Trajectories of pragmatic and nonliteral language development in children with autism spectrum disorders.

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JCD-5694; No. of Pages 13 Journal of Communication Disorders xxx (2015) xxx–xxx

Contents lists available at ScienceDirect

Journal of Communication Disorders

Trajectories of pragmatic and nonliteral language development in children with autism spectrum disorders Elisabeth M. Whyte *, Keith E. Nelson The Pennsylvania State University, United States

A R T I C L E I N F O

A B S T R A C T

Article history: Received 9 January 2014 Received in revised form 30 October 2014 Accepted 7 January 2015 Available online xxx

Children with autism spectrum disorder (ASD) often have difficulties with understanding pragmatic language and also nonliteral language. However, little is understood about the development of these two language domains. The current study examines pragmatic and nonliteral language development in 69 typically developing (TD) children and 27 children with ASD, ages 5–12 years. For both groups, performance on pragmatic language and nonliteral language scores on the Comprehensive Assessment of Spoken Language increased significantly with chronological age, vocabulary, syntax, and theory of mind abilities both for children with ASD and TD children. Based on a cross-sectional trajectory analysis, the children with ASD showed slower rates of development with chronological age relative to TD children for both the pragmatic language and nonliteral language subtests. However, the groups did not show significant differences in the rate of development for either pragmatic language or nonliteral language abilities with regard to their vocabulary abilities or TOM abilities. It appears that children with ASD may reach levels of pragmatic language that are in line with their current levels of basic language abilities. Both basic language abilities and theory of mind abilities may aid in the development of pragmatic language and nonliteral language abilities. Learning outcomes: After reading this article, the reader will understand: (1) the relation between basic language abilities (vocabulary and syntax) and advanced language abilities (pragmatic and nonliteral language), (2) how the cross-sectional trajectory analysis differs from traditional group matching studies, and (3) how pragmatic and nonliteral language development for children with autism shows both similarities and differences compared to typically developing children. ß 2015 Elsevier Inc. All rights reserved.

Keywords: Language development Autism spectrum disorder Pragmatic language Theory of mind

1. Introduction Individuals with autism spectrum disorders (ASDs) have difficulties with the development of social and communicative abilities. However, for domains of language development, some theories (such as the relevance theory; e.g., Happe´, 1993) suggest that some areas of language (especially the domains of pragmatic language or nonliteral language) may be disproportionately impacted for those individuals with autism who do show considerable development in their vocabulary and/or syntax abilities. For typically developing (TD) children, skills in both nonliteral language and pragmatic language

* Corresponding author at: Department of Psychology, 141 Moore Building, The Pennsylvania State University, University Park, PA 16802, United States. Tel.: +1 814 863 5626. E-mail address: [email protected] (E.M. Whyte). http://dx.doi.org/10.1016/j.jcomdis.2015.01.001 0021-9924/ß 2015 Elsevier Inc. All rights reserved.

Please cite this article in press as: Whyte, E. M., & Nelson, K. E. Trajectories of pragmatic and nonliteral language development in children with autism spectrum disorders. Journal of Communication Disorders (2015), http://dx.doi.org/ 10.1016/j.jcomdis.2015.01.001

