The role of rapid naming in reading development and dyslexia in Chinese

Journal of Experimental Child Psychology 130 (2015) 106–122

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The role of rapid naming in reading development and dyslexia in Chinese Chen-Huei Liao a, Ciping Deng b, Jessica Hamilton c, Clara Shuk-Ching Lee c, Wei Wei b, George K. Georgiou c,⇑ a

Department of Special Education, National Taichung University of Education, Taiwan, ROC School of Psychology and Cognitive Science, East China Normal University, Shanghai, China c Department of Educational Psychology, University of Alberta, Edmonton, Alberta T6G 2G5, Canada b

a r t i c l e

i n f o

Article history: Received 25 April 2014 Revised 2 October 2014

Keywords: Rapid automatized naming Reading Dyslexia Chinese Phonological awareness Orthographic processing

a b s t r a c t We examined in a series of studies the mechanism that may underlie the relationship between Rapid Automatized Naming (RAN) and reading (accuracy and fluency) in Mandarin Chinese. Study 1 examined the ‘‘arbitrary” connections hypothesis in a sample of Grade 2 children (N = 182). Study 2 contrasted the phonological processing, orthographic processing, and speed of processing hypotheses in a sample of Grade 2 children followed until Grade 5 (N = 72). Finally, Study 3 contrasted the same hypotheses in a sample of Grade 4 children with dyslexia (n = 30) and chronological-age controls (n = 30). The results indicated that (a) RAN is unrelated to Paired Associate Learning (PAL) tasks that tap the ability to form arbitrary connections between characters and their pronunciation, (b) controlling for nonverbal IQ and orthographic processing was sufficient to explain the RAN–reading accuracy relationship but not the RAN–reading fluency relationship, and (c) the observed differences between dyslexics and controls in RAN diminished after controlling for orthographic processing. Taken together, these findings suggest that RAN is related to reading accuracy (and partly to reading fluency) because children must access orthographic representations from long-term memory. Although accessing these representations is sufficient for accurate word recognition, it is not sufficient for fluent reading, which also requires efficient parafoveal processing. Ó 2014 Elsevier Inc. All rights reserved.

⇑ Corresponding author. E-mail address: [email protected] (G.K. Georgiou). http://dx.doi.org/10.1016/j.jecp.2014.10.002 0022-0965/Ó 2014 Elsevier Inc. All rights reserved.

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Introduction Rapid Automatized Naming (RAN), defined as the ability to name as fast as possible highly familiar stimuli such as digits, letters, objects, and colors, has been found to be a strong predictor of reading ability in different languages (e.g., Bowers, 1995; Cutting & Denckla, 2001; de Jong & van der Leij, 1999; Georgiou, Parrila, Cui, & Papadopoulos, 2013; Ho & Lai, 1999; Landerl & Wimmer, 2008; Lepola, Poskiparta, Laakkonen, & Niemi, 2005; Powell, Stainthorp, Stuart, Garwood, & Quinlan, 2007; Savage & Frederickson, 2005). Its popularity has grown, particularly after the findings of several studies showing that it predicts reading independently of other known correlates of reading such as letter knowledge, phonological awareness, vocabulary, short-term memory, and orthographic processing (e.g., Bowey, McGuigan, & Ruschena, 2005; Manis, Doi, & Bhadha, 2000; Pan et al., 2011; Parrila, Kirby, & McQuarrie, 2004; Powell et al., 2007; Savage & Frederickson, 2005). Despite the acknowledged importance of RAN in predicting reading, researchers have not yet been able to identify the mechanism that is responsible for the RAN–reading relationship. As a result, several competing theoretical accounts have been proposed (Georgiou & Parrila, 2013). For example, Torgesen, Wagner, and colleagues (e.g., Torgesen, Wagner, Rashotte, Burgess, & Hecht, 1997; Wagner & Torgesen, 1987) have argued that RAN is part of the phonological processing construct and that it predicts reading because it taps the ability to access and retrieve phonological information from long-term memory. In turn, Bowers and colleagues (e.g., Bowers, Sunseth, & Golden, 1999; Bowers & Wolf, 1993), have argued that RAN is related to reading because of its contribution to orthographic processing. Finally, Kail and colleagues (e.g., Kail & Hall, 1994; Kail, Hall, & Caskey, 1999) have argued that RAN and reading are related because skilled performance in both naming and reading depends, in part, on the rapid execution of their underlying processes. Not surprisingly, several studies have also shown that RAN predicts reading ability in Chinese (e.g., Chow, McBride-Chang, & Burgess, 2005; Ding, Richman, Yang, & Guo, 2010; Liao, Georgiou, & Parrila, 2008; Luo, Chen, Deacon, Zhang, & Yin, 2013; McBride-Chang & Ho, 2005; McBride-Chang & Kail, 2002; Pan et al., 2011; Tan, Spinks, Eden, Perfetti, & Siok, 2005; Yeung et al., 2011) and differentiates Chinese children with and without dyslexia (e.g., Chung, Ho, Chan, Tsang, & Lee, 2010; Chung et al., 2008; Ho, Chan, Lee, Tsang, & Luan, 2004; Ho, Chan, Tsang, & Lee, 2002; Ho & Lai, 1999; Li, Shu, McBride-Chang, Liu, & Xue, 2009; McBride-Chang et al., 2013; Shu, McBride-Chang, Wu, & Liu, 2006; Wang, Georgiou, Das, & Li, 2012). However, to our knowledge, no studies have systematically examined the mechanism underlying the RAN–reading relationship in Chinese. Examining the relationship between RAN and reading in Chinese is important for several reasons. First, Chinese differs from English and other alphabetic orthographies in many respects. Chinese is a morphosyllabic language in which the role of phonology in word reading is not as strong as in English (Hanley, 2005). It has been estimated that only 23% to 26% (when tone is taken into account) of the Chinese characters can be read accurately using the phonetic radical (Chung & Leung, 2008; however, see also Zhou, 1978, for a higher estimate). If RAN is related to reading because it taps the ability to access and retrieve phonological representations from long-term memory, then its contribution to Chinese reading should be relatively weak. Second, Chinese is perhaps the only orthography in which a variation of the orthographic processing account has been predominantly used to explain the unique contribution of RAN to reading. According to Manis, Seidenberg, and Doi (1999), RAN tasks may tap into children’s ability to learn arbitrary associations between symbols and sounds, an ability that is also used in learning to read exception words. Because reading in Chinese requires learning arbitrary connections between characters and their pronunciation (e.g., seeing character ‘‘书book” does not equip the reader with its pronunciation ‘‘shu[1],” where the number in brackets refers to the tone), several researchers have endorsed this hypothesis to justify RAN’s unique contribution to Chinese reading (e.g., McBride-Chang & Ho, 2005; Pan et al., 2011; Shu et al., 2006; Xue, Shu, Li, Li, & Tian, 2013). Unfortunately, this hypothesis has never been tested in Chinese. Third, although there are a few longitudinal studies in Chinese, they mostly covered the developmental period from kindergarten to Grade 2 (e.g., Chow et al., 2005; Lei et al., 2011; McBride-Chang & Ho, 2005; Tong, McBride-Chang, Shu, & Wong, 2009). Pan and colleagues’ (2011) and Song and colleagues’ (in press) longitudinal studies covered a longer developmental period (from kindergarten

