The Relationships Among Temporal Processing,Rapid Naming,and Oral Reading Fluency in Chinese Children With and Without Dyslexia

Abstract

The relationships among visual and auditory temporal processing, rapid naming, and oral reading fluency in Chinese children with and without dyslexia were examined. Primary school-aged Chinese children with dyslexia (N = 47) and chronological-age-matched controls (N = 47) were recruited. Temporal processing, rapid naming, oral reading fluency, Chinese character reading, and nonverbal IQ were assessed. There were significant correlations among visual and auditory temporal processing, rapid naming, and oral reading fluency. The patterns of the relationships among these measures differed between the children with and without dyslexia. The path analyses revealed that visual temporal processing had significant direct and indirect effects (through rapid naming) on oral reading fluency; only the children with dyslexia showed a significant direct effect of auditory temporal processing. These findings have research and educational implications for enhancing the reading abilities of Chinese children with dyslexia.

Keywords

reading thinking cognition

Dyslexia is known as a difficulty in learning that primarily influences the abilities that are responsible for the accuracy, fluency, and speed in spelling and reading words (Lyon et al., 2003). Regarding the reading problems of individuals with dyslexia, in addition to a weakness in phonological awareness (Snowling, 1998), dyslexia is also believed to be related to a deficiency in a domain-general cognitive ability, that is, processing speed (Stoodley & Stein, 2006). Thus, there is an important association among the rate at which information is processed, rapid naming, that is, the name-retrieval speed for a stimulus (e.g., letter, color, or digit; Wolf et al., 1986), and oral reading fluency that needs to be explored (Georgiou et al., 2008). For a detailed analysis of the functions (neuropsychological in nature) that underlie long-term word retrieval and speech production fluency, rapid naming has been an important clinical tool (Wolf & Bowers, 1999). Rapid naming has been depicted as a noteworthy forecaster of both longitudinal and current reading performance (Lei et al., 2010). Rapid and accurate reading with proper comprehension of the text is referred to as reading fluency (Langer et al., 2013). A major key factor behind reading problems and difficulties is thought to be poor temporal processing (e.g., Klein, 2002). Processing speed is defined as the ability to identify, discriminate, integrate, and make a decision about information and respond to visual and verbal information (Holdnack et al., 2016) and is considered the most crucial mechanism underlying temporal processing and other abilities that require timely responses (Benasich & Tallal, 1996). Temporal processing refers to the processing of rapidly presenting stimuli to the best level of detecting the target stimuli or identifying the changes in multiple stimuli based on their presenting sequence (Klein, 2002). Temporal processing in the visual modality is considered the rate at which our minds process auditory information; thus, unsurprisingly, fast simultaneous processing and the rate at which auditory information is processed by an individual contribute to rapid visual processing (Lallier et al., 2010). Recent evidence suggests that visual temporal processing may serve as an important factor underlying oral reading fluency (McLean et al., 2011). In addition, it is evident that the associations of temporal processing, reading-related abilities, and reading competency between children with and without dyslexia are different, considering the diverse nature of their cognitive profiles (Liu et al., 2016). To help readers improve their comprehension abilities, it is important to explore the mechanisms responsible for oral reading fluency. Hence, the current study examines the connection among temporal processing, rapid naming, and fluency in verbal reading in Chinese children who have or do not have dyslexia.

Literature Review

Processing speed has been declared the most vital mechanism underlying temporal processing (Benasich & Tallal, 1996) and is considered a vigorous psychometric factor in people’s daily life (Gooch et al., 2019). In other cognitive and linguistic tasks, the rate at which auditory information is processed and the rate at which the pronunciation of a stimulus is retrieved from memory (i.e., rapid naming) are greatly influenced by the underlying mechanism of processing speed (Kail et al., 1999; Kail & Hall, 1994). Previous studies have highlighted the contribution of auditory temporal processing to various components of phonological processing, such as rapid naming (e.g., Boets et al., 2008) and phonological awareness (e.g., Boets et al., 2007). The importance of phonological awareness and rapid naming in reading, especially in readers of alphabetic languages, has been well demonstrated in accordance with the processing of detecting/manipulating sounds and timing processes, respectively (for a review, see Wolf et al., 2000). In addition, the influence of auditory temporal processing has been demonstrated to extend to word reading, reading comprehension, and dictation (Catts et al., 2002; Caylak, 2011). In summary, it has been shown that the speed of processing plays a role at several levels such that reading performance might be influenced by temporal processing through the process of rapid naming (Kail et al., 1999; Kail & Hall, 1994).