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continue to improve throughout childhood (Rundblad & Annaz, 2010a; Ryder & Leinonen, 2003). Some research suggests that as a group, children with autism spectrum disorder (ASD) often have a difficult time understanding both pragmatic language (Lam & Yeung, 2012; Tager-Flusberg & Anderson, 1991) and nonliteral language (Rundblad & Annaz, 2010b). However, individuals with ASD show great variability in the rate of language acquisition across development (Tek, Mesite, Fein, & Naigles, 2014). Understanding the predictors of progress in pragmatic language and nonliteral language development for individuals with autism may be crucial for creating appropriate individualized targets for intervention (Klin et al., 2007). Comprehension of pragmatic language and nonliteral language share a common feature in that they require children to not only understand the individual meaning of words embedded in a sentence structure, but also to understand and respond appropriately to the communicative intent of language embedded in social and linguistic contexts (McTear & ContiRamsden, 1992). The specific definition of pragmatic language versus nonliteral language (as well as the boundary between these domains) varies between research studies, as well as the tasks used to assess abilities in these domains. The current study examines predictors of the development of pragmatic language and nonliteral language abilities using two subscales from the Comprehensive Assessment of Spoken Language (CASL; Carrow-Woolfolk, 1999). Pragmatic language in this case refers to the understanding and use of the literal aspects of context during communication (e.g., understanding the socially appropriate use of language for relevant contexts, such as: greetings, expressions of gratitude, making direct requests and answering questions). Nonliteral language refers to use of language where there is a specific mismatch between the literal meaning of the individual words of the phrase and the expected interpretation (e.g., ‘‘he was such a turtle’’ means that ‘‘he was slow’’ and not ‘‘he was a reptile with a shell’’). Types of nonliteral language include figurative language (e.g., metaphors, such as ‘‘the girl was a butterfly’’), sarcasm (e.g., saying ‘‘good job’’ when someone does poorly), and indirect requests (e.g., ‘‘I want all eyes on the board’’). Importantly, various aspects of nonliteral language may vary in how close the intended meaning of the phrase is to the individual words. The majority of research on both pragmatic language and nonliteral language abilities in children with ASD has used group-matching designs (often averaging across large age ranges). However, the overall heterogeneity in language abilities in children with ASD can be problematic for group matching designs (Tager-Flusberg, 2004). In particular, little is known about the development with age of these abilities in children with ASD. In addition, little is known about how vocabulary and/or syntax development relates to either pragmatic language or nonliteral language development for children with ASD, and whether or not these developmental relations differ between children with ASD and TD. Research has recently begun to use more developmentally sensitive approaches to examining language abilities for children with autism (e.g., Rundblad & Annaz, 2010b), but there are still remaining questions about the relative progress in development of pragmatic language and figurative language. The current study uses a cross-sectional developmental trajectory analysis (Thomas et al., 2009) to examine how pragmatic language and nonliteral language abilities develop in children with ASD and TD with regard to their age, syntax, and vocabulary abilities. 