Mediating Effects of Working Memory in the Relation ... - Springer Link

J Psycholinguist Res (2016) 45:945–959 DOI 10.1007/s10936-015-9385-z

Mediating Effects of Working Memory in the Relation Between Rapid Automatized Naming and Chinese Reading Comprehension Xiaoqian Weng1 · Guangze Li1 · Rongbao Li1

Published online: 18 July 2015 © Springer Science+Business Media New York 2015

Abstract This study examined the mediating role of working memory (WM) in the relation between rapid automatized naming (RAN) and Chinese reading comprehension. Three tasks assessing differentially visual and verbal components of WM were programmed by E-prime 2.0. Data collected from 55 Chinese college students were analyzed using correlations and hierarchical regression methods to determine the connection among RAN, reading comprehension, and WM components. Results showed that WM played a significant mediating role in the RAN-reading relation and that auditory WM made stronger contributions than visual WM. Taking into account of the multi-component nature of WM and the specificity of Chinese reading processing, this study discussed the mediating powers of the WM components, particularly auditory WM, further clarifying the possible components involved in the RAN-reading relation and thus providing some insight into the complicated Chinese reading process. Keywords Chinese reading · Rapid automatized naming · Working memory · Mediating effects · Skilled readers

Introduction Rapid Automatized Naming and Reading The rapid automatized naming (RAN) test requests individuals to name familiar visual symbols, such as letters, digits, colors, or pictures of simple objects, as quickly and accurately as they can (Denckla and Rudel 1974, 1976). Those symbols are usually randomly presented

B

Rongbao Li [email protected] Guangze Li [email protected]

1

College of Foreign Languages, Fujian Normal University, Fuzhou City 350007, Fujian Province, People’s Republic of China

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in a grid. The naming time has consistently proven an accurate indicator to predict reading abilities among children as well as adults, whether dyslexic or normal (e.g., Wolf et al. 2002; Swanson et al. 2003). In recent years, RAN is also found to be effective in reading non-alphabetic languages like Chinese (e.g., Cheung et al. 2006; Pan et al. 2011). Further, Chinese dyslexic children are reported to show several cognitive deficits, the most dominant of which is rapid naming (Ho et al. 2002, 2004). The relation exists between RAN time and various reading abilities including word reading, text reading fluency, and reading comprehension. As early as in 1984, Wolf pointed out that the rapid naming procedure was like reading in recognizing visual information, employing and retrieving of verbal clues, shifting attention and making fluent articulation. In other words, fast naming enables rapid identification of letters and other larger units, such as words, and resultant fast word identification facilitates reading comprehension (Johnston and Kirby 2006). Compared to word reading and reading fluency, however, reading comprehension, the ultimate goal of reading, seems to have no tight relation with RAN. Some studies have already assumed that the contribution of RAN to comprehension is mediated by word recognition or reading fluency. Johnston and Kirby (2006) argued that naming time contributed independently to word recognition, but when the effect of word recognition was controlled, naming time had little effect on reading comprehension. Wang and Liu (2008) confirmed that naming speed was indirectly related to reading comprehension, and that the contribution of processing speed was mediated by word recognition accuracy and fluency.

Working Memory as the Shared Component Since the relation between RAN and comprehension results partly from the contribution of word recognition and reading fluency, it is believed that those three reading components and RAN should share a certain or some cognitive capacities. While the roles of phonological awareness, orthographic ability, and processing speed might be still controversial, the role of working memory (WM) is sure to be more consistently agreed upon. Firstly, rapid naming requires the coordination of processes in attention, perception, conception, memory, lexicon and articulation (Bowers and Wolf 1993). The sequential nature of RAN implies that it involves some abilities required in the executive functioning which is an important component of WM (Denckla and Cutting 1999). Secondly, it has been universally established that WM is highly involved in successful reading comprehension (e.g., Friedman and Miyake 2004) in normal adult readers, because they need to hold information for a little while and simultaneously search the information in their long-term memories during comprehension (Numminen 2002). Thirdly, word recognition requires readers to access print words accurately and without too much difficulty. Thus readers need to recognize the visual forms of the words and memorize them for a short time while searching the words in their long-term memory. This process is similar to WM. Fourthly, WM is also involved in fluent reading which demands processing groups of words and constant shift of attention. Extensive studies have found the contribution of WM to reading fluency (e.g., Daneman and Carpenter 1980; Jacobson et al. 2011). On the basis of previous findings that the contribution of RAN to reading comprehension stems from the effect of word recognition and reading fluency, this study hypothesizes that WM may be the factor mediating the correlation between RAN and reading comprehension.

