Abstract
This meta-analysis aimed to systematically investigate the cognitive and linguistic correlates of both word decoding and reading comprehension among children with Autism Spectrum Disorder (ASD) across orthographies. Based on data from 26 studies of 1.92- to 18.92-year-old children with ASD, we found that (1) intelligence, theory of mind, and executive function exhibited modest associations (rs = .41, .46, and .39, respectively) with reading; (2) phonological awareness, semantic skills, and syntax skills also showed modest associations (rs = .53, .50, and .53, respectively) with reading; (3) cognitive and linguistic skills showed comparable contributions to word decoding and reading comprehension, when both skills were analyzed in the same model with meta-analytic structural equation modeling; (4) age, language type, publication type, sample type, and reading measures did not moderate the relations. Taken together, these findings suggested that although children with ASD exhibit language weaknesses, their linguistic skills still made important contributions to reading development beyond cognitive skills. Further, cognitive skills may compensate for language deficits in children with ASD’s reading development. Implications for reading interventions in children with ASD were also discussed.
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Introduction
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition typified by social communication difficulties and restricted interests and behaviors (American Psychiatric Association [APA], 2013). Reading is an essential life skill for children with ASD, as it enables cognitive development, greater participation in social activities, expanded interests, and an understanding of how to adjust to new environments (Lanter & Watson, 2008; Tipton et al., 2017). However, the factors important for reading among children with ASD remain unclear. The current meta-analysis aimed to investigate the unique roles of cognitive and linguistic skills in both word decoding and reading comprehension among children with ASD.
Cognitive and linguistic correlates of reading in typical development
According to Simple View of Reading (SVR), decoding and linguistic comprehension are the two components necessary for skilled reading. Decoding “refers to the ability to read printed nonwords by applying the grapheme-phoneme correspondence rules, and linguistic comprehension refers to the ability to interpret sentences and discourse presented orally” (Georgiou et al., 2009). For typically developing (TD) children, both decoding skills and reading comprehension skills can be predicted by domain-general cognitive skills and domain-specific linguistic skills. Within the cognitive domain for TD children, executive function skills such as working memory, attentional shifting, and inhibitory control show moderate positive relations (rs = .30–.39) with both decoding and comprehension (Follmer, 2018; Jacob & Parkinson, 2015; Peng et al., 2018). Within the linguistic domain for TD children, metalinguistic skills such as phonological awareness and rapid naming are strongly associated with word decoding across orthographies (e.g., McBride-Chang & Kail, 2002), while morphological awareness, syntactic awareness, vocabulary, and grammar predict comprehension (Dong et al., 2020; Hung & Loh, 2021; Muter et al., 2004). Metalinguistic skills may also indirectly affect reading comprehension through decoding (Peng et al., 2021).
Similarly, cognitive skills may indirectly affect reading comprehension through linguistic skills for TD children. For example, Peng et al.’s (2018) meta-analysis on the unique contribution of working memory to reading comprehension suggested that working memory may influence reading comprehension indirectly through decoding and language skills. Quinn and Wagner (2018) used the meta-analytic structural equation modeling (MASEM) technique to study the relations among components of reading comprehension. They demonstrated that after controlling decoding and linguistic comprehension, cognitive skills did not contribute additional variance to reading comprehension, suggesting that cognitive skills may influence reading comprehension indirectly via decoding and linguistic comprehension. Therefore, although cognitive and linguistics skills are both important for reading development, linguistic skills seem to play a more important role than cognitive skills in reading comprehension for TD children. In the present study, we aimed to systematically investigate the extent to which cognitive skills and linguistic skills affect reading development of children with ASD, in order to provide evidence-based suggestions on their early reading intervention.
Cognitive and linguistic correlates of reading development among children with ASD
The theory of SVR and empirical studies discussed thus far have laid out the cognitive and linguistic correlates of reading and suggested the larger role of linguistic skills (relative to cognitive skills) in reading comprehension for the TD population (Florit & Cain, 2011; Peng et al., 2021). In children with ASD, however, the opposite may be true. Because evidence suggests that the relation between cognitive skills and academic performance is typically stronger for children with severe disorders, than for TD children (Peng et al., 2016), and because children with ASD usually present with language delay or impairment (APA, 2013), we have reason to believe that children with ASD may compensate for their poor language skills by relying more heavily on cognitive skills while reading. Furthermore, few studies have focused on the extent to which cognitive skills (e.g., IQ, executive functions, theory of mind) and linguistic skills (e.g., phonological awareness, semantic skill, syntactic skill) of children with ASD contribute to their reading development.