1.1. Typical and atypical development Pragmatic language abilities develop across childhood and into adolescence for TD children (Nippold, 2000; Ryder & Leionen, 2014). For example, Ryder and Leinonen (2003) suggest that 5-year-old children are able to understand and answer more complex questions than 3- or 4-year-old children based on their increasingly more complex ability to use contextual information. Lokusa, Leinonen, & Ryder (2007) examined pragmatic language abilities in children, ages 3–9, and found that while three-year-old children were able to use some context for resolving potential ambiguity when answering questions, the use of context increased substantially among the older children. Research suggests that children with ASD have difficulties in their pragmatic language development, including difficulties with the use language in the context of social conversations (Loukusa & Moilanen, 2009; Reichow, Salamack, Paul, Volkmar, & Klin, 2008; Volden, Coolican, Garon, White, & Bryson, 2009; Young, Diehl, Morris, Hyman, & Bennetto, 2005). Lam and Yeung (2012) found that children and adolescents with ASD, ages 8–15 years, performed worse than individuals with TD when using an observational scale to measure the pragmatic language abilities, such as having more instances of ‘‘out-of-synchrony communicative behavior’’ (Lam & Yeung, 2012). Children with ASD do show some developmental progress in pragmatic abilities. Specifically, Loukusa, Leinonen, Kuusikko, et al. (2007) found that children with ASD who were 10–12 years old performed better at pragmatic language measures than children with ASD who were younger (7–9 years), suggesting that pragmatic language abilities and the use of context increases with age and experience for individuals with ASD. Nonliteral language comprehension also shows a protracted developmental period across childhood in typical development. Some TD children, ages 4–6 years, are able to demonstrate an emerging knowledge of metaphors, with older children and adolescents showing greater levels of metaphor comprehension (Le Sourn-Bissaoui, Caillies, Bernard, Deleau, & Brule, 2012; Rundblad & Annaz, 2010a; Vosinadou & Ortony, 1983; Winner, Rosenstiel, & Gardner, 1976). Additionally, comprehension of indirect requests (Bernicot, Laval, & Chaminaud, 2007; Ledbetter & Dent, 1988) and sarcasm (Glenwright & Pexman, 2010) also increase with age for TD children. Children with ASD may also have difficulties with understanding various aspects of nonliteral language compared to their same-age typically developing peers (Dennis, Lazenby, & Lockyer, 2001; MacKay & Shaw, 2004; Martin & McDonald, 2004; Rundblad & Annaz, 2010b). Research has found that children with ASD (diagnosed with Asgerger’s syndrome or high functioning autism) perform worse than TD children on measures of metaphor comprehension (Dennis et al., 2001; Nikolaenko, 2004). In addition, MacKay and Shaw (2004) found that children with Asgerger’s syndrome showed difficulties on a variety of nonliteral language measures (including irony, metonymy, and