The reading problems of people with dyslexia have been considered to be affected by a processing speed deficit that is apparent across one’s lifetime (Stoodley & Stein, 2006). This may be because people who have deficiencies in temporal processing show weakness and deficits in the speed of processing, a gradual decrease in the process of encoding information, or a reduction in the processing of temporal information when there is variability in time assigned to events and their features (for a review, see Farmer & Klein, 1995). Children with dyslexia have severe deficits (Tallal, 1980) in indicating (by pressing the corresponding key or button) paired temporally presented auditory stimuli in correct sequences, such as da/ba, and high tones (305 Hz) and low tones (100 Hz) when there is a short interval between the two stimuli, that is, interstimulus interval (ISI). Usually, the accuracy in various ISIs or the total accuracy across various ISI was recorded to highlight the temporal processing deficiency in different studies (for a review, see Farmer & Klein, 1995). On the basis of the above findings, there is a causal role of deficient temporal processing that interferes with phonological processing and may result in problems identifying or operating letter/sound correspondences in languages that are alphabetic (Strehlow et al., 2006).

It is widely accepted that deficits in temporal processing in children with dyslexia may occur in both visual and auditory modalities (Ramus & Ahissar, 2012; Tallal, 2014). In addition, deficient temporal processing has been found in multiple modalities, and magnocellular dysfunction is considered the cause of these conditions (Ramus, 2001). Many studies have supported this view of dyslexia (for a review, see Farmer & Klein, 1995). A modality specificity was found among the relationships of temporal processing and reading-related abilities; that is, orthographic knowledge was severely impacted by visual temporal processing, while phonological awareness was primarily affected by auditory temporal processing (Booth et al., 2000).

Considering the unique characteristics of reading Chinese, it is worth considering which modality Chinese readers may mainly rely on. Furthermore, it is worth considering the various association patterns that are revealed when examining Chinese children without and with dyslexia (e.g., Wang & Yang, 2018). In written Chinese, a character is considered to be the basic graphic unit as it represents a syllable of the spoken Chinese language (DeFrancis, 1989). A stroke is the basic element of a Chinese character and represents the subunits of Chinese characters, which makes them unique among written languages (Chung et al., 2008). Therefore, it is reasonable to consider that the processing of stroke-level (stroke: the smallest unit of Chinese character writing) and radical-level (radical: stroke patterns constituting the orthographic unit of Chinese characters) information relies on visual processing skills (Liu et al., 2015). For processing Chinese character configurations, spatial–visual perception is very important; for example, considering the differentiation of two Chinese characters with totally different sounds and meanings, 天 /tian1/ (sky) and 夭 /yao1/ (disaster), their number of strokes, order of strokes, and visual patterns are almost identical, and the difference shows in the angle of the top stroke. Thus, it is not surprising that visual temporal processing was found to be more dominant in Chinese character reading than auditory modality (e.g., Chung et al., 2008).

Moreover, these associations are made for Chinese words longer than two characters with visual perception and visual spatial connections in sentences as these relationships are helpful, and they will enable an individual to identify the word’s physical boundaries (e.g., Li et al., 2009). For oral reading fluency, these visual processing skills are vital as they help in the retention of visually presented information and allow a quick and complete visual–verbal transfer (Ding et al., 2009). Therefore, it is not new or surprising that compared to auditory temporal processing, visual temporal processing might be more important in Chinese (Li et al., 2009), and visual temporal processing can be considered important for appropriate language skills. For all language-related functions and abilities, such as comprehension and reading, it is reasonable to claim that the rapid processing of stimuli plays a vital role.

Regarding deficits in temporal processing and how it is related to rapid naming, most of the relevant investigations/studies have been conducted with alphabetic languages, although evidence has been accumulating with nonalphabetic languages such as Chinese (Wang et al., 2018). These studies have reported different connections between rapid naming or competency in reading and temporal processing. The investigations with the Chinese language have shown distinct patterns of relationships between temporal processing and rapid naming compared to those with alphabetic languages (e.g., Kail et al., 1999; Kail & Hall, 1994), and such inconsistencies are considered to be caused by the unique nature of the Chinese language.

A comprehensive review of this issue was performed by Zhang and McBride-Chang (2010), and they proposed a model with four stages of auditory function. These authors proposed that speech perception is critical for reading and that this is relevant regardless of the language. They share the view that at the most fundamental stages, there are two distinctive auditory sensitivities: the segmental perception of speech (e.g., phonemic), which may be affected by the rate at which auditory information is processed, and suprasegmental speech perception (e.g., prosody), which may be influenced by rhythm processing. Factors involved in phonological processing include rapid naming, and immediate verbal memory and phonological awareness directed to reading are strongly impacted by segmental speech perception (Rasinski, 2006). Thus, the role of auditory temporal processing in reading Chinese is supported by their model.