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to Grade 4/5), but they assessed RAN only in kindergarten and only with digits, which could be problematic because not all kindergarten children are familiar with digits. In both studies, the contribution of RAN to reading accuracy and fluency in Grade 4/5 was significant but rather small (1–3% of unique variance). Thus, it remains unclear whether RAN continues to predict reading development in upper elementary grades. This is important in light of arguments that the effects of RAN in reading (particularly reading accuracy) weaken in upper grades (e.g., Georgiou, Parrila, & Kirby, 2009; Roman, Kirby, Parrila, Wade-Wooley, & Deacon, 2009; Torgesen et al., 1997). This may be the case in English and other alphabetic orthographies in which all of the letters are introduced in Grade 1 and belong to a limited pool of items (i.e., 26 in English). In Chinese, children in Grade 1 are introduced to only 17% of the characters they are expected to learn in elementary school. If RAN reflects item identification (in addition to retrieval and articulation of item names; see Bowey et al., 2005), its contribution to Chinese word reading should be particularly strong in Grades 2 and 3, the time when children are introduced to 1250 new characters (49% of the body of characters children are expected to learn in elementary school; see Shu, Chen, Anderson, Wu, & Xuan, 2003). Finally, to our knowledge, no studies in Chinese have examined whether the prolonged times of dyslexic children in RAN tasks are due to longer articulation times or pause times. Most studies on dyslexia have examined RAN at a total time level; the children are instructed to name all of the stimuli shown on a card, and the total time to name them is recorded and used as an index of RAN performance. However, Neuhaus, Foorman, Francis, and Carlson (2001) suggested that instead of using RAN total time, we should focus on the intra-RAN components of articulation time (the mean time to articulate the RAN stimuli) and pause time (the mean time of the interstimulus intervals). Previous studies in English have shown that children with dyslexia experience deficits in both articulation time and pause time (Anderson, Podwall, & Jaffe, 1984; Snyder & Downey, 1995). However, this might not be the case in Chinese given that previous studies with unselected samples of children have shown that only pause time was a significant predictor of Chinese reading (e.g., Georgiou, Parrila, & Liao, 2008; Li, Kirby, & Georgiou, 2011).

Study 1 The purpose of Study 1 was to examine the validity of the ‘‘arbitrary” connections hypothesis in Chinese (Manis et al., 1999). If the ability to learn visual–verbal associations is the reason why RAN is related to Chinese reading, then RAN should correlate with measures of Paired Associate Learning (PAL) and, in turn, both should correlate with reading. According to Hulme, Goetz, Gooch, Adams, and Snowling (2007), visual–verbal PAL is fundamental to reading acquisition. More specifically, in learning the letter sounds, children must first learn the visual representation of each letter and the phonological representation that corresponds to each letter and then learn the associations between letters and their sounds. A critical feature of these associations is that they are essentially arbitrary and take time to learn. In light of this, one would expect visual–verbal PAL to be important in Chinese reading because of the relatively arbitrary nature of the visual–verbal associations in the pronunciation of some Chinese characters (particularly in the case of irregular characters for which no clue to pronunciation is available). Hence, the hypothesis is that lack of automaticity in RAN may be (at least partly) the product of poor learning of visual–verbal associations and that difficulty in establishing such connections is also implicated in learning to associate Chinese characters with their spoken equivalents. Previous studies in alphabetic orthographies that examined the relationship between RAN and measures of PAL have provided mixed findings (e.g., Lervåg, Bråten, & Hulme, 2009; Litt, de Jong, van Bergen, & Nation, 2013; Warmington & Hulme, 2012; Wiens, 2005). On the one hand, Warmington and Hulme (2012) reported substantial correlations between RAN and PAL (rs ranged from .47 to .51). On the other hand, Litt and colleagues (2013) reported that the correlation between RAN and PAL was only .06. In Chinese, although no studies have examined the RAN–PAL relationship, there is evidence suggesting that PAL is related to reading. Some studies have shown that Chinese dyslexic children (Li et al., 2009) and Chinese preschool children at familial risk for dyslexia (Ho, Leung, & Cheung, 2011) experience difficulties in visual–verbal PAL tasks. In addition, Huang and

