</td></tr><tr><td align="left">Variance Slope</td><td align="char" char=".">.01</td><td align="char" char=".">.192</td><td align="char" char=".">.05</td></tr><tr><td align="left">Covariance Int‐Slp</td><td align="char" char=".">−.07</td><td align="char" char=".">.358</td><td align="char" char=".">−.20</td></tr><tr><td align="left">Variance Error</td><td align="char" char=".">5.82</td><td align="char" char=".">.354</td><td align="char" char=".">14.46</td></tr><tr><td align="left">Visual Matching</td></tr><tr><td align="left">US Samples</td></tr><tr><td align="left">Mean Intercept</td><td align="char" char=".">11.87</td><td align="char" char=".">.667</td><td align="char" char=".">17.81</td></tr><tr><td align="left">Mean Slope</td><td align="char" char=".">5.07</td><td align="char" char=".">.145</td><td align="char" char=".">34.83</td></tr><tr><td align="left">Variance Intercept</td><td align="char" char=".">44.38</td><td align="char" char=".">9.668</td><td align="char" char=".">4.59</td></tr><tr><td align="left">Variance Slope</td><td align="char" char=".">1.35</td><td align="char" char=".">.443</td><td align="char" char=".">3.05</td></tr><tr><td align="left">Covariance Int‐Slp</td><td align="char" char=".">−4.87</td><td align="char" char=".">1.940</td><td align="char" char=".">−2.51</td></tr><tr><td align="left">Variance Error</td><td align="char" char=".">10.03</td><td align="char" char=".">1.070</td><td align="char" char=".">9.37</td></tr><tr><td align="left">East Asian Samples</td></tr><tr><td align="left">Mean Intercept</td><td align="char" char=".">13.56</td><td align="char" char=".">.421</td><td align="char" char=".">32.19</td></tr><tr><td align="left">Mean Slope</td><td align="char" char=".">5.46</td><td align="char" char=".">.156</td><td align="char" char=".">35.12</td></tr><tr><td align="left">Variance Intercept</td><td align="char" char=".">26.76</td><td align="char" char=".">5.732</td><td align="char" char=".">4.67</td></tr><tr><td align="left">Variance Slope</td><td align="char" char=".">1.48</td><td align="char" char=".">.661</td><td align="char" char=".">2.24</td></tr><tr><td align="left">Covariance Int‐Slp</td><td align="char" char=".">−4.11</td><td align="char" char=".">1.924</td><td align="char" char=".">−2.14</td></tr><tr><td align="left">Variance Error</td><td align="char" char=".">14.61</td><td align="char" char=".">.971</td><td align="char" char=".">15.04</td></tr></table> </ephtml> Note</p> <p>4 Parameters are from Model 5 in Table . Age was centered at 4.52, the youngest age in the data.</p> <p>We also estimated slope and intercept parameters separately for each of the eight samples; these are shown in Figure [NaN] . For the Cross Out task, the slopes for samples from Beijing, Hong Kong, and Changwon (older) were significantly greater than slopes for all US samples; only the Changwon (younger) sample did not differ from the US samples. For the Visual Matching task, the slopes for samples from Beijing and Hong Kong were significantly greater than slopes for all US samples, but the slopes for the two Changwon samples did not differ from the US samples. Finally, for the intercepts, there was no consistent pattern on either task: estimates overlapped substantially and did not vary systematically by group.</p> <hd id="AN0086979782-6">Discussion</hd> <p>The primary result of the present work is that speed of processing developed more rapidly in Chinese children from the Beijing and Hong Kong samples than in US children. There was also some evidence that speed of processing developed more rapidly in Korean children but this result was not consistent across measures or samples. In addition, there were differences in variance and covariance parameters across Asian and US samples but these were not consistent.</p> <p>The present findings provide no direct evidence regarding possible underlying mechanisms, but they are compatible with both genetic and cultural accounts. Regarding genetic accounts, behavior genetic studies consistently show a substantial heritable component to processing speed (Beaujean, [<reflink idref="bib1" id="ref27">1</reflink>] ; Hansell, Wright, Luciano, Geffen, Geffen & Martin, [<reflink idref="bib16" id="ref28">16</reflink>] ; Lee, Mosing, Henry, Troller, Lammel, Ames, Martin, Wright & Sachdev, [<reflink idref="bib24" id="ref29">24</reflink>] ). More specifically, the 7‐repeat allele of the dopamine D4 receptor genes is rare in East Asian populations (Chang, Kidd, Kivak, Pakstis & Kidd, [<reflink idref="bib6" id="ref30">6</reflink>] ) and the presence of this allele has been associated with (among many other variables) slower processing speed (Szekely, Balota, Duchek, Nemoda, Vereczkei & Sasvari‐Szekely, [<reflink idref="bib35" id="ref31">35</reflink>] ). By this account, cultural differences in processing speed would reflect the greater presence in Western samples of children with relatively slower processing speed.