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Working Memory WM is considered as the capacity to maintain certain materials in a very short period and to process it at the same time. Baddeley and Hitch (1974) proposed that the multi-component model of working memory comprised four components—phonological loop, visuospatial sketchpad, central executive and episodic buffer. The phonological loop also known as verbal WM has two systems: a phonological store and an articulatory rehearsal system. Visuospatial sketchpad is used for immediately maintaining and processing visual nonverbal information, including visual features and spatial analysis. Together with central executive and a newly proposed episodic buffer, the aforementioned four components form a complete system and are effective in explaining reading and other cognitive processes (Baddeley 2003). Of all the relevant cognitive abilities, verbal WM has been demonstrated to have great effect on reading performance (Friedman and Miyake 2004). Despite few studies on sketchpad, recent researches have assumed that the abilities to hold and process visuospatial information are likely to be vital in reading comprehension (Baddeley 2003). There was hardly any study so far addressing the effects of verbal and visual working memory in processing Chinese language. As a logographic language, Chinese is also known as a morphosyllabic writing system. The basic Chinese unit is a character which is associated with one spoken syllable (sometimes a character has two or more pronunciations) and represents a morpheme (meaning unit) (DeFrancis 1989). However, the association between phonological and orthographic forms of a character can be quite arbitrary. An example is /qing/, clean water, /qian/, beautiful girl, /jing/, gifted woman. The pronunciation of can get clue from its phonetic radical /qing/, whereas the other two similar characters have different pronunciations. In many cases, characters do not have phonetic radicals. Chinese readers tend to learn by rote the spoken names of each character. When it comes to semantic radicals, a high degree of regularity can be found. As in the above example, means water, is associated with human, and represents female. Compared to alphabetic reading, Chinese reading relies more on visual skills, such as orthographic skills and morphologic awareness (e.g., Huang and Hanley 1995; Tong and McBride-Chang 2010). Therefore, it is highly necessary to consider and compare both visual WM and auditory WM when discussing the effect of WM in Chinese reading.

Mediating Role of Working Memory The assumption that WM plays a mediating role in the Ran-reading relation finds support from several studies. Amtmann et al. (2007) argued that RAN letters might test the phonological loop capacity and proposed the possible mediation of WM in RAN and reading. Leong et al. (2008) examined the roles of verbal WM, RAN and other cognitive skills in reading comprehension among Chinese children. As expressed in their hierarchical multiple regression results, letter naming played a role in children’s reading comprehension (ß = .315). However, when verbal WM entered first, its function dropped dramatically to .157. They concluded that the role of RAN in children’s text comprehension was largely explained by different capacities in children’s verbal WM. Arnell et al. (2009) tried to decompose the relation between RAN and reading abilities. Their standardized path coefficients showed that the latent shared variance between RAN and reading ability was significantly accounted for by rapid serial visual presentation ability (RSVP). Jolicoeur (1998) thought that RSVP tested the ability to rapidly recognize stimuli and to integrate them into WM. Therefore, it is presumed that WM was probably a latent source of the RAN-reading relation (Arnell et al. 2009).