Several studies have suggested that there is a strong relationship between temporal processing and oral reading fluency. It is suspected that the processing of oral reading fluency could be subdivided based on sensory modality such that variation in the phonological aspects of reading is predicted by auditory temporal processes, and the variation in orthographic performance is explained by visual temporal processes (Booth et al., 2000). In addition, it has also been considered that oral reading fluency may be influenced by the readers’ prosody (Rasinski, 2006) and that prosody plays a significant role in determining its relation to the phonological aspects of oral reading fluency (Phillips et al., 2010; Walker et al., 2006). Prosody consists of temporal auditory patterns such that the prosodic pattern has an internal organization created by certain perceptions based on rhythm, intonation, and emphasis. Speech processing is coordinated through auditory information, and therefore, the presence of prosody in oral reading is fundamental (Pinto & Navas, 2011). In particular, prosodic aspects, such as intensity, velocity, emphasis, the phrasal rhythm pattern, and time, give prosody great semantic–pragmatic importance in performing the function of organizing verbal messages transforming auditory input into structural patterns that organize and maintain information in working memory (Ravid & Mashraki, 2007). In this point of view, the impact of temporal processing on prosody and then extending to oral reading fluency was particularly robust in the auditory modality (Walker et al., 2006). Thus, it is abundantly clear that basic temporal processing is correlated with oral reading fluency.

There has been a growing literature documenting the connection between oral reading fluency and rapid naming. Those studies showed that oral reading fluency could be independently predicted by literacy and related abilities, such as vocabulary, letter knowledge, orthographic processing, short-term memory, and phonological awareness (e.g., Pan et al., 2011; Powell et al., 2007; Savage & Frederickson, 2005). The mechanisms that are responsible for the rapid naming, that is, the oral reading fluency relationship, have not been completely identified by researchers despite the recognized utility of rapid naming for predicting reading. The precise relationship between rapid naming and oral reading fluency continues to elude the researchers in this field. Many researchers have attempted to isolate individual components of rapid naming, such as phonological or visual processes. Some researchers have taken a completely different view, in which rapid naming is conceptualized as mini-circuit or a microcosm of the reading circuitry that is gradually developing. A plethora of research has led researchers in this field to consider rapid naming tasks as one of the best predictors of oral reading fluency for all known orthographies (Georgiou et al., 2008; Norton & Wolf, 2012; Tan et al., 2005). Rapid naming seems to tap into fundamental or basic processes of oral reading fluency that apply not only with the English language but also across all languages. The importance of the relationship between rapid naming and oral reading fluency skills has been indicated by researchers (Waber et al., 2004). The acquisition of basic decoding skills leads to the development of oral reading fluency. The age at which the development of this fluency occurs is highly dependent on how complex or simple the sound-written symbol associations seem to be in a particular language (i.e., the level of orthographic complexity or simplicity). The relationship as well as the significance of rapid naming to oral reading fluency is important for children who do not have many clues for pronunciation in regard to written symbols; therefore, it takes time for a child learning any language to develop fundamental phonological awareness as well as decoding skills (Katzir et al., 2008).

Research Aims

This study was among the first to examine the relationships among visual and auditory temporal processing, rapid naming, and oral reading fluency in Chinese children with and without dyslexia. Specifically, two research aims were addressed. First, we aimed to examine how visual and auditory temporal processing and rapid naming contribute to Chinese oral reading fluency and whether the Chinese children with and without dyslexia differ in these interactions. Second, we aimed to examine and compare the contributions of visual and auditory temporal processing (if any) to Chinese oral reading fluency through the rapid naming abilities of Chinese children with and without dyslexia.