Cognitive correlates
Within the cognitive domain, IQ is a core cognitive skill with complicated implications for children with ASD’s reading development. Some studies have shown that IQ can help distinguish between varying levels of reading performance in children with ASD (Bullen et al., 2022; Mayes & Calhoun, 2008). However, Layton and Hao (2017) showed that Chinese students with high-functioning autism (HFA) and low-functioning autism (LFA) performed remarkably similarly on reading skills (e.g., reading simple words, and comprehending short paragraphs), as did age-matched peers who were intellectually disabled (ID), suggesting that IQ and reading skills in children with ASD may not be highly correlated. Another basic cognitive skill, visual processing, must also be taken into consideration amongst the population with ASD, as Ostrolenk et al.’s (2017) systematic review of cases and group studies demonstrated that children with ASD’s visual word form area of the visual cortex exhibited higher engagement during reading tasks, compared to TD controls.
Another important cognitive skill for reading and an area of consistent deficit in children with ASD is executive function (EF; Vanegas & Davidson, 2015). EF is the set of domain-general mechanisms that enables the dynamics of human cognition and action (Miyake & Friedman, 2012), and that usually includes (but is not limited to) working memory, attentional switching, updating, and inhibition (Miyake et al., 2000). There is mixed evidence on how different components of EF are related to reading among children with ASD. For example, for working memory, whereas some researchers have demonstrated that various types of working memory and comprehension are related in populations with ASD (Davidson et al., 2018), others have found working memory to have little relation to verbal comprehension for gifted students with ASD (Assouline et al., 2012) and little relation to comprehension of syntactic structures involving reflexives (Fortunato-Tavares et al., 2015). For attentional switching, updating, and inhibition, several studies have provided evidence that attentional switching is significantly correlated with word reading, and that mental flexibility, planning, and organizing are associated with reading comprehension in children with ASD (May et al., 2015; Kasirer, 2014). However, May et al., (2013) found no difference in sustained attention and attentional switching between TD children and children with ASD, and in regression analyses, none of these EF skills were significantly associated with reading attainment. Finally, theory of mind (ToM)—the ability to understand others' behaviors through an adoption of their mental state (Frith & Frith, 2005)—is a core deficit of autism (Baron-Cohen, 2000) and another cognitive skill relevant to reading. Tong et al. (2020) investigated the relation between ToM and reading comprehension in Chinese children with ASD and showed that ToM partially explains differences in performance in reading comprehension tasks, especially those require inference, mentalizing, and evaluation.
Thus, the empirical findings on the relations between cognitive skills and reading skills among children with ASD remain largely mixed. According to dual-process theory (Evans & Stanovich, 2013), task performance is accomplished by two different processing systems: autonomous processes and controlled processes. Studies show that there is an increasing number of children with ASD experiencing reading difficulties and greater heterogeneity in reading performance within this population (Solari et al., 2019; Westerveld et al., 2017), indicating that reading tasks are quite cognitively demanding for children with ASD. In addition, more than 60% of children with ASD demonstrate difficulties in reading comprehension during their school years (Nation et al., 2006; Ricketts, 2013), which might be explained by the hypothesis that children with ASD have a specific deficit in “theory of mind” or “executive functioning”. Thus, it is necessary to gain a better understanding for the degree to which basic and advanced cognitive skills are related to reading abilities as well as the factors that influence these relations. This meta-analysis systematically analyzes several moderators that potentially explain variations in the relations between cognitive skills and reading abilities of children with ASD. The moderators include types of cognitive skills (i.e., IQ, visual skills, EFs, and ToM) and types of reading (i.e., word decoding and reading comprehension). Understanding these relations and their influencing factors are important not only for the theoretical framework of how cognitive skills influence reading development of children with ASD, but also for educators, policy makers and parents in implementing more targeted intervention strategies.