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Nonetheless, it seems that the mediating role of WM is only partially assessed in previous studies. Amtmann et al. (2007) just put forward a simple conjecture; Leong et al. (2008) only explored a part of WM, the phonological loop, while Arnell et al. (2009) examined WM in the perspective of visual tasks. But the RSVP performance focuses more on the encoding phrase, representing the processing part of WM. To date, there is almost no comprehensive study on the mediating role of both verbal WM and visual WM in reading comprehension. The present study intends to explore the mediating role of WM in an all-round way, wishing to shed light on the relation between RAN and reading comprehension. On the one hand, this study will help to understand more about Chinese reading. As mentioned above, Chinese script decoding depends more on visual skills. As an important reading-related ability, RAN involves fast discrete naming, visual searching, attention maintaining and shifting and others (Wakamiya et al. 2011). And RAN deficit is believed to reflect visual processing difficulty (Huang et al. 2007). Backward visual skills, which are reflected by slow naming, may slower the orthographic manipulating for visual and spatial information of stokes and radicals of an individual character, impede the formation of larger orthographic units (Ho et al. 2004), and finally obstruct reading fluency, ending up in poor comprehension. The contribution of visual WM to Chinese reading is confirmed by a number of studies (e.g., Ding and Wang 2006; Lu and Zhang 2007). Given the features of the logographic language, the current study may simultaneously assess the mediating roles of visual and verbal WM in Chinese RAN-reading relation. On the other hand, this study may also solve a dispute about the role of visual skills in Chinese reading. Some researchers believe that skilled Chinese readers depend mainly on orthographic patterns rather than on phonological information (e.g., Song et al. 1995) whereas others argue that visual skills, especially the ability to remember visually presented digits, account for children’s future reading performance only when they are at early grades (e.g., Siok 2001). Thus the present study intends to choose normal adults as the participants to provide evidence about whether visual nonverbal skills are indispensable in different reading phases. The focus on skilled adult readers may exhibit another two benefits (Arnell et al. 2009). One is that adults would help to provide accurate data on the computerized Eprime system used in this study. Another benefit is that the study on adults may determine whether RAN continues to predict Chinese reading into adulthood as previous studies focus mostly on children. To sum up, this study examines the following two research questions: (1) Does WM play a mediating role in the relation between RAN and Chinese reading comprehension in skilled readers? (2) Do verbal WM and visual WM play a different role in the RAN-reading relation?

Methods Participants Normal adults are our participants. Apart from the high correlation between RAN and reading in skilled readers and the easier operability with more accurate data, the WM model was first put forward on the basis of research on adults (Baddeley and Lieberman 1980), and the model remains highly effective (Baddeley et al. 1998). We identified participants as skilled readers by consulting with their instructors about their reading performance in their college entrance examinations on Chinese. Fifty-eight freshmen and sophomores, aged 19–21, were recruited from Fujian University of Technology and Fujian Normal University. Participants completed all six tasks. However, three particiapants’ results were removed from the dataset because of their low accuracy in sentence judgement of AV and VV tasks.

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Tasks The present study contained one reading comprehension test, two verbal working memory tasks, one visual working memory task, and two naming tasks. What is worth noting is that working memory was assessed based on different inputs and different types of materials which include visually input verbal information (VV), visually input nonverbal information (VN) and auditorily input verbal information (AV), for previous studies (Cai and Dong 2012) indicated that encoding channels do make differences. The details of the tasks are shown below.

Reading Comprehension Test It included four passages which had 12 questions, and 15 short paragraphs, each paragraph with one question. In total, there were 27 questions in the test. One point was given for a correct answer and zero for a wrong answer or no reply. The passages and paragraphs were selected from Gaokao (college entrance examination) Chinese test and from the State Civil Service examination, respectively. This test was given previously to another 16 sophomores in Fujian University of Technology. The K–S test by SPSS showed that the scores of the test were normally distributed ( p = .942).

WM of Visually Input Verbal Information (VV Task) This task was modified from the study of Unsworth et al. (2005) by replacing the materials. Participants were asked to read sentences and try to memorize a series of irrelevant characters at the same time. First, participants read a sentence on the screen. If they fully understood it, they clicked the mouse to proceed. Then, they were asked to make decisions as to whether the sentence made sense. Afterwards, they saw an irrelevant character on the screen which lasted for 1 second. They should try to keep the character in mind. After several sets of sentences and characters, participants were finally asked to recall the characters they had just seen and memorized. At the recall phase, participants saw a 3 × 4 matrix of characters ( ) on the screen. They were asked to click the box left to the characters in the sequence in which the characters had shown up earlier. This phase was untimed, followed by a feedback. Each set had three trials. The set size ranged from three to seven. In order to prevent the participants from intentionally remembering the characters when reading the sentences, their reading time was limited individually. When one read 15 sentences in the practice session, his or her mean time to read the sentence was calculated automatically. The average time plus 2.5 SD was then taken as a time limit for the sentences reading during the formal experiment. In the test, if the participants spent more time in reading one sentence than the limited period, the program moved on automatically. The set order was randomly presented to each participant (Conway et al. 2005). The sentence reading was not scored, but to ensure that all participants focus both on the processing part and the storage part, an 80 % accuracy request of True or False statement was given to all participants. After the task was completed, the program took their performance in characters recall as their VV scores. In VV WM task, 81 simple sentences and 12 irrelevant characters were used as stimuli. There are 16–20 characters in each sentence. For example, one of the sentences is “when I get up in the morning, the first thing I do is brushing my teeth”. Of the 81 sentences, 44 are correct while the other 37 are grammatically unacceptable or meaningless. All those sentences were given previously to 10 undergraduates to test their acceptability. Some disputed sentences were replaced or modified with the help of a graduate Chinese major. And the 12 characters