Method

Participants

A total of 94 Chinese children in late primary school (fifth to sixth grade) were recruited for this study. Half were identified as having dyslexia (N = 47 [32 males and 15 females]). In the present study, all eligible children with dyslexia had poor Chinese character reading but typical intelligence (>85). Specifically, the participants with dyslexia had a standard score below −1.5 standard deviations on the Chinese Character Recognition Test (Huang, 2001) and scored in the lowest 25th percentile of achievement in the subject of Chinese language based on their performance on regular examinations in school. In addition, those with dyslexia also had normal or corrected-to-normal vision and hearing and the absence of attention deficit with hyperactivity disorder. The other half of the participants (N = 47 [32 males and 15 females]) were typically developing children who had standard scores above −1.5 standard deviations on the same Chinese character reading test, regular performance above the bottom 25th percentile in the subject of Chinese, typical intelligence, normal or corrected-to-normal visual acuity, and normal hearing. The participants in these two groups were carefully matched mainly by age (in months), IQ, and gender, and there are no significant differences in age (in months; dyslexia: M = 127.53, SD = 10.22, range:113.6−153.5; typically developing: M = 125.56, SD = 11.13, range: 107.7–148.6; T = 0.86, p > .05) or IQ (dyslexia: M = 100.65, SD = 13.36, range: 78–128; typically developing: M = 105.30, SD = 11.90, range: 81–129; T = 1.70, p > .05) between the two groups, but their Chinese character reading significantly differs (dyslexia: M = 40.74, SD = 8.96, range: 23–58; typically developing: M = 70.42, SD = 8.96, range: 56–98; T = −15.36, p < .01). The descriptive information for the two groups is shown in Table 1.

Table 1.

Means, Standard Deviations, and Comparisons Between Children With and Without Dyslexia.

Measure	Children with dyslexia		Typically developing children		t (92)	Cohen’s d
Measure	M	SD	M	SD	t (92)	Cohen’s d
Rapid naming (seconds)	52.42	8.77	29.65	10.25	−11.06**	2.38
Oral reading fluency (0–429)	108.81	8.55	180.33	11.78	32.21**	6.95
Visual temporal processing (0–72)	43.21	7.45	56.23	8.06	7.78**	1.68
Auditory temporal processing (0–72)	45.67	6.35	52.86	8.17	4.55**	0.98

Note. N = 47 in both the dyslexia and typically developing groups.

p < .01.

Measures

All participating students were tested with the cognitive and reading-related tests listed in the following, including both standardized and self-developed tasks, either individually or in a group.

Raven’s standard progressive matrices–parallel

This is a standardized test of nonverbal IQ that consists of 60 items with increasing difficulty. Each item has a target visual matrix with one missing part. The children were provided with six to eight response options and were asked to select the response that completed the missing piece of the visual matrix. Nonverbal IQ was calculated based on the Taiwanese norm established by the Chinese Behavioral Science Co. in 2006 (Chen & Chen, 2006). In terms of the manual, the test–retest reliabilities (with a 4-week interval) are .53–.92, the split-half reliabilities are .50–.93, and the validities (based on performance in mathematics) are .38–.78. Based on the sample in the study, Cronbach’s α coefficient for this task was .81.

Oral reading fluency task

In this task, a passage that contained second-grade-level Chinese characters was presented to the participating children. Second-grade-level Chinese characters were chosen to reach the function of automaticity in oral reading fluency (for a review, see Fuchs et al., 2001); the texts/words used for this purpose are easier for the readers assuming that the readers could process these texts/words accurately and automatically. Thus, second-grade-level Chinese characters were chosen in this study to guarantee that this task could reach the goal of oral reading fluency among all participants who were late primary school ages, even those with dyslexia who are usually considered approximately two grade levels behind their peers without disabilities (Wagner, 1995). The reading material was a revised passage extracted from an unused version of a second-grade textbook and contained 360 characters. Performance in this task was measured using the method of words-correct-per-minute (WCPM; Hasbrouck & Tindal, 2006). That is, children read aloud as many words as they could in 1 minute, earning one point for each correct character. The WCPM score was obtained by subtracting the total number of errors from the total number of words read. Based on our sample, Cronbach’s α coefficient in this task was .82.

Chinese character recognition scale

This standardized test, that is, the Chinese Character Recognition Test, was developed by Huang (2001) to assess the Chinese character vocabulary size of the participant. The participants were asked to read the Chinese characters one-by-one as quickly as possible. The examiner did not provide cues or hints to the participants before or after their answers. There are a total of 200 Chinese characters, arranged in 20 lines with 10 Chinese characters per line. This task was developed for use with first graders through ninth graders. In terms of the manual, the test–retest reliabilities (with a 4-week interval) are .81–.95, the split-half reliability is .99, and the validities (based on performance in Chinese) are .36–.76. Based on the sample in the study, Cronbach’s α coefficient for this task was .89.

Rapid naming task

This task was designed to test the participants’ speed to verbally pronounce the visual stimuli one-by-one and was adapted from similar measures used in previous studies (e.g., Liu et al., 2015). Particularly, participants were required to read aloud single-digit numbers arranged on a single page of the paper in a 5 (row) × 5 (column) array as quickly as possible. Only five numbers (i.e., 1, 2, 5, 6, and 8) were included in this task and listed in a randomized order in each row. Each child performed the task twice, and their completion times were recorded. Average completion time was used as the score for this task. Based on the sample in the study, Cronbach’s α coefficient for this task was .92.