Linguistic correlates
As for linguistic skills relevant to reading, the role of phonological awareness in reading abilities in children with ASD has been widely examined, but studies yield discrepant results. Dynia et al. (2017) found that phonological awareness emerged as the only statistically significant predictor of later decoding of non-words for children with ASD, but other research testing real-word decoding found phonological awareness to be independent of word recognition accuracy in school-aged children with ASD (Gabig, 2010). Oral language is another well researched linguistic skill with a nuanced relationship with reading in children with ASD. The literature on oral language distinguishes oral language as two separate skills: receptive language and expressive language. While receptive language has been shown to predict children with ASD’s reading in the first year of school (Westerveld et. al, 2018), expressive language may be more influential in children with ASD’s reading success, as expressive language emerges as stronger predictor of early, concurrent, and long-term reading in young children with ASD (Davidson & Ellis Weismer, 2014; St Clair et al., 2010).
Other linguistic skills that appear to correlate with reading in children with ASD include rapid automatic naming (RAN), syntax, and semantics. RAN is a reliable concurrent and longitudinal predictor of children’s reading skills (Kirby et al., 2010; Lervåg & Hulme, 2009; Westerveld et al., 2018), and speed and articulation in naming can account for reading fluency (Li et al., 2009). Syntax is associated with comprehension (Cronin, 2008, 2014; Whyte et al., 2014; Zhou et al., 2017), and knowledge about and manipulation of syntax enhances the abilities of children with ASD to comprehend predicate and reflexive sentence structure (Fortunato-Tavares et al., 2015). Semantics is related to both decoding and comprehension (Cronin, 2008, 2014), and is difficult for children with ASD, as they will generate semantically infelicitous answers when attempting to explain compound words (Kambanaros et al., 2019).
As discussed previously, decoding and language comprehension are two core predictive factors of reading comprehension according to the framework of SVR (Gough & Tunmer, 1986). Peng et al.’s (2021) meta-analysis of TD children revealed that meta-linguistic skills (e.g., phonological awareness, RAN, orthographic awareness) made direct and unique contributions to decoding but did not make direct contributions to reading comprehension beyond decoding and language comprehension. In contrast, meta-analyses on children with ASD suggested that linguistic skills like semantic knowledge and syntax can explain a substantial amount of variance within reading comprehension (Brown et al., 2013; Sorenson Duncan et al., 2021). Due to the discrepancy between how TD children and children with ASD use linguistic skills to read, as well as the inconsistent findings about specific linguistic skills we have previously discussed, the present study investigates several moderators that may explain variation in the relations between linguistic skills and reading abilities of children with ASD. These moderators include types of linguistic skills (i.e., phonological awareness, oral language, RAN, semantics, and syntax) and types of reading (i.e., word decoding and reading comprehension). A systematic analysis like ours can provide a more accurate estimate of the correlations between cognitive skills, linguistic skills, and reading abilities among children with ASD, which ultimately enables us to paint a more complete picture of the extent to which cognitive and linguistic skills predict reading development in children with ASD.
The present research
Due to the heterogeneity in linguistic and cognitive skills of individuals with ASD and the varied literature on the relevance of these skills for reading in ASD, this study aims to systematically examine children with ASD’s reading abilities by summarizing the cognitive and linguistic correlates of their reading development across orthographies. First, the current study determines the relations between cognitive and linguistic skills and reading abilities in children with ASD, as well as these relations’ moderators. Second, this study examines unique correlations using a path model and constrains both paths to reading in order to determine the larger of the two. We hypothesize that, for children with ASD, both cognitive and linguistic skills will explain unique variance in word decoding and reading comprehension. Moreover, we hypothesize that domain-general cognitive skills may play a more important role in children with ASD’s reading due to their delayed linguistic skills, compared to TD children. This meta-analysis of the correlates of reading in children with ASD will provide novel insight into the fundamental factors of general reading and language development, and in turn, inform early intervention.
