Phonological recoding, rapid automatized naming, and orthographic knowledge

Journal of Experimental Child Psychology 116 (2013) 738–746

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Brief Report

Phonological recoding, rapid automatized naming, and orthographic knowledge Susan J. Loveall a,⇑, Marie Moore Channell a,b, B. Allyson Phillips a, Frances A. Conners a a b

Department of Psychology, University of Alabama, Tuscaloosa, AL 35487, USA University of California Davis MIND Institute, Sacramento, CA 95817, USA

a r t i c l e

i n f o

Article history: Received 24 July 2012 Revised 10 May 2013 Available online 1 July 2013 Keywords: Orthographic knowledge Rapid automatized naming Phonological recoding Word identification Orthographic processing Reading

a b s t r a c t Phonological recoding, orthographic knowledge, and rapid automatized naming (RAN) are three major contributors to word identification. However, the interrelations between these components remain somewhat unclear. The current analyses focus on how phonological recoding and alphanumeric versus non-alphanumeric RAN contribute to different components of orthographic knowledge (word specific vs. general). Results indicate that alphanumeric and non-alphanumeric RAN contribute to orthographic knowledge components differently. Alphanumeric RAN relates more to wordspecific orthographic knowledge, whereas non-alphanumeric RAN relates more to general orthographic knowledge. Furthermore, phonological recoding is more closely related to word-specific orthographic knowledge than to general orthographic knowledge. Ó 2013 Elsevier Inc. All rights reserved.

Introduction Phonological recoding, orthographic knowledge, and rapid automatized naming (RAN) are three major contributors to word identification (Cutting & Denckla, 2001; Holland, McIntosh, & Huffman, 2004). The ability to sound out printed words, match orthographic features of printed words to those one knows, and rapidly access information in memory are important to accurate and speedy word identification. The interrelated nature of these components of word identification, however, is a topic of active investigation. Understanding the nature of the interrelations among these components would advance reading development theory and guide reading intervention efforts. The current analysis focuses on how phonological recoding and RAN relate to orthographic knowledge. ⇑ Corresponding author. Fax: +1 205 348 8648. E-mail address: [email protected] (S.J. Loveall). 0022-0965/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jecp.2013.05.009

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Orthographic knowledge refers to information stored in memory that represents spoken language in written form or, more simply, is the knowledge of how letters are combined to form words (Apel, 2011). It is one aspect of orthographic processing, which refers to the collection of abilities (learning/ acquisition, storage, and use) associated with the orthographic features of words. Orthographic knowledge is influenced by prior experience with print, whether recent and associated with a specific learning experience or accumulated over many years. Efficient use of orthographic knowledge can enable reading fluency, which in turn enhances reading comprehension. Recently, Apel (2011) proposed a model of orthographic knowledge and its components, suggesting two broad types of orthographic knowledge: one that is word specific and another that is more general (referred to by Apel as mental graphemic representations and orthographic rules, respectively; see also Burt, 2006). Word-specific orthographic knowledge relates to actual words that children have learned to identify and can be measured using choice tasks in which one choice is a real word (e.g., rain–rane). General orthographic knowledge relates to sensitivity to letter combinations that are legal and probable but not necessarily to specific and real words. It can be measured using choice tasks in which neither choice is a real word (e.g., filk–filv). Burt (2006) suggested that word-specific orthographic knowledge is influenced more by reading experience than general orthographic knowledge, which is consistent with findings that children show general orthographic knowledge before they can read specific words (Apel, 2011; Cassar & Treiman, 1997; Wolter & Apel, 2010). Some empirical evidence supports this distinction in elementary school-age children (Conners, Loveall, Moore, Hulme, & Maddox, 2011; but see Cunningham, Perry, & Stanovich, 2001). In the current analysis, we examined the relations of phonological recoding and RAN to both types of orthographic knowledge. Both phonological recoding and RAN have been suggested as mechanisms that promote orthographic knowledge. For example, the self-teaching hypothesis suggests that children acquire wordspecific orthographic structures naturally through the process of phonologically recoding new words (Jorm & Share, 1983; Share, 1995, 1999). Phonological recoding refers to the process of ‘‘sounding out’’ or translating a printed word into a speech-based form. In self-teaching studies, children are asked to phonologically recode target new words or nonwords, either in isolation or in paragraphs, and then are tested 3 to 7 days later on their orthographic learning of the targets. Results have shown that when children phonologically recode targets, they demonstrate orthographic learning of those targets days later (e.g., Bowey & Miller, 2007; Cunningham, Perry, Stanovich, & Share, 2002; de Jong & Share, 2007; Share, 1999). Furthermore, the accuracy of children’s phonological recoding of the new words or nonwords is related to the degree of orthographic learning (e.g., Bowey & Miller, 2007; Nation, Angell, & Castles, 2006), and children who are better at phonological recoding show more orthographic learning (Cunningham, 2006; Cunningham et al., 2002; Ouellette & Fraser, 2009). Although self-teaching studies focus on the acquisition of orthographic knowledge (orthographic learning) and not on accumulated orthographic knowledge itself, it is plausible that better phonological recoding skills lead to a greater accumulation of orthographic knowledge. Using an individual differences analysis, Conners and colleagues (2011) showed that accumulated orthographic knowledge mediated the relation between phonological recoding and word identification, consistent with the self-teaching hypothesis. Interestingly, word-specific and general orthographic knowledge mediated separate variance. Because the self-teaching hypothesis focuses on the relation between phonological recoding and word-specific orthographic structures, we would expect a strong corresponding relation between phonological recoding and word-specific orthographic knowledge in the current analysis. To the extent that phonological recoding also influences general orthographic knowledge, we would also expect to see a corresponding relation in the current study, perhaps weaker than for word-specific orthographic knowledge. In the current study, we examined the relation of phonological recoding to word-specific and general orthographic knowledge. Researchers have also suggested that RAN relates closely to orthographic knowledge. RAN is the speed and accuracy of naming visual symbols such as letters, digits, colors, and objects (Denckla & Rudel, 1974) and is one of the strongest correlates of early reading development (Bowers, 1995; Manis, Doi, & Bhadha, 2000; Manis, Seidenberg, & Doi, 1999; Meyer, Wood, Hart, & Felton, 1998; Parrila, Kirby, & McQuarrie, 2004; Wolf & Bowers, 1999). Wolf and Bowers (1999; see also Bowers & Wolf, 1993; Wolf, Bowers, & Biddle, 2000) hypothesized that when visual recognition of letters is slow, adjacent letters are not activated quickly enough to facilitate learning of common letter sequences. Thus,