Visual and auditory temporal processing tasks

There were two temporal processing tasks, a visual modality task and an auditory modality task, used in this study. The paradigm, temporal order judgment, was developed by Tallal (1980) and subsequently used in many studies (e.g., Bretherton & Holmes, 2003; Chung et al., 2008). Pairs of visual stimuli, white circles (○) and crosses (×), and pairs of auditory stimuli, sine wave tones [i.e., a low tone (250 Hz) and a high tone (500 Hz)], were used. Thus, the stimuli were presented in four possible pair sequences: ×○, ○×, ××, and ○○ in the visual modality and high–high, low–low, high–low, and low–high in the auditory modality, and the order of presentation of the conditions was counterbalanced. Both the visual and auditory stimuli had a 75-ms duration, appeared consecutively, and were separated by an ISI of 8, 15, 30, 60, 150, or 305 ms. The participants were asked to respond to the order of two stimuli by clicking the corresponding keys on the keyboard (i.e., “F” and “J” in response to each of two stimuli per task). Before beginning this task, the participants were informed of which key was assigned to which stimulus. There were eight practice trials with visual feedback for both the correct response and incorrect responses, followed by 72 experimental trials (12 trials per ISI) without visual feedback. The correct number of trials across the ISIs was treated as the outcome of this task. Based on our sample, the overall Cronbach’s α coefficient was .80.

Data Analysis

To achieve the two research aims of the present study, several statistical methods were used. First, we aimed to examine how visual and auditory temporal processing and rapid naming contribute to Chinese oral reading fluency and whether the Chinese children with and without dyslexia differ in these interactions. Second, we aimed to examine and compare the contributions of visual and auditory temporal processing (if any) to Chinese oral reading fluency through the rapid naming abilities of Chinese children with and without dyslexia.

To fulfill the first research aim, correlation analyses and a moderation analysis were used to examine whether visual and auditory temporal processing and rapid naming contribute to Chinese oral reading fluency in a differential manner across groups of participants. For the second research aim, path analyses and multigroup analysis were used to investigate the mediation effect of rapid naming between the predictive relationship between auditory temporal processing and Chinese oral reading fluency. Because the limited sample size per group in this study could impede the statistical power of the path analyses and multigroup analysis (Bearden et al., 1982), we used a bootstrapping technique, that is, partial least squares approach, to test the relationships among the variables (Hair et al., 2012).

Results

To examine the research goal of this study, a preliminary examination of how the involved variables correlated with each other was necessary. Thus, bivariate and partial correlation analyses were conducted, and the results are shown in Table 2.

Table 2.

Bivariate Correlations of All Variables in All Participants.

Measures	1	2	3	4	5	6	7	8
1. Gender	—	−.18	.08	−.21^†	.12	−.19	−.20	−.38**
2. Non-verbal IQ		—	.02	.28**	−.24*	.23*	.32**	.25*
3. Age			—	.06	−.02	.01	.18	−.01
4. Character reading				—	−.80**	.93**	.78**	.48**
5. Rapid naming					—	−.87**	−.79**	−.45**
6. Oral reading fluency						—	.79**	.51**
7. Visual TP							—	.53**
8. Auditory TP								—

Note. TP = temporal processing.

†

p < .06. *p < .05. **p < .01.

Bivariate correlation analyses of all variables, including the controlled variables (i.e., gender, nonverbal IQ, age, and Chinese character reading) and the target variables (i.e., visual and auditory temporal processing, rapid naming, and oral reading fluency), were also conducted (as shown in Table 2). The results indicated that gender, nonverbal IQ, and Chinese character reading were significantly correlated with all target variables; thus, these three variables were included in the following partial correlation analyses.

The results of the partial correlation analyses of all target variables while controlling for gender, nonverbal IQ, and Chinese character reading indicated that significant correlations exist among both visual and auditory temporal processing, rapid naming, and oral reading fluency (as shown in Table 3). Furthermore, rapid naming was also significantly correlated with oral reading fluency. The results demonstrated close relationships of all the target variables in the present study. Only the significant correlation between auditory temporal processing and rapid naming disappeared when all the controlled variables were introduced.

Table 3.

Partial Correlations of Visual and Auditory Temporal Processing, Rapid Naming, and Oral Reading Fluency in All Participants After Controlling for Gender, Non-Verbal IQ, Age, and Character Reading.