</p> <p>Regarding culturally specific accounts, one possibility involves the impact of learning to read in a language that has a complex orthography. Demetriou et al. ([<reflink idref="bib11" id="ref32">11</reflink>] ) suggested that mastery of a visually complex orthography would enhance children's visual‐spatial skills generally and drive the relatively faster processing of Chinese children in their study. Similarly, McBride‐Chang, Zhou, Cho, Aram, Levin and Tolchinsky ([<reflink idref="bib29" id="ref33">29</reflink>] ) showed that (a) visual‐spatial skill was greater in children learning to read complex orthographies, such as Chinese, than those learning to read relatively simple orthographies, such as Spanish, and (b) greater reading skill at 5 years of age was associated with more advanced visual‐spatial skill at 6 years of age (controlling for visual‐spatial skill at age 5). More generally, children begin school at a younger age in most East Asian countries and thus they tend to have more years of school than their agemates in Western countries. Although we are unaware of evidence linking school exposure directly to processing speed, there are findings showing that school attendance enhances some components of executive functioning (Burrage, Ponitz, McCready, Shah, Sims, Jewkes & Morrison, [<reflink idref="bib4" id="ref34">4</reflink>] ).</p> <p>A shortcoming of all these proposals is that they do not explain the results for the Korean samples, which resembled the US samples more often than they resembled the East Asian samples. We cannot account for the differences among the East Asian samples but will simply note that it would be premature to make broad claims about an East Asian advantage in processing speed. The advantage may turn out to be specific to Chinese children and, for that matter, may only hold for comparisons with US children, since we did not have samples of multiple Western countries. In any case, this result underscores the need to obtain samples from multiple countries within cultures, just as structural equation modeling depends upon multiple tasks to estimate latent constructs.</p> <p>More generally, regardless of the specific mechanism(s) responsible for these effects, cultural differences in the development of speed may help to explain the cultural differences in children's academic achievement and executive functioning. That is, children from East Asian countries typically surpass Western children on measures of academic achievement (Fleischman, Hopstock, Pelczar & Shelley, [<reflink idref="bib13" id="ref35">13</reflink>] ; Stevenson, Lee & Stigler, [<reflink idref="bib33" id="ref36">33</reflink>] ) and on measures of executive function, particularly those that tap inhibitory processes (Lahat, Todd, Mahy, Lau & Zelazo, [<reflink idref="bib23" id="ref37">23</reflink>] ; Sabbagh, Xu, Carlson, Moses & Lee, [<reflink idref="bib32" id="ref38">32</reflink>] ). These differences are often explained in terms of culturally specific processes, such as greater parental emphasis on academic achievement and greater parental expectations regarding impulse control (Chen, Hastings, Rubin, Chen, Cen & Stewart, [<reflink idref="bib7" id="ref39">7</reflink>] ; Stevenson et al., [<reflink idref="bib33" id="ref40">33</reflink>] ). However, given that faster processing speed is associated with greater skill in reading and math (Bull, Espy & Wiebe, [<reflink idref="bib2" id="ref41">2</reflink>] ; Chung & McBride‐Chang, [<reflink idref="bib8" id="ref42">8</reflink>] ; McBride‐Chang & Kail, [<reflink idref="bib28" id="ref43">28</reflink>] ) and more efficient executive functioning (Huizinga, Dolan & van der Molen, [<reflink idref="bib18" id="ref44">18</reflink>] ; Rose, Feldman & Jankowski, [<reflink idref="bib31" id="ref45">31</reflink>] ), cultural differences in academic achievement and executive functioning may well be linked, in part, to cultural differences in the development of processing speed.</p> <hd id="AN0086979782-7">Acknowledgements</hd> <p>We thank John McArdle for sharing the WJ‐R retest data and Silvia Bunge for sharing the San Francisco Bay data. The San Francisco Bay data were collected by Chloe Green, Brian Johnson, and Ori Elis at UC Berkeley. This research was supported in part by grants from the National Institute of Health (NINDS ‐ R01 NS057146, NICHD ‐ R01 046927) and by a grant from the Research Grants Council of the Hong Kong Special Administrative Region (#448907).</p> <ref id="AN0086979782-8"> <title>Footnotes</title> <blist> <bibl id="bib1" idref="ref20" type="bt">1</bibl> <bibtext>Because processing speed increases rapidly during childhood but more slowly in adolescence, nonlinear models best characterize growth of speed during childhood, adolescence, and young adulthood (Kail & Ferrer, 21). However, given that (a) this work was concerned solely with change during childhood, and (b) analyses revealed no consistent differences in the fit of linear and nonlinear models, for simplicity we focused on linear models. </bibtext> </blist> </ref> <ref id="AN0086979782-9"> <title>References</title> <blist> <bibtext>Beaujean, A.A. ( 2005 ). 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Error bars depict standard errors.</p> <p>Graph: Mean slope (top panels) and intercept (bottom panels) parameters for the Cross Out and Visual Matching tasks, separately for the four US and four East Asian samples. Ind, Natl, SFB, WL, Beij, and HK denote the Indianapolis, US National, San Francisco Bay, West Lafayette, Beijing, and Hong Kong samples, respectively; Chan(y) and Chan(o) denote younger and older samples in Changwon, respectively. Error bars depict 95% confidence intervals.</p> <aug> <p>By Robert V. Kail; Catherine McBride‐Chang; Emilio Ferrer; Jeung‐Ryeul Cho and Hua Shu</p> </aug>