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indirect requests) compared to TD children. While many studies find differences, at the group-level, in pragmatic and nonliteral language abilities between TD children and children with ASD, important questions still remain about the development of these language abilities both in individuals with TD and ASD. 1.2. Factors supporting development Factors supporting understanding of pragmatic language and nonliteral language for children have been examined in the literature, including the potential contributions of basic language abilities and theory of mind (TOM) for both language domains, though these relationships have been more commonly studied with regard to nonliteral language. For TD children, there is some evidence that language abilities develop together in parallel, with basic language abilities (such as vocabulary) correlating strongly with nonliteral language abilities (Johnson, 1991; Rundblad & Annaz, 2010a). Research suggests that understanding of nonliteral language is supported by the ability to use the linguistic and social context to abstract meaning, and that vocabulary and syntax abilities are likely to support this process (Gernsbacher & Pripas-Kapit, 2012; Rundblad & Annaz, 2010a; Vosniadou, 1987). With regard to pragmatic language, Volden et al. (2009) found that a composite of expressive and receptive basic language abilities predicted a large portion of the variance in pragmatic language ability for children with ASD. Eisenmajer and Prior (1991) found that pragmatic language abilities correlated with several basic language measures, supporting the relationship between basic language abilities and pragmatic language. There is, however, some controversy in the literature as to whether or not nonliteral language abilities are disproportionately impacted for children with ASD, beyond their potential differences in basic language abilities. For example, Rundblad and Annaz (2010b) found that receptive vocabulary abilities only predicted performance on metonyms, but not metaphors, in their sample of 10 children with ASD. The relevance theory has driven research focused on the potential role of theory of mind for contributing to pragmatic language (Le Sourn-Bissaoui, Caillies, Gierski, & Motte, 2011; Loukusa & Moilanen, 2009) difficulties for children with ASD. Loukusa and Moilanen (2009) suggest that receptive vocabulary skills alone may be insufficient for understanding various aspects of pragmatic language for individuals with ASD, due to their likely difficulties with regard to the use of the social and linguistic context for understanding the meaning of these utterances. This line of research largely suggests that individuals with ASD struggle with these more advanced language abilities, even when they have good basic language abilities (e.g., receptive vocabulary), potentially due to weaknesses in utilizing the social and linguistic context for understanding the meaning of these utterances (Loukusa & Moilanen, 2009; Noens & van Berckelaer-Onnes, 2005). Thus, limitations with theory of mind abilities have been implicated in difficulties with understanding nonliteral language, especially with regard to understanding metaphors (Happe´, 1993). While primarily examined with regard to performance on first- or second-order false belief measures (e.g., Happe´, 1993), there have overall been mixed results with using false belief measures with older children, with some studies failing to find a unique contribution of false belief performance on nonliteral language abilities (e.g., Norbury, 2005; Rundblad & Annaz, 2010b). Other advanced measures of theory of mind, that are more developmentally sensitive across childhood, may instead better elucidate these relations. For example, performance on the children’s ‘‘reading the mind in the eyes’’ task (RMTE; Baron-Cohen, Wheelwright, Spong, Scahill, & Lawson, 2001) is correlated with idiom comprehension abilities (e.g., understanding phrases such as ‘‘raining cats and dogs’’) in children with autism (Whyte, Nelson, & Scherf, 2014), but it is unknown how this measure relates to measures of pragmatic language or nonliteral language abilities. Other research suggests that receptive vocabulary may be an area of relative strength for individuals with ASD when compared to their syntax abilities (Eigsti, Bennetto, & Dadlani, 2007). It is possible that syntax abilities, specifically, are predictive of their relative pragmatic (Volden et al., 2009) and nonliteral language abilities (Gernsbacher & Pripas-Kapit, 2012; Norbury, 2004, 2005; Whyte, Nelson, & Khan, 2013) for individuals with ASD, rather than a diagnosis of ASD in particular. For nonliteral language, Norbury (2005) found that syntax comprehension is a better predictor of metaphor comprehension than receptive vocabulary in particular, for children with language impairments, regardless of whether or not they exhibited social impairments associated with ASD. Additionally, children with ASD (especially those with good syntax abilities) were able to benefit from the use of context in understanding unfamiliar idiomatic phrases (Norbury, 2004). This line of research overall suggests that some children with ASD may not show impairments in pragmatic language or nonliteral language if they show significant progress in the development of their basic language abilities (Gernsbacher & Pripas-Kapit, 2012; Norbury, 2004, 2005; Whyte et al., 2014). Thus, understanding the role of expressive vocabulary and syntax abilities in supporting the development of pragmatic language and nonliteral language may be important for choosing appropriate matching variables to prevent biased conclusions about the nature of language abilities for individuals with ASD. Further, any theoretical account of language development in children with TD as well as those with ASD needs to take into account how the varied language domains may develop together across childhood, and how this also may interact with other domains, such as theory of mind. 1.3. Developmental trajectories One approach to examining potential relations between language domains across development, arising from the neuroconstructivist theory, is the cross-sectional developmental trajectory analysis (Thomas et al., 2009). The developmental trajectory analysis described by Thomas et al. (2009) allows for examining differences in the rate of