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Hanley (1997) demonstrated that PAL was a strong predictor of Chinese reading. Given that the findings of previous studies examining the RAN–PAL relationship are mixed, we did not formulate a specific hypothesis regarding the role of PAL in RAN and reading in Chinese. Method Participants A total of 182 Grade 2 Mandarin-speaking children (83 girls and 99 boys; mean age = 97.83 months, SD = 3.58) were recruited from public schools in Shanghai, China, to participate in our study. Children came from middle socioeconomic backgrounds (based on parents’ education and occupation), they had average intelligence (based on Nonverbal Matrices; see below), and none was diagnosed with sensory or behavioral disabilities. Parental consent was obtained prior to testing. Measures Nonverbal intelligence. Nonverbal Matrices from the Cognitive Assessment System battery (Naglieri & Das, 1997) was used to assess nonverbal intelligence. Children were presented with a pattern of shapes/geometric designs that had a missing piece and were asked to choose from six options the missing piece that would best complete the matrix. A discontinuation rule of four consecutive errors was applied. A participant’s score was the total number correct (max = 33). The Cronbach’s alpha reliability coefficient in our sample was .94. Rapid automatized naming. Two measures of RAN were administered: Digit Naming and Character Naming. The former was adopted from the RAN/RAS (Rapid Alternating Stimulus) test battery (Wolf & Denckla, 2005), and the latter was developed following the same guidelines as in RAN Digits. Both tasks required participants to name as fast as possible five digits (2, 4, 5, 7, and 9, pronounced as er[4], si[4], wu [3], qi[1], and jiu[3]) or characters (我, 小, 天, 不, and 有, pronounced as wo[3], hsiao[3], tien[1], pu[4], and yu[3]) that were arranged semi-randomly in five rows of 10. Prior to beginning the timed naming, children were asked to name the digits or characters in a practice trial to ensure familiarity. The time to name all of the stimuli was a participant’s score. The number of naming errors was negligible (the mean number of errors was .06 in Digit Naming and .08 in Character Naming), and for this reason it was not considered further. Wolf and Denckla (2005) reported test–retest reliability for Digit Naming to be .89. The correlation between Digit Naming and Character Naming in our sample was .70. Paired associate learning (PAL). Visual–verbal PAL was assessed with two measures, both of which involved pairing six spoken Chinese syllables with novel visual referents. In the first task, six symbols were selected from the Akkadian orthography (see Litt et al., 2013, for the stimuli) and were randomly paired with six common Chinese syllables (mei[2], yuan[2], gong[1], hui[4], qi[3], and xian[4]). In the second task, six pictures of imaginary animals were selected from the Commentary on the Classic of Mountains and Seas (Shan Hai Jing Guang Zhu) and were randomly paired with a different set of six common Chinese syllables (hun[2], keng[1], lian[4], da[3], xuan[2], and nie[4]). In both tasks, children were instructed to learn the names of the unfamiliar symbols or animals. During the practice trial, the pictures of the symbols or animals were shown to children one at a time for approximately 5 s with the examiner saying their corresponding names aloud. Children were then asked to repeat the names of the symbols or animals, and the examiner would correct them when false or vague repetition was provided. In the test trials, the pictures of the six symbols or animals were presented to children one by one in random order, and children were asked to provide the name of each picture. One point was awarded for each correct response. The test consisted of six blocks of six trials in each block (max = 36). If a child correctly named all six pictures in two successive blocks, the task was discontinued and the examiner assigned a full score for the remaining trials. The Cronbach’s alpha reliability coefficient in our sample was .80 for symbols and .83 for animals. Reading. Reading was assessed with two measures that were adapted from the Hong Kong Test of Specific Learning Difficulties in Reading and Writing (HKT–SpLD): Character Recognition and One-Minute Reading (Ho, Chan, Tsang, & Lee, 2000). In Character Recognition, children were asked to read aloud

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150 Chinese two-character words that were arranged in terms of difficulty. A participant’s score was the total number of characters read correctly. The Cronbach’s alpha reliability coefficient in our sample was .94. In One-Minute Reading, children were asked to read aloud 140 Chinese two-character words as fast as possible. A participant’s score was the number of characters read correctly within a 1-min time limit. The split-half reliability coefficient in our sample was .88. Procedure All children were tested individually during April/May of the school year by trained graduate students. Testing was conducted in a quiet room at school during school hours. The tasks were administered in a fixed order: first we administered the Nonverbal Matrices, then the two RAN tasks, then the two PAL tasks, and finally the two reading tasks. Results and discussion Descriptive statistics and correlations between the measures are presented in Table 1. Both RAN tasks correlated strongly with One-Minute Reading but only weakly with Character Recognition. This finding is in line with that of previous studies showing that RAN is more strongly related to reading fluency than to reading accuracy (e.g., Georgiou et al., 2009; Liao et al., 2008; Savage & Frederickson, 2005; Song et al., in press). The PAL tasks did not correlate significantly with the RAN tasks, and for this reason we did not further examine the possibility of PAL mediating the RAN–reading relationship. The hypothesis that RAN is related to Chinese reading because it taps the ability of children to learn arbitrary connections is not supported in our study. Certainly, this finding needs to be replicated in a future study that will cover a longer developmental period and employ PAL measures that are more sensitive (i.e., trials to criterion). There may be three explanations for the nonsignificant relationship of PAL to RAN and reading. First, the association between a character and its pronunciation might not be as arbitrary as initially thought. Some researchers have argued that phonology in Chinese, just like in English, plays a central role in visual word recognition (see e.g., Leong, 1991; Leong & Tamaoka, 1998; Perfetti & Zhang, 1995; Tan & Perfetti, 1998). Leong (1991), for example, argued that ‘‘it should not be assumed that Chinese characters are learned by rote memory and that these lexical units are not amenable to some kind of phonological processing” (p. 234). Tan and Perfetti (1998) also pointed out that, in Chinese, phonological information is activated at the same moment as the complete identification of a character’s orthographic information. If phonology plays a central role in character recognition (see also ‘‘identification with phonology” hypothesis proposed by Perfetti & Zhang, 1995), then it might not be surprising that PAL tasks did not correlate significantly with either RAN or reading. An alternative explanation may relate to the way PAL tasks are scored. If RAN reflects the time it takes to retrieve the verbal component of a visual–verbal association when presented with the visual stimulus, then the question should be how fast a child learns the connection and not whether the child has formed a connection. In other words, the score in PAL should be response time and not accuracy. Certainly, this needs to be examined in a future study. Finally, our sample consisted of Grade 2 children. Although we selected this

Table 1 Descriptive statistics and correlations between measures used in Study 1.

1 2 3 4 5 6 7

Matrices RAN digits RAN characters PAL letters PAL objects CHR OMD

M

SD

19.71 22.22 25.28 20.20 25.38 115.87 82.65

4.16 4.37 4.39 7.10 6.56 15.41 15.05

1

2 .01

Note. CHR, Character Recognition; OMR, One-Minute Reading. N = 182. * p < .05. ** p < .01.