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are very commonly used in the daily life, all with nearly the same character frequency, from 3.0356 to 3.9604 (Institute of Linguistic Studies 1986). Each character has 5 to 8 strokes.

WM of Auditorily Input Verbal Information (AV Task) This task was similar to VV task. Participants were asked to listen to a sentence and to make a decision as to whether it could be understood or not. They should press “F” as correct and “J” as incorrect. After that, they heard an irrelevant word which they should try to remember. Then another sentence came. After several sets of sentences and words, there would be a recall screen when participants were asked to speak aloud all the words they had just heard in the correct order through the microphone. Different from the VV task, there was no personal time limit for sentence processing. As soon as the sentence disappeared, participants had only 2 seconds to make a response. The time limit was set according to the pre-test, enabling participants to make a decision but refrain from rehearsing the words. Three trials of each set size (set sizes 2–6) were presented, with the order of set size varying randomly. An 80 % accuracy of sentence processing was also imposed. After the task, the experimenter scored their performance on the words recall on the basis of the audio files. In AV WM task, 60 sentences and 60 irrelevant words were selected as stimuli. There are 10–14 characters in each sentence. 33 of the 60 sentences are correct and the others are grammatically unacceptable or meaningless. All are simple sentences, like “Xiao Ming is going to Beijing to attend an international conference”. Pre-test of their acceptability and modification were also taken. Those words were with similar word frequency, from 220 to 279 (Institute of Linguistic Studies 1986). All the words and the sentences were recorded by a female graduate student with a nationally recognized certificate of Chinese language proficiency. All the stimuli of this task were presented through headsets.

WM of Visually Input Nonverbal Information (VN Task) This task was modified from Cai and Dong’s study (2012). Visual nonverbal WM includes a visual subsystem and a spatial subsystem (Baddeley and Hitch 1974). The current study took spatial information as the stimuli because Chinese character recognition may demand more on spatial analysis skills. Similar to Cai and Dong’s study (2012), participants saw a 3 × 3 matrix with numbers on the screen which is the same with the numeric pad. Then they would see a yellow dot swiftly move from one box to another, in a random way. After several moves, participants were asked to press the keys on the numeric pad in the same order of yellow dot’s moves. An improvement of Cai and Dong’s task was that participants wore a headphone with continuous numbers played and were asked to repeat the numbers they heard while trying to remember the dot’s locations. This was to suppress the influence of the phonological loop. Each set size contained three trials, with moves in set sizes ranging from four to eight in a random way. The experimenter scored their recall performance. For the WM tasks, partial-credit load scoring adopted in the present study allowed partial points if participants recall correctly some of the characters or digits in one trial (Conway et al. 2005), for example, 3 + 3 + 3 + 4 + 4 + 4 + 5 + 4 + 5 + 5 + 5 + 6 + 4 + 3 + 5 = 63/75 = 0.84.

RAN Digit In this task, participants saw a sheet of paper with five digits (1, 2, 3, 5, 8) in a 10 × 5 matrix. Each digit was randomly presented with equal frequency. Participants were required to name the numbers as quickly and accurately as possible. There were three different sheets. Each

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subject named two of them and the average time of the two trials was taken as the final score (Chung et al. 2011). Error rate was not taken into consideration because the rate was relatively low.

RAN Object This task was similar with RAN digit, only with different stimuli: five pictures of simple objects (fire, umbrella, tree, cat, ball).

Apparatus Three WM tasks were written in E-Prime Version 2.0 and E-run script files were all copied into participants’ personal computers (TsingHua Tongfang PC with AMD Sempron (tm) Processor 3200+ in Microsoft Windows XP Professional). Fifteen-inch monitors were employed to present the stimuli.