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growth of language abilities across development, even for cross-sectional samples, using ANCOVA models to directly compare the potential differences in the intercepts and slopes of the trajectories. It is possible to look at age-related trajectories for a group of children with ASD compared to TD children to see if the children with ASD have a different rate of development with regard to chronological age. It is also possible to examine the rate of development in a behavior as a function of various possible predictors, such as vocabulary or syntax (Annaz, Karmiloff-Smith, & Thomas, 2008; Thomas et al., 2009). This analysis approach can detect group differences in the intercept of the trajectory (defined as differences at the youngest point of overlap in scores on the predictor variable between the groups), slowed rate of development (defined as differences in the slopes of the trend lines between groups), or both (Annaz et al., 2008; Thomas et al., 2009) for each potential predictor variable. This stands in contrast to traditional group matching approaches that are only sensitive to overall group differences. For group matching approaches, the choice of matching variables needs to be theory-driven, as improperly matched groups runs a risk of leading to biased conclusions about the nature of symptoms in autism (Gernsbacher & Pripas-Kapit, 2012; Jarrold & Brock, 2004; Thomas et al., 2009). However, the trajectory approach does not require this same a priori group matching. Instead of controlling for differences in vocabulary and other potential matching variables between groups, this approach can tell us important information about how these variables may actually provide important contributions to the rate of development of pragmatic and nonliteral language abilities. While this approach does not reduce the need for longitudinal research, it is more developmentally sensitive than group matching designs, and can potentially inform future longitudinal research (Annaz et al., 2008; Thomas et al., 2009). This cross-sectional developmental trajectory approach has been previously used in several studies to examine the development of vocabulary, nonliteral language, and face processing abilities of children with ASD (Annaz, Karmiloff-Smith, Johnson, & Thomas, 2009; Kover, McDuffie, Hagerman, & Abbeduto, 2013; Rundblad & Annaz, 2010b). For example, Rundblad and Annaz (2010b) used a developmental trajectory analysis to examine two different aspects of nonliteral language (metaphors and metonyms) in 11 children with ASD, ages 5–11 years. For the children with ASD, receptive vocabulary predicted performance on metonyms, but not metaphors (Rundblad & Annaz, 2010b). However, additional measures of language abilities (including syntax abilities and expressive vocabulary abilities) and larger sample sizes are needed to further investigate development of nonliteral language for children with ASD. 1.4. Current study The current study uses the trajectory analysis methods of Thomas et al. (2009) to examine how age, vocabulary, syntax, and theory of mind predict performance on the pragmatic language and nonliteral language subtests of the Comprehensive Assessment of Spoken Language (CASL; Carrow-Woolfolk, 1999) for children with ASD and TD. The two subtests from the CASL (pragmatic language and nonliteral language) were chosen due to their potential use in measuring nonliteral and pragmatic language in clinical settings (e.g., Reichow et al., 2008). Additionally, these outcome measures are sensitive across a wide age range of development and are unlikely to show ceiling or floor effects for either individuals with TD or ASD. For the current study, the age range of 5–12 years was chosen such that it would be likely that TD children aged 5–6 may perform poorly on these measures, with increasing mastery of pragmatic and nonliteral language with increasing age for the TD children. Thus, it is hypothesized that there may not be a difference in the youngest age of overlap between groups for the current sample (representing the intercepts of the trajectories). The primary outcome of interest, instead, is the comparison of the rate of development between groups for each trajectory. The current study hypothesizes that children with ASD will have slower rates of development of nonliteral and pragmatic language trajectories from the TD group when examining chronological age, but the two groups will not have significantly different nonliteral or pragmatic language trajectories when examining relations with syntax age-equivalence scores, vocabulary age-equivalence scores, or TOM scores. Additionally, it is predicted that TOM abilities will significantly relate to individual differences in pragmatic and nonliteral language abilities, above and beyond the contributions of basic language abilities. 2. Method 2.1. Participants 2.1.1. Children with ASD A total of 26 children (21 m, 5 f), ages 5–12 (M = 9.07, SD = 1.87) years, who were previously diagnosed with an autism spectrum disorder (ASD), participated in the study (see Table 1). Highly verbal children were included if they had a previous diagnosis by a clinician under the DSM-IV criteria (American Psychiatric Association, 1994) of autism or high-functioning autism (n = 8), Asperger’s syndrome (n = 13), or pervasive developmental disorder (PDD-NOS; n = 5), based on parent report. The current study collapses across these diagnostic labels, as suggested by the newest DSM-5 criteria (e.g., Mahjouri & Lord, 2012); especially given the recent concerns of potential variability in how these diagnostic labels were applied by clinicians and researchers (e.g., Lord et al., 2012). Participants were included if they were able to speak in sentences, were receiving treatment services or educational supports related to ASD, and had a raw score above 60 on the parent report Social Responsiveness Scale (SRS) that indicated the presence of high current levels of autistic social impairments (Constantino, 2002). Scores on the SRS measure of autism symptom severity ranged from 60 to 155 (M = 108.61) for the children with