Method
Literature search
We identified articles for this meta-analysis in two ways. First, we searched for literature in Education Resources Information Center (ERIC), MEDLINE, PsycArticles, and PsycInfo, from the earliest possible start date through December 2020. Second, we searched unpublished literature through Dissertation and Masters Abstract indexes in ProQuest. Titles, abstracts, and keywords were searched for using the following terms: (“Autism” or “ASD” or “autistic”) AND (“children”) AND (“letter sound knowledge” or “phonological awareness” or “RAN” or “oral language” or “receptive language” or “expressive language” or “grammatical” or “semantic” or “syntax” or “morphological” or “orthographic” or “executive function” or “working memory” or “inhibition” or “updating” or “switching” or “processing speed” or “visual skill” or “IQ” or “theory of mind”) AND (“character recognition” or “decoding” or “word reading” or “comprehension”). The initial search yielded 2304 studies. Three authors of this study then reviewed all studies by titles and abstracts. After excluding the 2185 irrelevant articles, the remaining 119 articles were closely reviewed using the following criteria.
First, studies needed to include a sample of children with ASD. Second, studies needed to include at least one task measuring reading and at least one task measuring cognitive or linguistic skills. Reading measures were defined as tasks that tapped into one of the following skills: character recognition, decoding, word reading, and reading comprehension. Cognitive measures were defined as tasks that tapped into one of the following skills: intelligence, theory of mind, visual skill, processing speed, working memory, updating, inhibition, and switching. Linguistic measures were defined as tasks that tapped into one of the following skills: letter sound knowledge, phonological awareness, RAN, oral language, receptive language, expressive language, grammatical skill, semantic skill, syntax, morphological skill, and orthographic skill. Third, studies needed to report at least one correlation (r) between any measure of reading and any measure of cognitive or linguistic skills.
Coding procedure
Studies were coded according to the characteristics, samples, and tasks used to measure different types of reading, cognitive and linguistic skills. The number of participants was coded for each correlation, which was used to weight each effect size (so that correlations obtained from larger samples would be given more weight in our analysis than those obtained from smaller samples). For the coding of correlation matrices, we first constructed a correlation matrix template with variables of interest, including reading (decoding and reading comprehension), cognitive skills, and linguistic skills. We then obtained correlations from the reviewed studies to fill in the correlation matrix template for each independent sample. For a correlation matrix from an independent sample, sometimes there are multiple correlations between a pair of variables due to multiple assessments. We averaged those correlations between two variables with multiple assessments.
Variables were discussed until a consensus was reached between the first and the last authors. The second and the last authors used this coding system to independently code half of the included studies (i.e., each coder coded different studies) and then checked the remaining half that the other author had previously coded. The first and the last authors coded the correlation matrices independently. The interrater reliability among the two coders was higher than 0.95 for all variables of interests in this study. Any disagreements were resolved by consulting the original article or through discussion.
Analytic strategies
The effect size index used for all outcome measures was Pearson’s r, the correlation between reading with cognitive and linguistic skills. We considered all eligible effect sizes in each study. That is, a study could contribute multiple effect sizes as long as the sample for each effect size was independent. For studies that reported multiple effect sizes from the same sample, we accounted for statistical dependencies using the random-effects robust standard error estimation technique developed by Hedges et al. (2010). This analysis allowed for clustered data (i.e., effect sizes nested within samples) by correcting the study standard errors to consider the correlations between effect sizes from the same sample.
Based on Borenstein et al.’s (2005) recommendations, we converted the correlation coefficients to Fisher’s Z, and all analyses were performed using these transformed values. The results, such as summarized effects and their confidence intervals, were then converted back to correlation coefficients. A random-effects model was used since we hypothesized that this body of research reports a distribution of correlation coefficients with significant between-studies variance, as opposed to a group of studies that attempts to estimate one true correlation (Lipsey & Wilson, 2001). Weighted, random-effects meta-regression models using Hedges et al.’s (2010) corrections were run in R to summarize correlation coefficients and to examine potential moderators.
Specifically, we first estimated the overall weighted mean correlations of reading with cognitive and linguistic skills, respectively. Then, subgroup analyses were used to examine the relations between reading and cognitive/linguistic skills for each subgroup of each moderator. Meta-regression analyses were used to examine whether types of reading and types of cognitive and linguistic skills moderated the relations between reading and cognitive/linguistic skills. For the moderation analysis, all moderators were entered into the model simultaneously, and publication type (peer-reviewed and non peer-reviewed), language (English and others), child age, and ASD type (ASD and high-functioning ASD) were entered as covariates. Dummy coded variables were generated for categorical moderators in order to compare these categories (Cohen et al., 2013).