Measures	1	2	3	4
1. Rapid naming	—	−.60**	−.46**	−.16
2. Oral reading fluency		—	.33**	.21^†
3. Visual TP			—	.27*
4. Auditory TP				—

Note. TP = temporal processing.

†

p < .06. *p < .05. **p < .01.

Moreover, multiple linear regressions were implemented to further examine the first research aim. In all the following linear regressions, oral reading fluency was treated as the dependent variable; furthermore, (a) the two temporal processing measures and rapid naming performance were treated as different independent variables in Model 1 by using the entry method, and (b) the interaction of groups of participants and each of two temporal processing measures and rapid naming performance were treated as the independent variables in the following models by using the stepwise method. This approach was adapted from the design of previous studies (e.g., Moll et al., 2015) and has been used by relevant previous studies (e.g., Wang et al., 2018). The results are shown in Table 4.

Table 4.

Multiple Regressions of Interaction Effects Among Group Differences, Temporal Processing and Rapid Naming on Oral Reading Fluency.

Measures	β	t	∆ R ²	R ²
1. Rapid naming	−.66	−8.28**	.80**	.80
Visual TP	.21	2.59**
Auditory TP	.09	1.62
2. Group × visual TP	−.59	−25.39**	.17**	.96
3. Group × rapid naming	−.17	−2.65*	.01*	.97

Note. TP = temporal processing.

p < .05. **p < .01.

The results indicated that visual temporal processing and rapid naming contributed significantly to oral reading fluency in Model 1. Furthermore, the contribution of the interaction effect between the participant groups and visual temporal processing on oral reading fluency remained significant in Model 2, while a significant interaction occurred between group and rapid naming, explaining the remaining variance in the participants’ oral reading fluency in Model 3. This result implied that the two groups of participants in this study, that is, children with and without dyslexia, had distinct cognitive profiles regarding the predictions of various types of temporal processing and rapid naming on oral reading fluency.

In addition, path analyses and multigroup analysis were used to pursue the second research aim. The models for examining the unique contributions of auditory and visual temporal processing in predicting oral reading fluency mediated by rapid naming between the two groups were good. The variance inflation factors (VIFs), which was used to check collinearity among the constructs, were all between 1.05 and 2.81 in the typically developing children and between 1.08 and 2.12 in the children with dyslexia. These results are considered to be very good, and there is small multicollinearity of the independent variable with the other variables in the regression models (O’brien, 2007). In addition, the R² results indicated that the included independent variables and controlled variables have great effect sizes on oral reading fluency in both groups (effect sizes were 0.92 in the typically developing children and 0.80 in the children with dyslexia). Finally, the normed fit index (NFI) of the model of the typically developing group was 0.89, and the standardized root mean square residual (SRMR) was 0.06. In contrast, the NFI of the model of the children with dyslexia was 0.99, and the SRMR was 0.03. Although it is considered a good fit to the data when the NFI values exceeded 0.95 and SRMR was less than 0.08, the model fit is acceptable as long as the NFI is no less than 0.80 (Hooper et al., 2008). All results revealed that the models for both groups are good to acceptable.

To reveal the unique contributions of auditory and visual temporal processing in predicting oral reading fluency as mediated by rapid naming in the two groups, we estimated several path analyses. These models estimated the paths from the auditory and visual temporal processing to oral reading fluency, and they specified the paths from sex, nonverbal IQ, and Chinese character reading to oral reading fluency to control for these variables. Because age was not correlated with any target variables, it was excluded from the model. The findings of this analysis revealed similar patterns of results between the two groups, which were also similar to the previous results; thus, visual temporal processing had a significant direct effect and indirect effect (through rapid naming) in predicting oral reading fluency (typically developing children: direct effect [B = 0.20, SD = 0.07, t = 2.68, p < .01] and indirect effect [B = 0.27, SD = 0.07, t = 3.71, p < .01]; children with dyslexia: direct effect [B = 0.41, SD = 0.10, t = 3.97, p < .01] and indirect effect [B = 0.15, SD = 0.07, t = 2.19, p < .05]). However, this influence of temporal processing on oral reading fluency was found to be limited in the auditory modality; that is, there was a direct effect of auditory temporal processing on oral reading fluency (B = 0.34, SD = 0.11, t = 3.05, p < .01). The results are shown in Figure 1.

Figure 1.

Mediation models of children with and without dyslexia addressing the mediation of rapid naming in the relationship between visual and auditory temporal processing and Chinese oral reading fluency.