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Table 1 Mean (SD) and range for age, basic language abilities, and background measures for individuals with ASD and TD.

# of participants Age (in months) Syntax age-equiv. Vocabulary age-equiv. Nonverbal IQ age-equiv. SRS total raw

ASD

TD

26 115.5 (22.7) 69–153 93.0 (27.8) 48–150 111.1 (29.3) 58–180 120.0 (52.1) 62–222 107.1 (24.5) 60–155

69 105.97 (24.14) 63–147 108.62 (33.0)* 54–186 127.13 (37.7) 70–222 127.13 (37.6) 56–222 21.43 (11.7)* 2–50

Note: ASD, autism spectrum disorder; TD, typical development; syntax, syntax construction subtest of the Comprehensive Assessment of Spoken Language; vocabulary, verbal IQ subtest of the KBIT-2; nonverbal IQ, nonverbal IQ subtest from the KBIT-2; SRS, Social Responsiveness Scale. * p < .05.

ASD. One additional child with ASD (not included in the above totals) was excluded due to having a score below 60 on the SRS. A total of 21 of the 26 children with ASD were either currently or previously enrolled in speech therapy services. Participants were native English speakers from middle-class neighborhoods in the United States, and had normal or corrected-to-normal hearing and vision. Parent report indicated that 22 children were non-Hispanic and Caucasian, 2 children were Hispanic and Caucasian, 1 child was American Indian, and 1 child was biracial. Participants were recruited through fliers distributed through an autism participant recruitment database, special education classrooms at schools, and autism social skills intervention programs. 2.1.2. TD children A total of 69 typically developing children (39 m, 30 f), ages 5–12 years (M = 8.4 years, SD = 24.14), participated in the study (see Table 1). Children were excluded from the TD group if they had a diagnosis of a language impairments, a diagnosis of any other developmental disorder (such as ADHD or autism), were currently receiving speech therapy services, or had a score on the SRS above 50 (allowing for a 10 point gap in the SRS scores between groups). Participants were native English speakers from middle-class neighborhoods, with normal or corrected-to-normal hearing and vision. Parent report indicated that 57 children were non-Hispanic and Caucasian, 5 children were Hispanic and Caucasian, 1 child was American Indian, 5 children were biracial, and 1 child was African-American. Participants were recruited using fliers and phone calls to families from a participant recruitment database containing information from birth records in a geographic area within approximately an hour drive of the university. The TD children were recruited to span from the youngest mental age (represented by age-equivalence scores on vocabulary and syntax) to the oldest chronological age of the children with ASD (see Table 1). When using a trajectory analysis, restricting the normal variability of the TD children by removal of any individuals with high performance on any predictor variable may bias the results of the trajectories (Thomas et al., 2009), and it is recommended that specific group matching on the predictor variables not be done for this analysis. Thus, while the children with ASD had significantly lower syntax scores than TD children, t(93) = 2.29, p < .05, this mean difference for syntax is unlikely to impact any of the current study results. The children with ASD also had higher SRS scores than the TD children, t(93) = 23.02, p < .05. For the TD children, chronological age was predictive of 74.5% of the variance in vocabulary age-equivalence scores, from the verbal IQ subtest of the Kaufman Brief Intelligence Test, 2nd edition (KBIT2; Kaufman & Kaufman, 2004), and 66.2% of the variance in syntax age-equivalence scores from the CASL syntax construction subtest. In addition, standardized nonverbal IQ scores (from the KBIT-2 nonverbal IQ subtest) for the TD children were in the average range (M = 107.68; SD = 15.71). 2.2. Measures 2.2.1. Pragmatic language The Pragmatic Judgment subtest of the CASL was completed (Carrow-Woolfolk, 1999). Items on this measure involved judging the appropriate use of language in a social context (e.g., explaining that it is n’t appropriate to talk about dissecting worms when eating lunch at school, or to talk loudly in a movie theater), and verbally producing language appropriate for the context (e.g., ordering food in a restaurant, making requests, saying please or thank you, asking for directions, and greeting people appropriately). An example item includes: ‘‘Suppose the telephone rings. You pick it up. What do you say?’’ (Carrow-Woolfolk, 1999). Responses did not need to be grammatically correct as long as they were pragmatically appropriate for the situation. Raw scores could range from 1 to 63.