To examine whether cognitive and linguistic skills contributed uniquely to reading, we used two-stage MASEM (Cheung, 2014; Cheung & Chan, 2005) to estimate the path from cognitive and linguistic skills to word decoding and the path from cognitive and linguistic skills to reading comprehension, using the “metaSEM” package in R (Cheung, 2015). The two-stage MASEM combines meta-analytic techniques and structural equation modeling within a unified framework. Specifically, stage one computes the pooled correlation matrix from primary studies, and stage two fits the pooled correlation matrix to estimate a hypothesized structural equation model (Cheung, 2014; Cheung & Chan, 2005). In stage one, we pooled and estimated the correlation among cognitive skills, linguistic skills, and reading (either word decoding or reading comprehension). Our major objective was to examine the unique contribution of each cognitive and linguistic skill on reading among ASD children, however, there were only 13 studies simultaneously reporting the correlations of cognitive and linguistic skills with reading. We finally could only conduct the SEM analysis using the overall cognitive skills as a whole and the overall linguistic skills as a whole due to the power issues. Then, in stage two, we investigated the unique contributions of cognitive and linguistic skills to reading (either word decoding or reading comprehension), in which reading was regressed on both cognitive and linguistic skills. The regression weights of cognitive skills with reading and of linguistic skills with reading were estimated. In our analyses, all tested models were only identified, since the number of observed variables was identical to the number of parameters, and model fit statistics could not be estimated.
Publication bias
Publication bias was examined using the method of Egger et al. (1997) and funnel plots. We did not find significant publication bias based on Egger et al.’s (1997) publication bias statistics for the correlation between reading and linguistic skills or between reading and cognitive skills. The standard errors of correlations did not significantly predict correlations among studies βs = .98–1.40, ps > .18. We did not notice significant outlier or asymmetry using funnel plots.
Results
Based on our inclusion criteria, as shown in Fig. 1, a total of 93 studies were excluded for the reasons: (1) not having an ASD sample (n = 4); (2) not having a measure of reading and a measure of cognitive/linguistic skills. (n = 36); (3) not reporting at least one correlation between reading and cognitive/linguistic skills (n = 53). And 26 studies involving 27 independent samples, 1,323 participants, and 331 correlations between cognitive/linguistic skills and reading among children with ASD were included for the final analyses. Due to the highly heterogeneous characteristics of children with ASD, in this meta-analysis, the age range of all the included studies was 1.92- to 18.92-year-old. Descriptive information for 26 studies included in the present meta-analysis was summarized in Table 1. And Table 2 shows the detailed correlations between reading and cognitive and linguistic skills, the size of the relation between cognitive skills and reading (including both decoding and comprehension) was r = .39, 95% CI [.31, .46], τ2 = .04, and r = .51, 95% CI [.45, .57], τ2 = .04, for linguistic skills and reading. After controlling for covariates and other moderators (including age, language type, publication type, sample type, and reading measures), the difference between cognitive-reading and linguistic-reading correlations was not significant, β = .12, SE = .06, t = 2.03, 95% CI [.00, .25], p = .06. Next, we examined the relation between cognitive/linguistic skills and reading for the subcategory of each moderator, and whether the type of reading, type of cognitive skill, or type of linguistic skill affected the correlations between cognitive/linguistic skills and reading.
Flow diagram of the inclusion of studies
Moderation effects of reading skills
With respect to the relation between cognitive skills and reading among children with ASD, there were 66 correlations involving word decoding, and 71 correlations involving language comprehension. The average correlation between cognitive skills and word decoding was significant, r = .39, 95% CI [.32, .44], τ2 = .02, and the average correlation between cognitive skills and reading comprehension was also significant, r = .40, 95% CI [.30, .50], τ2 = .05. As Table 3 shows, after controlling for covariates and other moderators (including age, language type, publication type, sample type, and cognitive skills), no significant difference was found between the correlations, β = –.08, SE = .08, t = –1.02, 95% CI [–.24, .09], p = .33. The average correlations of linguistic skills with word decoding, r = .51, 95% CI [.44, .56], τ2 = .04, and with reading comprehension, r = .51, 95% CI [.44, .57], τ2 = .03, were significant. As Table 4 shows, after controlling for covariates and other moderators (including age, language type, publication type, sample type, and linguistic skills), no significant difference was found between the correlation, β = − .04, SE = .06, t = − .71, 95% CI [− .17, .09], p = .49.