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the acquisition of orthographic knowledge is compromised and orthographic recognition during reading is inefficient. Manis and colleagues (2000) reported that, for second graders, rapid naming of letters and digits explained significant variance in three orthographic knowledge tasks (both word specific and general) after controlling for vocabulary and phonological awareness. Furthermore, Bowers, Sunseth, and Golden (1999) and Conrad and Levy (2007) found that children with naming speed deficits performed more poorly on both word-specific and general orthographic tasks than children without naming speed deficits. Several other studies have also reported findings supporting the relation between RAN and orthographic knowledge (Holland et al., 2004; Manis et al., 1999; Mesman & Kibby, 2011). Thus, it is reasonable to expect that RAN will be related to orthographic knowledge in the current study. The contributions of phonological and rapid naming skills to more general reading skills have been examined across languages varying in orthographic transparency. Whereas English is an opaque orthography in which graphemes do not always map consistently to phonemes and vice versa, Dutch and German are relatively more transparent with more consistent mappings. Vaessen and colleagues (2010) found that phonological awareness contributed to reading fluency more for opaque orthographies than for transparent orthographies for readers in Grades 1 to 4. However, RAN contributed similarly regardless of transparency (see also Ziegler et al., 2011). Consistent with these results, Moll, Fussenegger, Willburger, and Landerl (2009) found that for typical fourth-grade readers of German orthography, phonological awareness did not contribute significant unique variance to reading fluency, yet RAN did. More specifically, relevant to the current study and consistent with Manis and colleagues (2000), both phonemic awareness and RAN contributed uniquely to spelling (their orthographic knowledge measure) in this sample. In the current study, we examined the relation of phonological recoding and RAN to both wordspecific and general orthographic knowledge in children in Grades 2 and 3. However, we also separately analyzed alphanumeric and non-alphanumeric RAN. Developmentally, and in terms of underlying skills, alphanumeric (letter or digit) RAN and non-alphanumeric (color or object) RAN present somewhat different patterns. Both alphanumeric and non-alphanumeric RAN relate to reading measures in kindergarten, but alphanumeric RAN relates more strongly to reading measures beginning in first or second grade (Meyer et al., 1998). In addition, in factor analysis, these two types of RAN separate out during early elementary school (Narhi et al., 2005; Savage, Pillay, & Melidona, 2007). One possible reason for this developmental pattern is that letter and digit naming become highly automatic, whereas color and object naming do not. This may be because for alphanumeric naming there is always a single name for each stimulus, but for non-alphanumeric naming there may be more than one name (see Narhi et al., 2005). Consistent with this explanation, Savage and colleagues (2007) found that processing speed loaded more strongly on an alphanumeric RAN factor than on a nonalphanumeric RAN factor. Wolf and Bowers’ (1999) interpretation of the relation between RAN and orthographic knowledge focused on the speed of visual activation of letters and probability of coactivations of adjacent letters. If this is correct, we would expect the more automatized alphanumeric RAN to relate more strongly to orthographic knowledge in our sample than the less automatized non-alphanumeric RAN. In addition, this would be particularly true for word-specific orthographic knowledge and not necessarily for general orthographic knowledge. Word-specific orthographic knowledge most often involves mapping printed words to single names and is likely to become automatic. In contrast, general orthographic knowledge involves many possible mappings for various orthographic features. In the current study, we examined the relation of RAN to orthographic knowledge in an English orthography controlling for IQ and phonological recoding.