Procedures All the tasks were administered to 58 participants under the same condition. The experiment consists of two sections. First, all the participants were given the reading test and VV task in the same IT room, which took 40 and 20 min respectively. Between these two tasks was a 10min break. In section two, participants were led into several compartments with good sound insulation. In each compartment, participants were given the AV task and VN task, which took about 25 min to complete. When one participant finished the tasks, an experimenter would enter his or her compartment to give them RAN tasks. There was an interval of 2 days between section one and section two.

Results Descriptive Statistics Descriptive statistics of all the tasks were presented in Table 1. Reading comprehension test seemed to be too difficult for the majority of the participants. Those reading materials were designed mainly for selecting real talents, which means an accuracy rate of around 60 % was acceptable. Results showed a normal distribution in the reading performance, and the difficulty level of reading test did not affect the correlation and regression analyses in the current study. Participants performed better in VV task, even though VV and AV tasks were similar in difficulty. Object naming took much more time than digit naming. The data in RAN object task were also more widely dispersed as indicated by its higher standard deviation. In addition, all the four reading and WM tasks had a fairly high split-half reliability.

Pearson Correlations Table 2 showed Pearson correlations among the tasks. Because the time was used as the measurement of RAN, two RAN tasks scores presented negative correlations with other tasks. Reading, verbal WM tasks (VV, AV, Verbalcomp) and RAN digit all enjoyed significant correlations with each other. It is worth pointing out that VN task was correlated merely with Chinese reading performance. Also beyond our expectation is that only RAN digit was

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Table 1 Descriptive statistics and reliability of the study tasks Tasks

n

M

SD

Spearman–Brown coefficient

Guttman coefficient

Reading

55

17.11

2.27

0.754

0.744

VV

55

0.85

0.12

0.825

0.823

AV

55

0.79

0.12

0.976

0.963

VN

55

0.74

0.10

0.921

0.903

RAN digit

55

12.09

1.79

RAN object

55

18.07

2.22

WM working memory, VV WM of visually input verbal information task, AV WM of auditorily input verbal information task, VN WM of visually input nonverbal information task, RAN rapid automatized naming Table 2 Correlations between reading, RAN and WM tasks Tasks

1

2

1.Reading

–

.325*

.370**

–

.475** –

2.VV 3.AV

3

4.VerbalComp 5.VN 6.RAN digit 7.RAN object

4

5

6

7

.405**

.281*

−.355**

−.174

.863**

.106

−.366**

−.208

.855**

.061

−.398**

−.096

–

.098

−.444**

−.177

–

−.135

−.121

–

.370** –

WM working memory, VV WM of visually input verbal information task, AV WM of auditorily input verbal information task, VN WM of visually input nonverbal information task, RAN rapid automatized naming. VerbalComp (verbal composite) was based on the performance of two verbal working memory tasks combined * p < .05; ** p < .01

correlated with reading and verbal WM tasks. Additionally, Verbalcomp indicated moderately significant correlations with reading comprehension, but was insignificantly related to VN (nonverbal working memory task).

Hierarchical Regression Tests of Mediation This study examined verbal working memory and visual working memory as mediators of the relationship between rapid automatized naming and Chinese reading comprehension. To test the mediating effect of verbal working memory, we followed Baron and Kenny (1986) three-step procedure. First, the independent variable (RAN digit) was significantly associated with the dependent variable (reading) (R = −.355, p < .01), as indicated in Table 2. Second, the independent variable was significantly associated with the mediator (Verbalcomp) (R = −.444, p < .01), as provided also in Table 2. Third, in a model with both the independent variable and the mediator predicting the dependent variable, it should be shown that the mediator is significantly associated with the dependent variable. It should be also verified that in this multiple regression model, the path between the independent variable and the dependent variable is reduced to zero or by a significant amount. As demonstrated in Table 3, when controlling for the independent variable (RAN digit), the mediator (Verbalcomp) still contributed significantly to the dependent variable (reading) (ß = .308, p < .01), while the independent variable (RAN digit) was no

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Reading

VerbalComp

Reading

Step-1

Step-2

Step-3

.014

−.276 × 3.478

RAN digit × VerbalComp

.175 × 1.562

.163

−.050

RAN digit

−.450

SEb

b

RAN digit

Independent Variable (s)

−.218 × .308

−.444

−.355

ß

−1.574 × 2.226*

−3.612**

−2.761**

t

.122 × .030

.001

.008

Sig.