3

4 .06 .70**

.26* .11 .01

5

6 .14 .02 .06 .36**

7 .08 .27** .32** .14 .20**

.02 .62** .61** .02 .12 .50**

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grade level because in Grade 2 children are introduced to disproportionally more new characters (750 of them) than in any other grade (see Shu et al., 2003), their ability to learn arbitrary associations may have already been formed and, thus, may contribute little to variation in RAN and reading. Study 2 Because in Study 1 we failed to find evidence in support of the arbitrary connections hypothesis, in Study 2 we turned our interest to the popular hypotheses regarding the RAN–reading relationship used in alphabetic orthographies: the phonological processing, orthographic processing, and speed of processing hypotheses. Researchers who contrasted these hypotheses in different alphabetic orthographies (e.g., Georgiou et al., 2013; Poulsen, Juul, & Elbro, in press; Powell et al., 2007) agree that orthographic consistency may affect the strength of the processes underlying the RAN–reading relationship over time but not the processes per se. Could it then be the case that the mechanisms underlying the RAN–reading relationship in Chinese are the same as the ones identified in alphabetic orthographies? In Study 2, we employed a longitudinal design following the same children from Grade 2 to Grade 5, and we assessed both reading accuracy and fluency. We selected Grade 2 as our departure point because most previous longitudinal studies examining the role of RAN in word reading in Chinese covered the developmental span from kindergarten to Grade 2 (e.g., Chow et al., 2005; Lei et al., 2011; Li, Shu, McBride-Chang, Liu, & Peng, 2012; McBride-Chang & Ho, 2005). Information regarding the contribution of RAN to reading in upper elementary grades is limited and derived primarily from concurrent and cross-sectional studies (e.g., Liao et al., 2008; Tan et al., 2005; Xue et al., 2013). These studies have shown that RAN continues to predict reading over and above phonological awareness (e. g., Liao et al., 2008; Tan et al., 2005) and orthographic processing (e.g., Li et al., 2012; Liao et al., 2008; Yeung et al., 2011) but have not examined the joint effects of these processing skills along with processing speed in the RAN–reading relationship. This is important in light of arguments that RAN operates as a ‘‘lexical midpoint” in a cascaded system of processing speed effects (Wolf & Bowers, 1999). Method Participants A total of 80 Grade 2 Mandarin-speaking Taiwanese children (41 girls and 39 boys) from four public inner-city schools in Taichung City, Taiwan, participated in Study 2. The children were followed from Grade 2 to Grade 5 and were assessed three times (at the end of Grades 2, 3, and 5). In Grade 5, our sample consisted of 72 children (38 girls and 34 boys) after 5 children withdrew from the study in Grade 3 and 2 more withdrew in Grade 5. Their performance on the measures administered in Grade 2 was within the average range, and the exclusion of their scores did not affect the distributional properties of the variables. Children in our sample came from families of middle socioeconomic background (based on parents’ education and occupation), they had average intelligence (based on Raven’s Progressive Matrices; see below), and none had any documented sensory or behavioral difficulties. The mean age of the participants who were available at all measurement points and with whom the analyses were performed was 98.23 months (SD = 3.60) in Grade 2, 112.56 months (SD = 3.98) in Grade 3, and 135.42 months (SD = 3.49) in Grade 5. Parental consent was obtained prior to each testing. Measures Nonverbal intelligence. Raven’s Progressive Matrices were used to assess children’s nonverbal intelligence (Raven, Raven, & Court, 1998). The task consisted of three sets of 12 items (Sets A, Ab, and B) and required children to select one of the provided options to fill out a pattern. A participant’s score was the total number correct. The Cronbach’s alpha reliability coefficient in our sample was .90. Rapid automatized naming. Two measures of RAN were administered: Digit Naming and Color Naming. The tasks were adapted from the RAN/RAS test battery (Wolf & Denckla, 2005) and required partici-

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pants to name as fast as possible five digits (2, 4, 5, 7, and 9, pronounced as er[4], si[4], wu[3], qi[1], and jiu[3]) or colors (blue, green, red, yellow, and black, pronounced as lan[4], lyu[4], hong[4], huang [2], and hay[1]) that were arranged semi-randomly in five rows of 10. Prior to beginning the timed naming, children were asked to name the digits or colors in a practice trial to ensure familiarity. The time to name all of the stimuli was a participant’s score. Only a few errors occurred (the mean number of errors was .06 in Digit Naming and .61 in Color Naming), and for this reason they were not considered further. Wolf and Denckla (2005) reported test–retest reliability for Digit Naming and Color Naming to be .89 and .90, respectively. In our sample, Digit Naming correlated .56 with Color Naming. Phonological awareness. A sound detection task was used to assess children’s phonological awareness. We chose this task because of the onset and rime characteristics of Chinese syllables (Cheung & Ng, 2003). After listening to four syllables, children were asked to tell which syllable had a different initial sound (12 items) or final sound (12 items). For example, after listening to fu[1] 夫, ma[1] 媽, mi[1] 咪, and mo[1] 摸, children were asked to choose the syllable that had a different initial sound than the other three. Single-syllable words were used in the task. The tones of the syllables were controlled such that all four syllables in each trial were in the same tone. A participant’s score was the total number of correct answers (max = 24). The Cronbach’s alpha reliability coefficient in our sample was .83. Orthographic processing. A character dictation task was used to assess children’s orthographic processing. Children were asked to write down a character that the examiner dictated to them. Following standard administration procedures, the examiner would first pronounce each character in isolation, then pronounce it within a sentence, and finally repeat the character in isolation. The characters were arranged in terms of difficulty. A discontinuation rule of 10 consecutive errors was applied. A participant’s score was the total number of correctly spelled characters (max = 45). The Cronbach’s alpha reliability coefficient in our sample was .90. Speed of processing. Visual Matching from the Woodcock–Johnson Tests of Cognitive Ability (Woodcock & Johnson, 1989) was used to assess speed of processing. Children were asked to circle identical numbers dispersed in 60 rows. Each of the 60 rows in the task contained six digits, two of which were identical (e.g., 8 9 5 2 9 7), and children were asked to circle the identical digits in each row. Children completed four practice items prior to timed testing. A participant’s score was the total number of rows completed correctly within a 3-min time limit. Woodcock, McGrew, and Mather (2001) reported the test–retest reliability coefficient to be .87 for 7- to 11-year-old children. Reading. Reading was assessed with two measures: Graded Chinese Character Recognition and One-Minute Reading. The Graded Chinese Character Recognition task (Huang, 2001) is a standardized reading measure used in Taiwan for children in Grades 1 to 9. Children were asked to read aloud 150 Chinese two-character words (200 in Grade 5) that were arranged in terms of difficulty. The average frequency of characters in the test was 3541, and the average number of strokes was 12.53. A participant’s score was the total number of correctly read characters. The Cronbach’s alpha reliability coefficient in our sample was .87 in Grade 2 and .91 in Grades 3 and 5. One-Minute Reading was adapted from the HKT–SpLD (Ho et al., 2000). Children were asked to read aloud as fast and accurately as possible 90 two-character Chinese words. The words were selected from Grade 1 to 6 Chinese language textbooks and had a mean frequency of 224. A participant’s score was the number of characters read correctly within a 1-min time limit. The split-half reliability coefficient in our sample was .85 in Grades 2 and 3 and .90 in Grade 5. Procedure All children were tested individually during April/May of the school year by the first author and two graduate students who received extensive training on how to administer the tests. Testing was conducted in a quiet room at school during school hours. In Grade 2, children were assessed in nonverbal cognitive ability, phonological awareness, orthographic processing, speed of processing, RAN, and reading. In Grades 3 and 5, children were assessed only in reading.