In addition, the multigroup analysis was implemented to compare the difference of each parameter between the two models. In terms of the design of the multigroup analysis, if the p values were smaller than 0.05 or larger than 0.95 for a certain difference in the group-specific path coefficients, those results were significant at the 5% probability of error level (Henseler et al., 2009). The results of this analysis revealed that the differences in several of the paths reached significance. Specifically, a statistically significant group difference was found only in the direct path of auditory temporal processing to oral reading fluency (B difference = 0.33; p = .99), and there were no significant group differences in the other paths, including the direct path from visual temporal processing to oral reading fluency (B difference = 0.22; p = .94), the indirect path from auditory temporal processing to oral reading fluency (B difference = 0.07; p = .81), and the indirect path from visual temporal processing to oral reading fluency (B difference = 0.11; p = .14).

In sum, our results indicated the close relationships of visual and auditory temporal processing, rapid naming, and oral reading fluency. However, the cognitive profiles of such variables between children with and without dyslexia were distinct. Thus, the mediation effect of rapid naming in the relationship between the two temporal processing modalities and oral reading fluency was examined separately for the children with and without dyslexia. The results revealed similar patterns in the two groups; that is, visual temporal processing had a significant direct effect and an indirect effect (through rapid naming) on oral reading fluency. However, only children with dyslexia showed a significant direct effect of auditory temporal processing on oral reading fluency. The group difference showed that the direct effects of both visual and auditory temporal processing on the oral reading fluency of the children with dyslexia were significantly greater than those on the typically developing children.

Discussion

The main contribution of the current study was to extend the implication of temporal processing deficits of dyslexia in the Chinese language from the level of character accuracy to oral reading fluency. In addition, the role of rapid naming in the relationships between temporal processing and oral reading fluency was preliminarily confirmed.

Many models and hypotheses of oral reading fluency have been proposed, and one of the most well-known models is LaBerge and Samuels’ (1974) automaticity model of reading, which indicated that gradual increases in the automaticity of word recognition could lead to increases in reading fluency. However, in addition to the familiarity of these literacy stimuli, such as words or Chinese characters, individuals’ processing speed has also been demonstrated as the universal mechanism of reading (Kail & Hall, 1994). Thus, based on this assumption, the comparison of students with and without dyslexia, that is, students considered to have poor and better processing speed, respectively, is unavoidable.

The first research aim was to examine the long-term debate regarding whether the cognitive profiles of children with dyslexia differ from those of children without dyslexia. Our results echoed the previous evidence conducted with alphabetic languages indicating that the predictions of some reading-related abilities, including rapid naming, to oral reading fluency were significantly different in children with dyslexia relative to those without dyslexia (e.g., Landerl et al., 2009). Our findings showed that the symptom of dyslexia may cause a different pattern of predictions of rapid naming/visual temporal processing on oral reading fluency (due to the significant interactions shown in Table 4). The findings preliminarily indicated the distinct cognitive profiles of oral reading fluency between Chinese children with and without dyslexia. Regarding rapid naming, this result is similar to but not the same as that obtained in studies conducted in Chinese, indicating the presence of these reading-related abilities, such as rapid naming and lexical tone awareness, in typically developing children (e.g., Chung et al., 2010). In addition, the part of visual temporal processing echoed in these studies demonstrated that the distinct abilities of Chinese children with dyslexia may also occur at the cognitive level, such as visual perception and memory (e.g., Ho et al., 2004) and temporal processing (e.g., Wang et al., 2018). Therefore, it is reasonable to assume that diverse levels of the aforementioned abilities of Chinese children with and without dyslexia may lead to the differences in oral reading fluency.

Following, our results reported slightly different patterns between the two groups, and the most obvious difference we found, which was subsequently confirmed by multigroup analysis, was the existence of a direct contribution of auditory temporal processing to oral reading fluency. Although no direct evidence proved how temporal processing influences oral reading fluency in Chinese in children with and without dyslexia, Wang and Yang’s (2018) findings indicated that children with dyslexia tended to rely on temporal processing while character reading more than typically developing children. This finding implied that character reading in children with dyslexia may be influenced by their rapid sensitivity to both visual and auditory stimuli. In addition, such reliance on temporal processing for character reading was found to gradually disappear from the auditory modality and then the visual modality. Such phenomenon may be attributed to the children’s development of visual and auditory attention, and auditory attention reached the ceiling earlier than visual attention (Klenberg et al., 2001). Thus, the significant predictive value of auditory temporal processing in oral reading fluency occurred only in the children with dyslexia and not in the typically developing children, confirming and extending this hypothesis from character reading to oral reading fluency.