WM working memory, VV WM of visually input verbal information task, AV WM of auditorily input verbal information task, VN WM of visually input nonverbal information task, RAN rapid automatized naming. VerbalComp (verbal composite) was based on the performance of two verbal working memory tasks combined * p < .05; ** p < .01

Dependent Variable

Steps

Table 3 Hierarchical regression tests of mediation

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longer a significant correlate of the dependent variable, sizably reducing the path coefficient (ß for Step-3 which equals to .218 is less than ß for Step-1 which equals to .355). Hence verbal working memory is a mediator of the relationship between rapid automatized naming and reading comprehension. To test the mediating effect of visual working memory, we adopted the similar procedure, with the results presented in Table 4. Although the independent variable (RAN digit) was significantly related to the dependent variable (reading) (ß = −.355, p < .01), the mediator (VN) was neither significantly related to the independent variable (ß = −135, p = .325) nor a significant predictor of the dependent variable beyond the independent variable (ß = .238, p = .066). Obviously, visual working memory does not mediate the relationship between rapid automatized naming and reading comprehension.

Discussion The findings of this study have shown that differences in adults’ verbal WM capacities could account for a large part in the effect of RAN on Chinese reading comprehension, suggesting working memory mediates the RAN-reading relation in adult Chinese readers. As an extension of previous studies, this study has also sought to compare the relative effects of visual WM and auditory WM and found that merely verbal WM underlies the relation between RAN and reading comprehension. Further, AV WM has a more powerful correlation with digit naming and Chinese reading comprehension than VV WM. To explain such results, it is necessary to reconsider what different abilities are involved in VV WM and AV WM.

Mediating Role of WM VV WM is thought as a visually input phonological loop involving an orthography-tophonology conversion (OPC). Manis et al. (1999) emphasized that the totally arbitrary relation between a visual symbol and its spoken name is a very important aspect of RAN and that it is also a significant characteristics of Chinese characters. Unlike alphabetic languages, Chinese characters can not be spelled and pronounced. The Chinese OPC, an all-or-none access, is essential to successful reading. As pointed out by Denckla (1998) (as cited in Wolf and Bowers 1999), rapid naming is like a scaled-down version of reading, representing how fast OPC is made. Moreover, the rapid naming task can examine this arbitrary orthographyto-phonology relation of Chinese characters (e.g. Manis et al. 1999). Therefore, VV WM, which tests the OPC ability, strongly connects RAN and reading. AV task, however, mainly involves phonological store and articulatory rehearsal. Meng et al. (2004) argued that RAN at least involves phonological processing and pronunciation speed. As to skilled readers, along with their improved reading ability, their phonological WM can function automatically. Then, they can focus more on the understanding of what they are reading (Numminen 2002). It thus suggests that when larger units are dealt with, phonological processing would be highly required in Chinese reading so as to ensure good memory and successful reading comprehension (Zhang and Perfetti 1993). Therefore, AV WM, which taps the phonological store and articulatory rehearsal, makes a difference in the RAN-reading relation. For the stronger correlation between AV WM and RAN, it should be noted that although RAN requires rapid retrieval of discrete stimuli and sequential skills, it is a voice producing task as well. It is possible that explicit motor production takes a much larger proportion of time than implicit print-to-voice conversion. Arnell et al. (2009) indicated vocal production

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Reading

Step-3

−.409 × 5.296

RAN digit × VN

.161 × 2.820

.163 .008

−.450

RAN digit −.008

SEb

b

RAN digit

Independent variable(s)

−.322 × .238

−.135

−.355

ß

-2.546* × 1.878

−.993

-2.761**

t

.014 × .066

.325

.008

Sig.