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Results and discussion Table 2 presents the descriptive statistics for all of the measures used in Study 2, and Table 3 presents the correlations between the measures. RAN Digits correlated strongly with One-Minute Reading, and the correlations remained stable across time (rs ranged from .65 to .69). The correlations of RAN Colors with the reading measures were lower and in one instance (the correlation with Character Recognition in Grade 5) failed to reach significance. Significant correlations were also observed between the RAN tasks and phonological awareness, orthographic processing, and speed of processing, with the highest correlation being between RAN Digits and orthographic processing (r = .53). To examine whether RAN shares its predictive variance on reading with phonological awareness, orthographic processing, and speed of processing, a series of hierarchical regression analyses was conducted. Because RAN Digits correlated more strongly with reading than RAN Colors, the regression analyses were performed with RAN Digits only. The order of the variables entered in the regression equation was as follows. First, after controlling for the effects of nonverbal IQ, RAN was entered at Step 2 of the regression equation to estimate its unique effect on reading. Second, RAN was entered in the regression equation following nonverbal IQ (entered at Step 1) and speed of processing, phonological awareness, and orthographic processing (entered interchangeably at Step 2). Third, the contribution of RAN to reading was estimated after controlling for nonverbal IQ (entered at Step 1) and speed of processing, phonological awareness, and orthographic processing (entered as a block at Step 2). An additional regression analysis was performed entering autoregressor (reading ability at an earlier point in time) at Step 1 of the regression equation followed by RAN Digits at Step 2. Table 4 presents the results of the regression analyses with Character Recognition as the dependent variable, and Table 5 presents the results of the regression analyses with One-Minute Reading as the dependent variable. Standardized beta coefficients, levels of significance, and R2 changes are shown in both Tables 4 and 5. When Character Recognition was the dependent variable, the contribution of RAN was eliminated after controlling for the effects of nonverbal IQ and orthographic processing. When we controlled for nonverbal IQ and speed of processing or phonological awareness, RAN continued to explain 6% to 12% of unique variance in Character Recognition. In contrast, when One-Minute Reading was the dependent variable, RAN Digits continued to account for a sizable amount of unique variance (19–22%) even after controlling for nonverbal IQ, phonological awareness, orthographic processing, and speed of processing. RAN also accounted for 4% to 6% of unique variance in One-Minute Reading after controlling for the effects of the autoregressor. Although this is a relatively small amount of unique variance, it suggests that RAN can account for unexpected growth in reading fluency. These findings are similar to those in previous studies in alphabetic orthographies (e.g., Georgiou et al., 2009; Landerl & Wimmer, 2008; Savage & Frederickson, 2005; Vaessen & Blomert, 2010) and in Chinese (e.g., Liao et al., 2008; Song et al., in press; Xue et al., 2013), and they suggest that RAN (particularly Digit Naming) is a strong predictor of reading fluency. Importantly, RAN accounted for 19% to 22% of unique variance in reading fluency over and above the effects of nonverbal IQ, phonological awareness, orthographic processing, and speed of processing. This suggests that we cannot Table 2 Descriptive statistics of measures used in Study 2. Grade 2

Nonverbal IQ RAN digitsa RAN colorsa Sound detection Spelling dictation Visual matching Character recognition One-Minute Reading a

Measured in seconds.

Grade 3

Grade 5

M

SD

M

SD

M

SD

35.27 25.09 47.61 17.52 28.43 40.01 55.45 71.32

10.55 5.47 10.31 5.14 6.18 5.55 22.92 14.61

67.88 79.04

24.11 16.40

118.77 95.79

22.00 17.93

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Table 3 Correlations between all measures used in Study 2. 1. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Nonverbal IQ RAN digits RAN Colors Sound detection Spelling dictation Visual matching CHR, Grade 2 OMR, Grade 2 CHR, Grade 3 OMR, Grade 3 CHR, Grade 5 OMR, Grade 5

2.

3.

.29*

4.

.48** .56**

5.

.43** .30* .11

.41** .53** .29* .44**

6.

7.

.46** .42** .35** .31** .51**

.36** .48** .27* .43** .54** .34**

8.

9.

.41** .69** .38** .39** .45** .47** .52**

.45** .48** .25* .40** .67** .38** .76** .59**

10.

11.

.37** .67** .36** .37** .42** .47** .43** .70** .53**

.41** .45** .18 .42** .63** .26* .67** .57** .86** .47**

12. .33** .65** .31** .35** .42** .45** .48** .75** .54** .75** .57**

Note. CHR, Character Recognition; OMR, One-Minute Reading. N = 72. * p < .05. ** p < .01.

Table 4 Results of hierarchical regression analyses with Grade 2 RAN Digits as a predictor of character recognition in Grades 2, 3, and 5. Step

Variable

Character recognition Grade 2

Grade 3

DR

2

b **

DR

b ***

***

DR 2

b

1 2

Nonverbal IQ RAN digits

.356 .405***

.12 .14***

.451 .380***

.20 .13***

.375 .369***

.14*** .13***

2 3

SOP RAN digits

.244* .345*

.05* .10**

.206* .334***

.04* .10**

.140 .375***

.02 .12***

2 3

PA RAN digits

.393*** .310**

.13*** .08**

.322*** .271*

.09** .07*

.390*** .274*

.13*** .07*

2 3

OP RAN digits

.480*** .200

.19*** .03

.589*** .191

.29*** .03

.556*** .189

.26*** .03

2

.018 .299** .463*** .190

.35**

.020 .263** .495*** .189

.33**

.03

.127 .278** .504*** .172

.32**

3

SOP PA OP RAN digits

.02

1 2

Autoregressor RAN digits

.685*** .118

.41** .01

.852*** .102

.58** .01

.03

**

Grade 5 2

***

Note. SOP, Speed of Processing; PA, Phonological Awareness; OP, Orthographic Processing. N = 72. * p < .05. ** p < .01. *** p < .001.

explain the RAN–reading relationship (at least when reading fluency is concerned) by employing a single theoretical account. More than half of RAN’s predictive value in reading fluency remained unexplained. At the same time, we should be cautious when interpreting the high contribution of orthographic processing to the RAN–reading accuracy relationship because we used a spelling dictation task to assess orthographic processing. Certainly, if children know how to write a character, then they also know how to read it. Reading in Chinese has also been found to depend on writing (Tan et al., 2005).