The significant direct predictive value of visual temporal processing to oral reading fluency was consistent with previous works, even though previous studies were conducted with alphabetic languages, which were thought to place less importance on visual temporal processing than auditory temporal processing (e.g., Hood & Conlon, 2004). However, it is worth noting that these results were inconsistent with Zhao et al.’s (2017) findings. In Zhao et al. (2017), visual temporal processing did not show a significant predictive value for either silent or oral reading fluency. Such a discrepancy between their work and our findings may be due to the diverse levels of reading fluency. That is, single-character and sentence-level reading fluency were examined in Zhao et al. (2017), while passage-level reading fluency was tested in the present study. The differential processing among various levels of oral reading fluency has been suggested in past work (Klauda & Guthrie, 2008).

In addition, there is abundant evidence indicating the significant predictive value of rapid naming for oral reading fluency across languages (for a review, see Norton & Wolf, 2012). Our results again confirmed this view and extended these findings by incorporating the mediation effect of rapid naming among the relationships between two types of temporal processing and oral reading fluency. We found a significant and consistent indirect path of visual temporal processing to oral reading fluency through rapid naming, but this was not observed with the auditory modality. This pattern is consistent with previous work in which the pattern of visual temporal processing predicted character reading through rapid naming in children with dyslexia but not typically developing children (Wang et al., 2018). This pattern may refer to the modality-specific influences of processing speed due to the heavy reliance on the attentional span to process visible/auditory stimuli (Lobier et al., 2013). In such a case, that is, Chinese script, which has been well recognized to contain complex visual–spatial elements and is highly visual processing demanding (Liu et al., 2015, 2016), it is reasonable to find a stronger prediction from visual temporal processing than the auditory modality.

Future Research

There are two limitations in this study. First, we considered only digit naming representative of rapid naming as it is the most effective predictor of reading capabilities (Savage & Frederickson, 2005). However, undeniably, rapid naming could incorporate diverse types of symbols to be named, including digits, letters, colors, and objects (Rudel et al., 1978). The naming of digits and letters is believed to require corresponding verbal codes associated with these stimuli that are readily accessible at the surface level, while additional conceptual processing (i.e., the semantic conceptual system) is believed to be necessary when colors and pictures are named (Donker et al., 2016). In addition, the nature of the diverse potential stimuli used in rapid naming may influence its mediating role among the types of temporal processing and Chinese reading (Wang et al., 2018). In this case, the sole use of digit naming as a representative of rapid naming may not reveal the whole picture of its role in the relationship between temporal processing and oral reading fluency. Thus, future research focusing on a similar topic could consider using a variety of rapid naming stimuli.

In addition, it is essential to investigate the extent of the distinct cognitive profiles of the children with dyslexia compared with the typically developing children—whether the differences represent a deficit or delay. Thus, some studies have involved two groups of typically developing children to address this issue and include a chronological-age control group and a reading-age control group (e.g., Chung et al., 2008). The involvement of the reading-age control group intends to test whether children with dyslexia perform significantly more poorly in specific tasks than those typically developing children with lower chronological ages (whose cognitive skills are assumed to be less developed); thus, these comparisons are highly likely to identify factors that play a causal role in reading development (Goswami & Bryant, 1989). In this sense, the lack of a reading-age control group in this study allows only an indication that children with dyslexia have deficits in temporal processing but the consequence of such differences cannot be known (Peng et al., 2017). Thus, it is suggested that future research examines these relevant issues by simultaneously involving both chronological-age control and reading-age control groups.

Implications for Practice

Despite the above limitations, this study is among the first to disclose the relationships among temporal processing, rapid naming, and oral reading fluency in Chinese children with and without dyslexia; Chinese children with dyslexia indeed have certain deficits in oral reading fluency, and the patterns of temporal processing, rapid naming, and oral reading fluency in Chinese children with and without dyslexia are distinct. Our findings extend to the use of temporal processing training to improve children’s word reading level in Chinese (e.g., Wang et al., 2019) and alphabetic languages (e.g., Fostick et al., 2014), that is, oral reading fluency. In particular, training children to improve their temporal processing in the visual modality seems to be more comprehensive for oral reading fluency in both children with and without dyslexia, while auditory temporal processing training may only be effective among children with dyslexia. In addition, similar to previous works (e.g., Poelmans et al., 2011), our results provide solid evidence for adapting teaching designs and strategies for Chinese children with dyslexia by reducing the presenting speed of teaching materials.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Li-Chih Wang

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