WM working memory, VV WM of visually input verbal information task, AV WM of auditorily input verbal information task, VN WM of visually input nonverbal information task, RAN rapid automatized naming * p < .05; ** p < .01

Reading

VN

Step-1

Step-2

Dependent variable

Steps

Table 4 Hierarchical regression tests of mediation

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accounted for a large part in RAN processes. AV task, which is tested orally, taps the ability to rapidly speak out what is maintained in the short-term storage. Thus it bears a tighter correlation with RAN. On the other hand, in the process of acquiring Chinese OPC rules, children are required to develop phonological skills (Ho and Bryant 1997). In a language like Chinese, which demands a lot on the efficient conversion between arbitrary orthography and phonology, phonological skills are crucial. When it comes to sentences and paragraphs, similar to English readers, Chinese readers need to employ their phonological processing abilities to support memory and comprehension (Zhang and Perfetti 1993). It is likely that although Chinese readers focus more on the visual information to access the meaning of each character, they have to rely on the phonological loop to retain their visual-to-phonological information, and employ their phonological skills to grasp the gist of the sentence or paragraph. Therefore, despite the fact that orthographic skills may account for more of Chinese word reading than phonological skills, AV WM is far more effective than VV WM in Chinese reading comprehension of sentences or paragraphs.

Different RAN Categories and Reading Results have shown that RAN digit is much stronger than RAN object in predicting reading. On the RAN object test, there were obvious jams during participants’ pronunciations with long and struggling retrievals. RAN is generally defined as the rapid and sequential naming of extremely familiar visual symbols, but those pictures of simple objects might be very unfamiliar to the participants. Although some researchers have found that performance on rapid naming pictures of objects could predict reading ability among older children (Meyer et al. 1998), the long retrieval time may attribute to irrelevant factors. For one thing, the nonautomatic process loses its discriminating power (Misra et al. 2004). For another thing, the complexity of OPC might render participants unable to name the list fluently, adversely affecting the performance of RAN object.

Visual-Spatial WM and Chinese Reading In Table 2, results have also shown that VN WM is significantly correlated with Chinese reading in skilled readers (R = .281, p < .05), implying that skilled readers may depend on visual searching skill and spatial WM to process Chinese reading materials. Some researchers have found (e.g., Tan et al. 2001) that when reading Chinese texts, readers highly activate their left lateral middle frontal lobe. They explained that for a logographic language, Chinese readers need to specially process spatial information of stokes so as to recognize the characters. Those strokes, which aggregate as a square, are elements of Chinese characters. Chinese reading requires quick recognition of visual symbols. And this fast recognition is based on good visual-spatial cognitive competence (Huang et al. 2007) as reflected in the VN WM task. The current study indicate the contribution of visual-spatial skill to Chinese reading in adults, which is quite different from some previous observation that visual skills are insignificantly correlated with reading performance after childhood (Siok 2001). As indicated earlier in Table 2, there is no significant relation between VN and RAN tasks. The reasons can be consistent with what is previously discussed. That is, compared with visual skills and OPC, phonological store and articulatory rehearsal account for a much larger part in RAN. Therefore, VN WM task, which mainly taps the visual skills and OPC ability, is significantly related to reading comprehension but not to either RAN task. To have

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a thorough understanding of VN-RAN relation, future studies should include both discrete and continuous formats of naming and other naming stimuli like color. In a word, the present study carries out hierarchical regression tests of mediation to investigate the mediating power of WM in the relation between RAN and Chinese reading comprehension. The findings from this study not only confirm the role of WM in mediating RAN and Chinese reading, but also lend compelling support to LaBerge and Samuels’ (1974) Theory of Automatic Information Processing in Reading and Perfetti’s (Perfetti 1985) Verbal Efficiency Theory, both of which posit that efficient word decoding facilitates comprehension of the reading text by freeing up limited attentional capacity that will be available for resource-demanding comprehension. Surprisingly, however, this study indicated no mediating effect of visual working memory which was thought essential to Chinese reading. One of the potential reasons is that visual working memory might contribute more strongly to other reading components like word reading, and that it would possibly have a role in mediating the relation between RAN and those reading components. In order to provide a clear picture of the relationship between RAN, WM and reading components, future studies should be conducted. First, because of the multi-dimensional nature of RAN, WM, and reading, future studies should better take into consideration reading fluency and word reading as well as other linguistic processes on the one hand and adopt the structural equation modeling approach to further examining the mediating effects on the other hand. Second, future studies should explore whether the mediating effects of WM may persist from childhood to adulthood. Such studies would deepen the understanding of the role of WM in the RAN-reading relation from a developmental perspective. Acknowledgements The research was supported by China Foundation of Social Sciences Research Project (No. 09BYY02) granted to the corresponding author.

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