Method Participants The current analysis included 38 English-speaking children in Grades 2 (n = 24) and 3 (n = 14) who participated in a larger study (see Channell, Loveall, & Conners, 2013, and Conners et al., 2011, for data

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from the same larger study). All children in the larger study who were in Grade 2 or 3 and who completed every measure used in the current analysis were included (mean age = 8.75 years, SD = 0.84, range = 7.58–10.83; mean IQ = 104.79, SD = 13.22, range = 78–126). The children came from five schools within a public school system in Alabama in the southern United States and were eligible for the study if they spoke English as a native language and were not receiving special education services. There were 17 boys and 21 girls, and the sample was ethnically diverse (21 Caucasian Americans, 16 African Americans, and 1 ‘‘other’’). Procedure Participants completed a battery of tests in two testing sessions within a 2-week period. Testing was administered individually in a quiet area at the children’s school or in our laboratory on campus. A set order of tasks was used for all participants. The tasks used in the current analysis are described below in order of appearance in the test battery. Measures IQ estimate The Kaufman Brief Intelligence Test–Second Edition (KBIT-2; Kaufman & Kaufman, 2004) is a standardized abbreviated measure of intelligence with both verbal (Verbal Knowledge and Riddles) and nonverbal (Matrices) components. From the three subtests, full-scale IQ scores were calculated and used in the main analyses. The KBIT-2 is normed for ages 4 to 90 years, and its estimated split-half reliability ranges from .90 to .93 for the ages represented in this study’s sample. It also correlates strongly with the Wechsler Intelligence Scale for Children–Fourth Edition (Kaufman & Kaufman, 2004). RAN The Comprehensive Test of Phonological Processing (CTOPP; Wagner, Torgesen, & Rashotte, 1999) is a standardized measure normed for ages 5 to 24 years that assesses a variety of phonological processing abilities. Subtests used in the current study were Rapid Digit, Rapid Letter, and Rapid Color Naming. In each task, participants looked at a page with letters, numbers, or colors and read aloud the stimuli as fast as possible from left to right from the top row to the bottom row. The examiner measured the time to name all items on the page by stopwatch. If a participant made fewer than four errors on the first page, he or she continued to the second page. If the participant made fewer than four errors across both pages, the test was considered valid and the participant’s score was the total naming time for both pages combined. Internal consistency reliability for the RAN subtests is reported to range between .70 and .84 for the ages in this sample. Digit and Letter Naming scores were combined into a composite alphanumeric RAN score using z-scores. Because alphanumeric and non-alphanumeric RAN begin to diverge after kindergarten (Meyer et al., 1998), Color Naming was left separate to represent non-alphanumeric RAN. Orthographic knowledge Three computerized tasks were devised to measure orthographic knowledge because they probe a wide range of orthographic material without a specific and deliberate learning episode preceding testing. Orthographic Choice was modeled after Olson, Forsberg, Wise, and Rack (1994). It measures participants’ ability to identify correct spelling patterns of words. With eight practice trials and 80 experimental trials, word pairs were presented individually on the computer screen. Each pair contained one word spelled correctly and a pseudo-homophone (e.g., room and rume). Participants selected the real word by pressing a computer key on the same side as their selection as fast as possible. Raw scores out of 80 were used in analyses. Spearman–Brown split-half reliability based on a similar sample from the larger study was .72 (Conners et al., 2011). Homophone Choice was modeled after Olson, Forsberg, and Wise (1994) to measure participants’ ability to discriminate specific orthographic sequences. It consists of five practice trials and 40 experimental trials. In each trial, participants were shown two correctly spelled homophones (e.g., I and eye)