Study 3 In Studies 1 and 2, we assessed unselected samples of children. However, we also know that RAN differentiates children with and without dyslexia (e.g., Chung et al., 2008; Ho & Lai, 1999; Ho et al.,

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Table 5 Results of hierarchical regression analyses with Grade 2 RAN Digits as a predictor of One-Minute Reading in Grades 2, 3, and 5. Step

Variable

One-Minute Reading Grade 2

Grade 3

DR 2

b .407 .628***

.17 .36***

.362 .612***

.13 .34***

.327 .608***

.11** .34***

2 3

SOP RAN digits

.346** .599***

.12** .31***

.387*** .581***

.13*** .29***

.373*** .576***

.12*** .29***

2 3

PA RAN digits

.304** .599***

.08** .29***

.373** .556***

.11*** .25***

.267* .603***

.06* .31***

2 3

OP RAN digits

.357** .580***

.11** .27***

.281* .590***

.07* .28***

.341*** .563***

.10** .26***

2

SOP PA OP RAN digits

.285* .151 .298* .556***

.17**

.315* .063 .279* .518***

.19***

.284* .062 .291* .545***

.17**

***

.47** .04**

Autoregressor RAN digits

.22***

**

***

.663 .345**

**

DR2

Nonverbal IQ RAN digits

1 2

***

b

1 2

3

**

Grade 5

DR2

b

.19*** **

.37 .06**

**

.732 .268**

.21***

Note. SOP, Speed of Processing; PA, Phonological Awareness; OP, Orthographic Processing. N = 72. * p < .05. ** p < .01. *** p < .001.

2004; McBride-Chang et al., 2013; Shu et al., 2006; Wang et al., 2012). Thus, the purpose of Study 3 was to examine the role of RAN in developmental dyslexia in Chinese and, similarly to Study 2, to contrast the prominent theoretical explanations of the RAN–reading relationship in this group of children. Based on the findings of Study 2, any group differences in RAN should be eliminated after controlling for orthographic processing but not phonological awareness or speed of processing. To further examine whether our choice of orthographic processing task in Study 2 inflated its contribution to the RAN–reading relationship, in Study 3 we employed both spelling dictation and a more acceptable measure of orthographic processing in Chinese (Non-Character Recognition; see below). In addition, we examined what components of RAN (articulation time, pause time, or both) may account for the observed group differences. The studies that decomposed the RAN total times into articulation time and pause time in Chinese have been conducted with typically developing children (Georgiou et al., 2008; Li, Shu, et al., 2012). Decomposing the RAN total times will allow us to obtain a finer picture of where the problem lies when dyslexic children take longer to name the same set of visual stimuli compared with controls. Method Participants Our sample consisted of 30 Grade 4 children with dyslexia (16 boys and 14 girls; mean age = 122.28 months, SD = 3.37) and 30 chronological-age controls (16 boys and 14 girls; mean age = 123.16 months, SD = 2.73). All children were native speakers of Mandarin and attended public schools in Taichung City, Taiwan. Children with dyslexia were initially nominated by their teachers as having significant reading difficulties. Those nominated were subsequently tested on a measure of nonverbal IQ (Raven’s Matrices) and on a standardized measure of reading accuracy (Character Recognition; Huang, 2001). Children with a nonverbal IQ score higher than 85 and a Character Recognition score at least 1.5 years behind their chronological age made up the dyslexia group. None of these children was experiencing any sensory or behavioral problems. The chronological-age controls were selected from the same schools as the dyslexic children and were matched to the dyslexic children on nonverbal IQ, gender, and age. Parental consent was obtained prior to testing.

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Materials For nonverbal IQ, phonological awareness, speed of processing, and reading, we used the same measures as in Study 2. To assess RAN, we used three measures: Digit Naming, Character Naming, and Color Naming. The RAN tasks were the same as those administered in Studies 1 and 2. Finally, to assess orthographic processing, we administered spelling dictation (same as in Study 2) and the Non-Character Recognition task. This is similar to the Lexical Decision task in HKT–SpLD (Ho et al., 2000). Two kinds of stimuli were used: rare characters that are unfamiliar to elementary school students and non-characters that are ill-formed because they violate Chinese character formation rules. The formation of Chinese characters follows a set of graphic principles that involve rules of positional regularity for the constituent components (Ho, Yau, & Au, 2003). For example, the grass radical ‘‘艹” can appear only on the top of characters such as 花 flower, 蓮 lotus, and 葉 leaf. If ‘‘艹” appears at the bottom of the character, it will become a non-character. The test consisted of 60 items (30 left– right structured rare characters and 30 non-characters). Children were asked to cross out all of the non-characters. One point was given for each rare character not crossed out, and one point was given for each non-character correctly crossed out (max = 60). The Cronbach’s alpha reliability coefficient in our sample was .78. Procedure All children were tested individually during April/May of the school year by two graduate students with experience in psychoeducational assessments. Testing was conducted in a quiet room at school during school hours. The order of administering the tests was fixed: first we administered the three RAN tasks, then the phonological awareness task, then the orthographic processing tasks, and finally the speed of processing task. Manipulation of sound files The sound files containing Digit Naming, Character Naming, and Color Naming responses for each participant were analyzed using a sound editing program (GoldWave, Version 4.26). Data extraction was completed following the procedure described in detail in Georgiou, Parrila, and Kirby (2006). Articulation time represents the mean of those articulation times that were correctly verbalized and were not preceded by a skipped stimulus. The maximum number of articulation times was 50. Interrater reliability for articulation time in our study ranged from .92 to .94. In turn, pause represents the mean of the pause times that occurred between two correctly articulated stimuli. The maximum number of pause times was 49. Interrater reliability for pause time in our study ranged from .89 to .93. Results and discussion Table 6 presents the descriptive statistics on all of the measures used in Study 3 separately for each group. Before conducting any analyses, we examined the distributional properties of the variables within each group. In the control group, phonological awareness was negatively skewed. Unfortunately, no transformation could resolve the problem, and for this reason we kept the variable as is. In the dyslexic group, all RAN total times and pause times were positively skewed. This was due to the presence of two outliers in Color Naming, one outlier in Digit Naming, and one outlier in Character Naming. The outliers in the RAN total times were the same individuals as those in the RAN pause times. After winsorizing the scores of the outliers, the distributions became normal. The subsequent analyses were performed with the winsorized data. The results of one-way analyses of variance (ANOVAs) showed that dyslexic children performed significantly poorer than chronological-age controls on Character Recognition, F(1, 58) = 240.08, p < .001, One-Minute Reading, F(1, 58) = 20.93, p < .001, Sound Detection, F(1, 58) = 36.07, p < .001, Spelling Dictation, F(1, 58) = 62.76, p < .001, Non-Character Recognition, F(1, 58) = 9.99, p < .01, and Visual Matching, F(1, 58) = 14.80, p < .001. In addition, one-way multivariate analysis of variance (MANOVA) with RAN total times as dependent variables and group as a fixed factor showed a significant effect of group (Wilks’ k = .699), F(3, 56) = 5.30, p < .01. Dyslexic children performed significantly slower than controls on all RAN tasks (Color Naming: F(1, 58) = 11.17, p < .01; Digit Naming: F(1, 58) = 9.74, p < .01; and Character Naming: F(1, 58) = 11.59, p < .01). To further examine whether the group