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  Group: Avail
  Data: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 8
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2013
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Cultural+Differences%22">Cultural Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Processes%22">Cognitive Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Children%22">Children</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+Studies%22">Longitudinal Studies</searchLink><br /><searchLink fieldCode="DE" term="%22Child+Development%22">Child Development</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Development%22">Cognitive Development</searchLink><br /><searchLink fieldCode="DE" term="%22Age+Differences%22">Age Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22California%22">California</searchLink><br /><searchLink fieldCode="DE" term="%22China+%28Beijing%29%22">China (Beijing)</searchLink><br /><searchLink fieldCode="DE" term="%22Hong+Kong%22">Hong Kong</searchLink><br /><searchLink fieldCode="DE" term="%22Indiana%22">Indiana</searchLink><br /><searchLink fieldCode="DE" term="%22South+Korea%22">South Korea</searchLink><br /><searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink>
– Name: SubjectThesaurus
  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22Woodcock+Johnson+Tests+of+Cognitive+Ability%22">Woodcock Johnson Tests of Cognitive Ability</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/desc.12039
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1363-755X
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The aim of the present work was to examine cultural differences in the development of speed of information processing. Four samples of US children ("N" = 509) and four samples of East Asian children ("N" = 661) completed psychometric measures of processing speed on two occasions. Analyses of the longitudinal data indicated that, although processing speed was comparable among US and East Asian children at the youngest age (4.5 years), it developed more rapidly in some but not all of the East Asian samples. Results are discussed in terms of factors that may promote more rapid development of processing speed in some East Asian cultures. (Contains 2 figures, 1 footnote, and 3 tables.)
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: Ref
  Label: Number of References
  Group: RefInfo
  Data: 39
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2014
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1010864
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1010864
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      – Type: doi
        Value: 10.1111/desc.12039
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 476
    Subjects:
      – SubjectFull: Cultural Differences
        Type: general
      – SubjectFull: Cognitive Processes
        Type: general
      – SubjectFull: Children
        Type: general
      – SubjectFull: Longitudinal Studies
        Type: general
      – SubjectFull: Child Development
        Type: general
      – SubjectFull: Cognitive Development
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      – SubjectFull: Age Differences
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      – SubjectFull: Foreign Countries
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      – SubjectFull: California
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      – SubjectFull: China (Beijing)
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      – SubjectFull: Hong Kong
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      – SubjectFull: Indiana
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      – SubjectFull: South Korea
        Type: general
      – SubjectFull: United States
        Type: general
      – SubjectFull: Woodcock Johnson Tests of Cognitive Ability
        Type: general
    Titles:
      – TitleFull: Cultural Differences in the Development of Processing Speed
        Type: main
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            NameFull: Kail, Robert V.
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            NameFull: McBride-Chang, Catherine
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            NameFull: Ferrer, Emilio
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            NameFull: Cho, Jeung-Ryeul
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            NameFull: Shu, Hua
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              Value: 1363-755X
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              Value: 3
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            – TitleFull: Developmental Science
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