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on a computer screen and prompted with a question (e.g., ‘‘Which one belongs on your face?’’). Participants selected the correct homophone as quickly as possible. Raw scores out of 40 were used in analyses. Spearman–Brown split-half reliability based on a similar sample from the larger study was .73 (Conners et al., 2011). Orthographic Awareness measures understanding of general patterns of letters in words. For each trial, two nonwords (e.g., filk and filv) were presented on the computer screen, and participants chose which one looks more like a real word. Nonword pairs were either linguistically legal versus illegal spellings, regular versus irregular spellings, or high versus low positional frequency of letters. The task was modeled after Siegel, Share, and Geva (1995), and the stimuli came from Massaro, Taylor, Venezky, Jastrzembski, and Lucas (1980), Siegel and colleagues (1995), and Treiman (1993). There were eight practice trials and 72 experimental trials. Raw scores out of 72 were used. Spearman–Brown split-half reliability based on a similar sample from the larger study was .85 (Conners et al., 2011). Phonological recoding The Word Attack subtest of the Woodcock Reading Mastery Tests–Revised (WRMT-R; Woodcock, 1998) measures phonological recoding. Participants read aloud nonwords (e.g., dee or plip), and the examiner scored their responses as correct or incorrect based on legal pronunciations from the English language. Nonwords increased in difficulty until participants reached a ceiling score. Raw scores were used in all analyses. Estimated split-half reliability for Word Attack is reported to be .91 for third graders. This subtest correlates with the Woodcock–Johnson III Word Attack (r = .74; Woodcock, 1998). Table 1 Intercorrelations among key tasks and participant performance on key tasks. 1

2

3

Age

–

–.10

–.03

.05

.03

.04

.04

–.08

–.08

–.23

–.08

IQ

–

–

.53**

.09

.16

.51**

.37*

.06

.14

–.23

.10

***

**

–.20

–.13

–.28

*

WA

–

**

.53

4

–

5

.31

6

.33

*

7

.61

8

.52

9

*

–.33

10

11

a

OA

–

.10

.31

–

.25

.23

.26

–.41

–.28

–.37

–.36*

OCb

–

.17

.33*

.25

–

.62***

.90***

–.45**

–.32

–.18

–.40*

HCc

–

.51**

.61***

.22

.62***

–

.90***

–.56***

–.47**

–.39*

–.54**

WS-OK

–

.38*

.52**

.26

.90***

.90***

–

–.56***

–.44**

–.31

–.52**

RAN-D

–

.05

–.33*

–.41*

–.45**

–.56***

–.56***

–

.84***

.47**

.96***

RAN-L

–

.13

–.20

–.27

–.32

–.47**

–.44**

.84***

–

.59***

.96***

RAN-C

–

–.26

–.14

–.37*

–.18

–.39*

–.32

.47**

.59***

–

.56***

Alpha

–

.09

–.28

–.36*

–.40*

–.53**

–.52**

.96***

.96***

.55***

–

*

Mean (SD) 8.75 (0.84) 104.79 (13.22) 25.66 (7.04) 56.53 (7.04) 59.92 (5.67) 31.79 (4.17) 0.00 (0.90) 41.56 s (8.86) 45.32 s (9.78) 73.60 s (14.33) 0.00 (0.96)

Note: Simple correlations are above the diagonal, and partial correlations corrected for age are below the diagonal. WA, Word Attack (phonological recoding); OA, Orthographic Awareness (general orthographic knowledge); OC, Orthographic Choice (word-specific orthographic knowledge); HC, Homophone Choice (word-specific orthographic knowledge); WS-OK, WordSpecific Orthographic Knowledge (composite); RAN-D, RAN–Digits; RAN-L, RAN–Letters; RAN-C, RAN–Colors; Alpha, alphanumeric RAN (composite). * p < .05. ** p < .01. *** p < .001. a Number correct out of 72 items. b Number correct out of 80 items. c Number correct out of 40 items.