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C.-H. Liao et al. / Journal of Experimental Child Psychology 130 (2015) 106–122 Table 6 Descriptive statistics on measures used in study for each group separately. Dyslexics (n = 30)

Controls (n = 30)

F

M

SD

M

SD

Nonverbal IQ Character recognition One-Minute Reading Sound detection Spelling dictation Non-character recognition Visual matching

99.52 51.10 64.89 15.29 31.00 49.52 35.43

9.55 13.51 15.41 3.95 6.91 3.66 4.73

103.05 120.70 84.47 21.15 42.40 55.30 41.15

11.66 15.23 11.62 1.90 1.70 3.22 4.79

1.59 240.08*** 20.93*** 36.07*** 62.76*** 9.99** 14.80***

RAN digits Digits AT Digits PT

27.55 .31 .20

6.44 .05 .05

22.56 .32 .10

4.54 .06 .05

9.74** 0.48 31.00***

RAN Characters Characters AT Characters PT

32.56 .39 .26

5.49 .06 .06

27.42 .38 .16

4.53 .06 .05

11.59** 0.51 33.09***

RAN Colors Colors AT Colors PT

51.13 .54 .44

8.31 .08 .09

46.29 .49 .39

7.13 .10 .13

11.17** 2.74 14.54**

Note. AT, Articulation Time; PT, Pause Time. ** p < .01. *** p < 001.

differences remain after controlling for speed of processing, phonological awareness, and orthographic processing, we performed four separate multivariate analyses of covariance (MANCOVAs). In line with our expectation, the results showed that the group differences remained significant after covarying for speed of processing (Wilks’ k = .681), F(3, 55) = 6.11, p < .01, and phonological awareness (Wilks’ k = .719), F(3, 55) = 4.68, p < .01, but were eliminated after covarying for either spelling dictation (Wilks’ k = .871), F(2, 55) = 1.78, p = .168) or Non-Character Recognition (Wilks’ k = .800), F(2, 55) = 2.10, p = .058. These findings complement those of Study 2 but also indicate that the use of spelling dictation in Study 2 likely inflated the role of orthographic processing in the RAN–reading relationship. Although covarying for Non-Character Recognition also diminished group differences in RAN, it did not have the same impact as spelling dictation. Next, we examined whether the slower RAN performance of dyslexics was due to slower articulation times, pause times, or both by performing two separate MANOVAs: one with articulation time as a dependent variable and one with pause time as a dependent variable. The results indicated a significant effect of group for pause times (Wilks’ k = .484), F(2, 57) = 17.61, p < .01, but not for articulation times (Wilks’ k = .925), F(2, 57) = 1.33, p = .277. Follow-up univariate analyses for pause time indicated that the two groups differed in all tasks (Color Naming: F(1, 58) = 14.54, p < .01; Digit Naming: F(1, 58) = 31.00, p < .001; and Character Naming: F(1, 58) = 33.09, p < .001). These findings suggest that the locus of difficulties in children with dyslexia lies in the processes incorporated within pause time (shifting of attention, lexical access, and motor programming). An explanation for the absence of significant differences between groups in articulation time may be the short names of the RAN stimuli in Chinese. Certainly, when a child pronounces digits such as 2 (er[4]) and 4 (si[4]), there is not a lot of room for variation (how much faster could someone pronounce these digits?). Subsequently, any variability in RAN total time is determined by the length of pauses.

General discussion There is little doubt that RAN is an important predictor of reading ability in Chinese (e.g., Lei et al., 2011; Li, Shu, et al., 2012; Liao et al., 2008; McBride-Chang & Ho, 2005; McBride-Chang, Shu, Zhou, Wat, & Wagner, 2003; Shu et al., 2006; Tan et al., 2005; Xue et al., 2013; Yeung et al., 2011). Research-