Moderation effects of cognitive skills
With respect to the relation between cognitive skills and reading among children with ASD, there were 49 correlations involving intelligence, 28 correlations involving theory of mind, 7 correlations involving basic cognitive skills (visual skills and processing speed were combined), and 49 correlations involving executive function. The average correlation between reading (which included word decoding and reading comprehension) the following cognitive skills were significant: intelligence, r = .41, 95% CI [.30, .51], τ2 = .03; theory of mind, r = .46, 95% CI [.26, .62], τ2 = .03; and executive function, r = .39, 95% CI [.29, .49], τ2 = .04. However, the correlation between reading and basic cognitive skills was not significant, r = .30, 95% CI [–.19, .67], τ2 = .09. As Table 3 shows, after controlling for covariates and other moderators (including age, language type, publication type, sample type, and reading measures), no significant differences were found in all comparisons.
Moderation effects of linguistic skills
With respect to the relation between linguistic skills and reading among children with ASD, there were 94 correlations involving phonological skills, 86 correlations involving semantic skills, 19 correlations involving syntax skills. The average correlation between reading (which included word decoding and reading comprehension) and each of the three linguistic skills was significant: phonological skills, r = .53, 95% CI [.46, .59], τ2 = .03, semantic skills, r = .50, 95% CI [.42, .58], τ2 = .06, and syntax skills, r = .53, 95% CI [.29, .71], τ2 = .06. As Table 4 shows, after controlling for covariates and other moderators (including age, language type, publication type, sample type, and reading measures), no significant differences were found in all comparisons.
Unique contribution of cognitive and linguistic skills to reading
We used two-stage MASEM to examine the unique contribution of cognitive and linguistic skills on reading in children with ASD. After correlation matrices were extracted from studies including correlations among cognitive skills, linguistic skills, and reading, we ended with 13 independent samples with 515 ASD participants. We found that cognitive skills contributed to reading when linguistic skills were controlled (β = .32, 95% CI [.22, .42], p < .001), and linguistic skills also contributed to reading when cognitive skills were partialled out (β = .24, 95% CI [.15, .35], p < .001).
Next, we examined the unique contribution of cognitive and linguistic skills on word decoding. After correlation matrices were extracted from studies including correlations among cognitive skills, linguistic skills, and word decoding, we ended with 10 independent samples with 345 ASD participants. We found that cognitive skills contributed to word decoding after controlling for linguistic skills (β = .29, 95% CI [.17, .41], p < .001), and linguistic skills also contributed to word decoding when cognitive skills were controlled for (β = .21, 95% CI [.09, .32], p < .001).
Finally, we examined the unique contribution of cognitive and linguistic skills on reading comprehension. After correlation matrices were extracted from studies including correlations among cognitive skills, linguistic skills, and reading comprehension, we ended with 12 independent samples with 501 ASD participants. We found that cognitive skills contributed to reading comprehension after controlling for linguistic skills (β = .34, 95% CI [.23, .45], p < .001), and linguistic skills also contributed to reading comprehension when cognitive skills were controlled for (β = .25, 95% CI [.11, .39], p < .001).
Discussion
This meta-analysis provided evidence of the cognitive and linguistic correlates of reading in children with autism. We found that both cognitive and linguistic skills contributed to reading across various orthographies in ASD populations. Both cognitive skills and linguistic skills showed comparable relations to reading, supporting the compensatory mechanism. Sample type, types of reading, types of cognitive skills, and types of linguistic skills did not moderate these relations. Below we discussed these findings in details.
The correlations of cognitive and linguistic skills with word decoding and reading comprehension
In finding that cognitive and linguistic skills contributed to reading abilities and that the degree of their contributions was similar, we asserted that basic and advanced cognitive skills and reading-related linguistics factors are equally imperative for learning to read. Further, both decoding and comprehension showed comparable correlations with cognitive and linguistic skills.