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Results Preliminary analyses Mean scores and standard deviations for all measures are reported in Table 1. There were no floor or ceiling effects, but there was one statistical outlier for Orthographic Awareness. This score was adjusted to fit within 3 standard deviations from the mean, as suggested by Tabachnick and Fidell (2001). Correlations among the measures are also reported in Table 1. Only two of the three orthographic knowledge tasks (Orthographic Choice and Homophone Choice) were significantly correlated with each other. Theoretically, this makes sense because both Orthographic Choice and Homophone Choice involve real words, whereas Orthographic Awareness involves only general letter patterns. Thus, Orthographic Choice and Homophone Choice were combined into a composite variable, Word-Specific Orthographic Knowledge, using z-scores, and this composite score was used in all subsequent analyses. To differentiate, Orthographic Awareness was renamed general orthographic knowledge (see also Conners et al., 2011).

Primary analyses To determine how RAN contributed to both word-specific and general orthographic knowledge beyond the contribution of phonological recoding, a series of hierarchical linear regressions was conducted. Regression Models 1 and 2 predicted word-specific orthographic knowledge, and Models 3 and 4 predicted general orthographic knowledge. For each regression, IQ was entered in Step 1, phonological recoding was entered in Step 2, and alphanumeric and non-alphanumeric RAN were entered in Steps 3 and 4 (order entered dependent on model). Including both alphanumeric and nonalphanumeric RAN in separate steps allowed for a more fine-grained analysis of the relationships

Table 2 (Panel A) Predicting word-specific orthographic knowledge. (Panel B) Predicting general orthographic knowledge. R2

R2 change

F change

p

b

IQ WA Alpha NonAlpha

.14 .29 .49 .49

.14 .15 .20 .01

5.73 7.27 13.15 0.35

.02 .01 .001 .56

.34m .20 –.55* .10

IQ WA NonAlpha Alpha

.14 .29 .34 .49

.14 .15 .05 .15

5.73 7.27 2.73 9.81

.02 .01 .11 .004

.34m .20 .10 –.55*

IQ WA Alpha NonAlpha

.01 .10 .18 .23

.01 .09 .07 .06

0.29 3.67 3.04 2.41

.59 .06 .09 .13

–.14 .32 –.08 –.32

IQ WA NonAlpha Alpha

.01 .10 .23 .23

.01 .09 .13 .003

0.29 3.67 5.58 0.14

.59 .06 .02 .72

–.14 .32 –.32 –.08

Variable (A) Model 1 Step 1: Step 2: Step 3: Step 4: Model 2 Step 1: Step 2: Step 3: Step 4: (B) Model 3 Step 1: Step 2: Step 3: Step 4: Model 4 Step 1: Step 2: Step 3: Step 4:

Note: b, full model betas; WA, Word Attack (phonological recoding); Alpha, alphanumeric RAN; NonAlpha, non-alphanumeric RAN. * p < .05. m Marginal, p = .05.