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ers have speculated that this is due to the arbitrary connections between Chinese characters and their spoken equivalents, which is analogous to the way digits are arbitrarily mapped to their pronunciation (e.g., Pan et al., 2011; Shu et al., 2006; Xue et al., 2013). Thus, in this study, we sought to examine the mechanism that is responsible for the RAN–reading relationship in Chinese. In doing so, we employed both alphanumeric and non-alphanumeric RAN tasks, distinguished between reading accuracy and fluency, and assessed both typically developing children and children with dyslexia. The correlations between RAN and reading (see Studies 1 and 2) were of the same size as those reported in previous studies with children of the same age in Chinese (e.g., Li, Shu, et al., 2012; Liao et al., 2008; Tan et al., 2005; Xue et al., 2013) and in other alphabetic orthographies (e.g., Georgiou et al., 2013; Powell et al., 2007; Savage & Frederickson, 2005; Vaessen & Blomert, 2010). In addition, similar to what has been found in alphabetic orthographies (e.g., Georgiou et al., 2009; Savage & Frederickson, 2005), RAN correlated more strongly with reading fluency than with reading accuracy. This suggests that even in the context of Chinese, there is a processing skill that boosts the RAN– reading fluency relationship. Obviously, this processing skill is not speed of processing because in Study 2 we found that RAN Digits continued to predict reading fluency even after controlling for speed of processing (see Table 5). There may be two explanations for the stronger relationship of RAN with reading fluency. First, it may be related to the content of each reading task. One-Minute Reading includes mostly highfrequency words (e.g., 學校 school, 高興 happy, 天空 sky). These words are recognized and named as fast as single digits or characters included in the RAN tasks. On the other hand, in Character Recognition, words are graded in terms of difficulty and do not enjoy the same level of automaticity as those included in One-Minute Reading. This important difference may give rise to the RAN–reading fluency relationship. A more plausible explanation relates to the presentation of the reading tasks. Because the words in One-Minute Reading are all presented in one card (as in the RAN tasks), this allows children to use parafoveal information. Recent studies in Chinese have demonstrated that dyslexic children do not benefit from parafoveal processing as much as normal readers during the RAN tasks (e.g., Pan, Yan, Laubrock, Shu, & Kliegl, 2013; Yan, Pan, Laubrock, Kliegl, & Shu, 2013). In contrast, in Character Recognition, words are presented individually, which takes away the benefit of using parafoveal information. Certainly, this hypothesis needs to be examined further. We did not find evidence in support of the dominant explanation of the RAN–reading relationship in Chinese, namely that it is due to the arbitrary connections between orthography (characters) and phonology (characters’ pronunciation). The results of Study 1 showed that neither one of the PAL tasks was associated with RAN. Although we acknowledge the limitations of our PAL measures and the concurrent nature of our study, another line of research seems to support our findings. If the relationship of RAN to reading was due to the arbitrary connections, we should also see significantly larger correlations between RAN and reading in Chinese than in consistent orthographies such as Finnish, Greek, and Dutch, where the connections are not arbitrary. However, cross-linguistic studies have shown that the correlations in Chinese are similar to (in some cases even lower than) those in consistent orthographies (e.g., Georgiou et al., 2008; McBride-Chang & Kail, 2002; Smythe et al., 2008). In contrast, we showed that it is primarily orthographic processing that explains the RAN–reading accuracy relationship (Study 2). This finding was replicated in Study 3, where the observed differences between dyslexics (identified with the use of Character Recognition) and controls in RAN disappeared after covarying for the effects of orthographic processing. Taken together, this means that it is not the ability to learn arbitrary connections that matters in the RAN–reading relationship; rather, it is how readily available existing orthographic representations are (in the case of Chinese, it would be the name of the character) in long-term memory. Our findings differ from those of previous studies that reported unique contributions of RAN to Character Recognition (e.g., Li, Shu, et al., 2012; Luo et al., 2013; McBride-Chang et al., 2003; Tan et al., 2005; Xue et al., 2013). This can be attributed to two factors. First, it can be attributed to differences between studies in the cognitive processes assessed and controlled for before entering RAN in the regression equation. Some researchers reported significant contributions of RAN to Character Recognition but did not assess orthographic processing (e.g., Luo et al., 2013; McBride-Chang et al., 2003; Shu et al., 2006). If orthographic processing is related to RAN and is not controlled for when predicting reading, then there is room left for RAN to account for unique variance. Second, it

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could be attributed to differences in the orthographic processing tasks used in different studies. Recently, Liao, Georgiou, and Parrila (2014) found that different orthographic processing tasks relate differently to RAN. For example, whereas Non-Character Recognition was strongly related to RAN Digits (r = .48), Radical Position (a test measuring children’s knowledge of positional regularity) was weakly related to RAN Digits (r = .25). Some limitations of this study are worth mentioning. First, our samples consisted of Mandarinspeaking children. The extent to which our findings generalize to Cantonese-speaking children remains unknown. However, because the findings of previous studies on RAN and reading in Cantonese were similar to those in Mandarin (e.g., McBride-Chang, Chow, Zhong, Burgess, & Hayward, 2005; McBride-Chang et al., 2013), we could argue with some confidence that there would be no significant differences across the two languages. Second, the children in Study 1 learned to read simplified Chinese characters, and the children in Studies 2 and 3 learned to read traditional Chinese characters. Although the language spoken across studies was the same (Mandarin), we acknowledge that learning to read traditional Chinese characters may have added a level of complexity. The extent to which differences in the form of Chinese learned in school affect the contribution of different cognitive processes remains unknown. However, because the studies in Chinese that employed different languages (Cantonese and Mandarin) have provided similar results (e.g., McBride-Chang et al., 2005, 2013), we do not have a reason to believe that the findings within the same language will differ. Third, Visual Matching was used to operationalize speed of processing. Although it is perhaps the most common measure of processing speed used in studies that have examined the RAN–reading relationship (e.g., Bowey et al., 2005; Georgiou et al., 2009), it is not a pure measure of speed. Because strategies such as scanning and sequencing are also involved in the task, participants’ scores may be more a function of these strategies than of speed per se. Other speed measures of more automatic processing should be used in future studies. Fourth, the spelling dictation task we used in Studies 2 and 3 to operationalize orthographic processing is too close to reading, which may partly explain its strong relationship to Character Recognition and One-Minute Reading. Although spelling dictation has been found to load on the same factor as other orthographic processing measures (Cunningham, Perry, & Stanovich, 2001), we must be cautions when interpreting the role of orthographic processing in our study for the reason mentioned above. Finally, we acknowledge that our interest was to examine the relationship between RAN and word reading, and that is why we did not assess reading comprehension. If word reading is the best predictor of reading comprehension in Chinese (e.g., Li, McBride-Chang, Wong, & Shu, 2012; Yeung, Ho, Chan, Chung, & Wong, 2013) and RAN predicts reading comprehension through word reading (Yeung et al., 2013), then we may also try to understand first why RAN is related to word reading before we target the RAN–reading comprehension relationship. To conclude, our findings add to a small but growing number of studies examining the mechanism underlying the RAN–reading relationship (e.g., Cutting & Denckla, 2001; Georgiou et al., 2013; Poulsen et al., in press; Powell et al., 2007). Similar to the findings of studies conducted in alphabetic orthographies, nonverbal IQ and orthographic processing were sufficient to explain the RAN–reading accuracy relationship in Chinese. However, RAN continued to predict reading fluency over and above the effects of speed of processing, phonological awareness, and orthographic processing. Taken together, these findings suggest that despite the significant differences between Chinese and alphabetic orthographies, the differences in the tasks used to operationalize phonological awareness and orthographic processing across languages, and differences in reading instruction, the mechanisms underlying the RAN–reading relationship are likely the same and their contribution is determined by the time when the RAN–reading relationship is examined. In that sense, orthographic transparency affects the time when different cognitive processes will exert their contribution to the RAN–reading relationship, not the mediators per se. Future studies need to contrast these theoretical hypotheses across languages and across time. References Anderson, S. W., Podwall, F. N., & Jaffe, J. (1984). Timing analysis of coding and articulation processes in dyslexia. Annals of the New York Academy of Sciences, 433, 71–86. Bowers, P. G. (1995). Tracing symbol naming speed’s unique contributions to reading disabilities over time. Reading and Writing, 7, 189–216.

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