Within the cognitive domain, we found that intelligence, executive function, and ToM are significant predictors of reading in children with ASD. These findings echo previous literature, as May et al. (2013) also found that full-scale IQ explained a significant amount of variance in word reading attainment in individuals with ASD. Another study by Lin and Chiang (2014) showed that when IQ was controlled for, there was no longer a significant difference in language comprehension between children with ASD and those with Asperger’s Syndrome, suggesting that IQ may be responsible for variation in language comprehension abilities amongst those on the autism spectrum. The large role that executive function has in reading ability is consistent with the Executive Dysfunction Theory of autism, which asserts that the executive function deficits (e.g., deficits in inhibition, attentional switching, and working memory) are responsible for symptoms in individuals with ASD (Pennington & Ozonoff, 1996) and may extend to the autists’ impairments in language, since problems in reading comprehension may be caused by poor working memory or strategic planning (Locascio et al., 2010). Further, ToM plays an important function in reading, as impairments in ToM have been postulated as a major reason for social communicative deficits of autism (Baron-Cohen, 2000). These deficits set autism apart from other mental conditions with comorbid symptoms (e.g., ADHD which is also characterized by poor executive functioning, academic achievement, and reading skills) and notably, uniquely predict reading difficulties in children with high-functioning ASD, compared to ADHD and TD groups (McIntyre et al., 2017a, 2017b). Oberman and Ramachandran (2007) offered an explanation for this close relationship between ToM and language comprehension: language comprehension tasks requiring ToM (e.g., interpreting metaphorical language) make use of the mirror neuron system, which allows for the embodiment of the abstract language being used and which is compromised in individuals with ASD. ToM may also influence children with ASD’s reading in an indirect way (Sigman & Ungerer, 1984): the other cognitive deficits seen in children with ASD (e.g., deficits in executive functioning or visual processing) are “secondary” to social deficits. That is, development of these other cognitive skills requires highly functioning social cognitive skills like ToM, and thus, it is deficits in ToM that constrain the development of other cognitive skills important for reading development.
As for the linguistic domain, we identified phonological, semantic, and syntactic skills as significant predictors of reading ability. Like TD children, children with ASD use phonological awareness in reading and to a degree that is more than that of children with other disabilities (Dynia et al., 2017). Moreover, phonological awareness may have an indirect effect on children with ASD’s reading comprehension through its role in vocabulary and verbal memory (Engen & Høien, 2002). However, children with autism may also rely on other statistical learning strategies (e.g., perceiving visual similarities across whole words) in reading process (Cardoso-Martins & Da Silva, 2010). For semantic skills, children with ASD’s knowledge about concepts, vocabulary and word frameworks is so imperative that limited integration of these various pieces of knowledge leads to the emergence of differing comprehension profiles (Carnahan & Williamson, 2013; Williamson et al., 2012). Additionally, semantic knowledge is particularly important for expressive vocabulary development, which further predicts decoding and reading comprehension in children with ASD (Davidson & Ellis Weismer, 2014; Wise et al., 2007; Young & Killen, 2002). For syntactic skills, Jacobs and Richdale (2013) observed that syntax predicts reading comprehension for both TD children and children with high-functioning ASD. Furthermore, in an intervention study of anaphoric cueing, emphasizing pronouns’ functions and relations to other words within their respective sentences and paragraphs was effective in improving children with high-functioning ASD’s reading comprehension (Feng & Peng, 2019). Finally, at the interface of these various linguistic skills, children with ASD struggle with integrating syntax with semantics and pragmatics (Kambanaros et al., 2019). Consistent with these findings, our meta-analytic results indicate that linguistic skills including phonological awareness, semantic and syntactic skills, as well as the integration of these skills, are critical for reading among children with ASD.
The unique roles of cognitive and linguistic skills in reading
Meta-analytic structural equation modeling demonstrated that cognitive skills and linguistic skills uniquely predicted either decoding or reading comprehension skills in children with ASD. This finding stands out in contrast to similar meta-analyses conducted on typically-developing children: Peng et al. (2022) found that while cognitive skills could explain the variance in reading abilities in typically developing children, cognitive skills did not directly influence reading comprehension, after controlling for decoding and language comprehension. Peng et al. (2022) also suggested linguistic skills largely explained the cognitive differences between TD and population with reading difficulties (RD). Our present finding suggests the population with ASD is different from the population with RD in that the population with ASD exhibits both cognitive and language deficits, children with ASD are more likely to suffer from difficulty in reading and their cognitive and language deficits seemed equally important for reading. While for TD and populations with RD, linguistic skills play a more important role in their reading development.