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between different types of RAN and orthographic knowledge. All assumptions of multiple regression (see Pallant, 2005; Tabachnick & Fidell, 2001) were met. Each analysis used all 38 participants. Models 1 and 2: predicting word-specific orthographic knowledge After controlling for IQ, phonological recoding accounted for 15% of the variance in word-specific orthographic knowledge (p = .01). After controlling for both IQ and phonological recoding, alphanumeric RAN accounted for an additional 20% of the variance (p = .001, Model 1); however, non-alphanumeric RAN accounted for only 5% of the variance (ns, Model 2). Furthermore, alphanumeric RAN accounted for a significant 15% of the variance after non-alphanumeric RAN was entered first (p = .004, Model 2). See Table 2 for results. Models 3 and 4: predicting general orthographic knowledge A different pattern was found for general orthographic knowledge. After controlling for IQ, phonological recoding accounted for a smaller 9% of the variance (p = .06). After controlling for both IQ and phonological recoding, alphanumeric RAN accounted for only 7% of the variance (p = .09, Model 3), whereas non-alphanumeric RAN accounted for a larger 13% of the variance (p = .02, Model 4). Nonalphanumeric RAN still accounted for 6% of the variance after alphanumeric RAN was entered first (Model 3), but this was not statistically significant. See Table 2 for results. Discussion Orthographic processes have been much less researched than phonological processes. The current study offers insight into orthographic processes and suggests that, similar to phonological processes, orthographic processes may have several associated skills. Just as phonological processing includes skills such as awareness and recoding, orthographic processing may include skills such as word-specific and general orthographic knowledge. The current study’s results support this distinction, indicating that word-specific and general orthographic knowledge are related but separate orthographic skills. Furthermore, the current study suggests that these separate skills of orthographic processing relate differently to other reading skills such as phonological recoding and RAN. As predicted, phonological recoding was related to orthographic knowledge. Consistent with selfteaching studies on the acquisition of orthographic knowledge, phonological recoding had a stronger relationship to word-specific orthographic knowledge than to general orthographic knowledge. After controlling for IQ, phonological recoding contributed significantly to word-specific orthographic knowledge but only marginally to general orthographic knowledge. With a larger sample size, it is possible that the contribution to general orthographic knowledge would also be significant. However, the self-teaching hypothesis suggests that word-specific orthographic structures are acquired as a result of phonological recoding. Therefore, it makes sense that individuals with stronger phonological recoding skills might have a greater accumulation of word-specific, rather than general, orthographic knowledge (see Conners et al., 2011). Also as predicted, RAN was significantly related to orthographic knowledge even after controlling for IQ and phonological recoding. Consistent with Wolf and Bowers’ (1999) hypothesis, RAN was more strongly related to word-specific orthographic knowledge than to general orthographic knowledge. Together, alphanumeric and non-alphanumeric RAN accounted for 21% of the variance in word-specific orthographic knowledge but only 13% of the variance in general orthographic knowledge. However, alphanumeric RAN accounted for the majority of variance in word-specific orthographic knowledge, whereas non-alphanumeric RAN accounted for the majority of variance in general orthographic knowledge. Wolf and Bowers (1999) hypothesized that speed of visual activation allows for linking of letters and orthographic structures into words. Because alphanumeric RAN is more automatized than nonalphanumeric RAN for children in Grades 2 and 3, it makes sense that alphanumeric RAN would more strongly predict orthographic knowledge in the current study. It also makes sense that alphanumeric RAN relates more strongly to word-specific orthographic knowledge than to general orthographic knowledge. Because word-specific orthographic knowledge involves the mapping of printed words

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into exact names, it is likely to become automatic. In contrast, general orthographic knowledge involves many possible mappings for various orthographic features rather than real words. It would be much more difficult for this skill to become automatized. Because non-alphanumeric RAN becomes less automatized as children age, it is not surprising that non-alphanumeric RAN did not relate more strongly to word-specific orthographic knowledge. The significant relationship between general orthographic knowledge and non-alphanumeric RAN is very intriguing. Because non-alphanumeric RAN shared less variance with phonological recoding, it was a significant predictor after controlling for phonological recoding, whereas alphanumeric RAN was not. Thus, for general orthographic knowledge, non-alphanumeric RAN explained unique variance even in second and third graders. It is possible that this relation was evident because non-alphanumeric RAN and general orthographic knowledge are not as automatized as their counterparts (alphanumeric RAN and word-specific orthographic knowledge, respectively). The influences on general orthographic knowledge and the role of general orthographic knowledge in reading development could be an exciting avenue for future research. More research on the development of orthographic knowledge, including the onset and progression of both general and word-specific orthographic knowledge, is needed. It is not yet known whether one of these skills (general or word-specific) develops first and/or whether one predicts the development of the other. It is also possible that, similar to non-alphanumeric RAN, general orthographic knowledge becomes less applicable to reading as children age. As noted above, more research is needed. Two limitations of the current study should be noted. First, because of the small sample size, these results should be interpreted with caution. More research with larger samples and more narrow age groups is needed to replicate these findings. Second, the current study was conducted with Englishspeaking children, and as such these results might not fully generalize to other orthographies. Additional research is needed on both alphabetic and non-alphabetic orthographies to more fully understand the relationship among orthographic knowledge, phonological recoding, and RAN. Overall, the current study indicates that both phonological recoding and RAN contribute to orthographic knowledge and that these relations can be better understood by taking into account different types of RAN and different tests of orthographic knowledge. Alphanumeric RAN was more closely related to word-specific orthographic knowledge, whereas non-alphanumeric RAN was more closely related to general orthographic knowledge. Furthermore, phonological recoding was more closely related to word-specific orthographic knowledge than to general orthographic knowledge. These findings may shed light on how RAN and orthographic processing are related. Future research should consider differences in word-specific and general orthographic knowledge as well as differences in alphanumeric and non-alphanumeric RAN when measuring reading skills. Understanding the interrelated nature of these skills could possibly lead to more focused reading interventions. For example, enhanced training in alphanumeric RAN could aid in the development of word-specific orthographic knowledge, ultimately leading to enhanced reading abilities.

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