Since our findings showed that in children with ASD, cognitive and linguistic skills play an equally crucial role in reading development, we tentatively draw two conclusions. First, because we controlled for cognitive skills and still found a significant effect of linguistic skills, we suggest that linguistic skills play a unique and direct role in both decoding and reading comprehension rather than simply regulating reading comprehension through decoding and language comprehension. Second, we have reason to believe that cognitive skills play an important role in reading, especially reading comprehension, for children with ASD, and this may compensate for their deficits in linguistic skills. This can be further understood through the theoretical perspective of neuroconstructivism: growth or constrains in one developmental domain may influence the developmental mechanism in other important domains. For example, Fisher et al. (2005) found that children with ASD rely more on language to understand ToM tasks than do typically developing children. Conversely, the additional burden of language delay in children with ASD may lead to relying more on cognitive skills in their reading development. Since ASD involves core deficits in the social cognitive and linguistic domains, cognitive skills may compensate for linguistic limitations and support children with ASD in developing their reading, both in decoding and reading comprehension.
Limitations and future directions
Several limitations are considered in the interpretation of our findings and form the motivation for future research directions. First, in order to understand the specific deficits in reading comprehension among this population, we originally aimed to explore how the relationship of cognitive and linguistic skills with each component of SVR (word decoding and language comprehension) varied across different moderators, and whether these effects on reading varied by subgroups of ASD. However, after selecting our reviewed studies, we did not find enough data on the effects of cognitive and linguistic correlates on decoding and comprehension separately, making it difficult to consider each of these skills in the model. Thus, our findings should be further validated with both cognitive and linguistic skills when more data is available in decoding and reading comprehension development in children with ASD. Second, we used two-stage MASEM in our analysis, which is unable to estimate the between-study heterogeneities given that the model is fitted on the pooled correlation matrix but not directly on the observed correlations as the one-stage MASEM. These issues would be further investigated with more studies in the future. Third, in this meta-analysis, we have examined the correlates of reading in the population with ASD as a whole, without partitioning out various population subgroups, such as children who perform poorly in all reading and language skills, children with hyperlexia, and children who have reading skills commensurate with typically developing children (McIntyre et al., 2017a, 2017b). Partitioning out subgroups in future studies will allow us to understand how correlates of reading may differ based on strengths and weaknesses within particular profiles of reading ability, which are sometimes associated with distinct processing mechanisms (Macdonald et al., 2021).
Conclusion
Despite these limitations, our study is the first meta-structural analysis that has systematically investigated the cognitive and linguistic correlates of reading abilities among children with ASD across orthographies. Our findings have several important implications for reading development and intervention in the population with ASD. Theoretically speaking, our findings indicate that both cognitive and linguistic skills equally predict children with ASD’s word decoding and reading comprehension and suggest a compensatory mechanism of cognitive and linguistic skills for learning to read. Practically speaking, the cognitive and linguistic skills we have identified as correlates of reading can serve as useful and accurate diagnostic markers for future reading success in young children with ASD (Anderson et al., 2007). Furthermore, heterogeneity in reading abilities within the population with ASD may reflect varying sensitivities to different degrees and types of support in language learning environments (Pickles et al., 2014), urging educators, parents, and policy makers to utilize targeted early intervention strategies that make use of these cognitive and linguistic skills to improve children with ASD’s decoding and comprehension skills.
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This study was supported by National Natural Science Foundation of China Grant (31900771; 32000761) and the Humanities and Social Science project of the Ministry of Education of China (No. 20YJC880095).
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Wang, Y., Lan, Z., Duan, I. et al. A meta-analysis on the cognitive and linguistic correlates of reading skills among children with ASD. Read Writ 36, 1487–1514 (2023). https://doi.org/10.1007/s11145-022-10338-7
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DOI: https://doi.org/10.1007/s11145-022-10338-7