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2.2.2. Nonliteral language The Nonliteral Language subtest of the CASL was completed (Carrow-Woolfolk, 1999). This subtest contains various types of nonliteral language expressions, where the intent of the spoken phrases differs from a literal interpretation, categorized as figurative, indirect requests, and sarcasm (Carrow-Woolfolk, 1999). Each non-literal phrase is presented in the context of a short sentence supporting the figurative meaning. For example, items could include metaphors, such as: ‘‘Janet was waiting for Sara to finish changing into her swimsuit. Janet said, ‘You are such a turtle.’ What did Janet mean?’’ An example of indirect requests included: ‘‘The teacher told the class that he wanted all eyes on the board. What did he mean?’’ The two children scoring zero points on this measure (one with ASD and one with TD) are excluded from the analysis due to recommendations of the test protocols (Carrow-Woolfolk, 1999). Total nonliteral raw scores could range from 1 to 50. 2.2.3. Syntax The Syntax Construction subtest of the CASL was completed (Carrow-Woolfolk, 1999). The experimenter presented pictures and asked the child to verbally complete sentences. The targeted syntactic structures (e.g., verb tense, plurals, and prepositional phrases) in the sentences grew increasingly complex throughout the test. Syntax raw scores (out of a possible 60 points) were converted into age-equivalence scores (in months). 2.2.4. Vocabulary Receptive and expressive vocabulary abilities were assessed using the Verbal IQ subtests from the Kaufman Brief Intelligence Test, 2nd edition (KBIT2; Kaufman & Kaufman, 2004). This vocabulary measure was a composite of two sub-tests, verbal knowledge (measuring receptive vocabulary) and riddles (measuring verbal comprehension and expressive vocabulary). The riddles subtest asked children to provide one word answers to questions such as: ‘‘what hops, eats carrots, and has long ears?’’ (e.g., rabbit or bunny). Total raw vocabulary scores (out of 108 points) were converted into ageequivalence scores (in months). 2.2.5. Theory of mind The children’s version of the ‘‘Reading the Mind in the Eyes’’ (RMTE) task was used to measure theory of mind abilities (Baron-Cohen et al., 2001). During this task, children viewed pictures of the eye regions of faces. The children were asked to choose one of four labels which best matched the expression or mental state of the person in the pictures (i.e., shy, worried, thinking about something, not believing). The labels were read out loud to the children. Scores could range from 0 to 28. 2.2.6. Additional background measures The matrices subtest of the KBIT-2 was used to measure nonverbal IQ (Kaufman & Kaufman, 2004). One parent for each child completed the Social Responsiveness Scale (SRS), which is a questionnaire used to assess current social skills and repetitive behaviors, with higher scores indicating more severe social impairments associated with ASD symptomology (Constantino, 2002). Nonverbal IQ and SRS scores were not significantly correlated with either pragmatic language or nonliteral language scores (all p > .05). 2.3. Procedure Prior to their child’s participation in the study, a parent or legal guardian provided informed consent and then completed a brief demographic questionnaire and the SRS for their child. In addition, all children provided verbal assent prior to participating. The experimental procedures complied with the standards of the university’s review board. Participant families were compensated for their time. The study was completed over two hours (completed over 1 or 2 sessions). Testing took place either in offices in the research lab, or in a quiet room in the child’s home (for families who were unable to travel to the lab). Testing procedures were kept as similar as possible across testing locations, and children completed the tasks in the same order. For all tasks in the current study, the test items were read aloud to the children, and children were asked to respond verbally (or by pointing when appropriate). Breaks were provided between tasks as necessary to ensure attention. 2.4. Developmental trajectory analysis The cross-sectional developmental trajectory analysis approach followed the methods of Thomas et al. (2009). Separate regressions were conducted for each group (ASD and TD) to see if each of the outcome variables (pragmatic language and nonliteral language) increases with each of the predictor variables (chronological age, syntax age-equivalence scores, vocabulary age-equivalence scores, or TOM scores). These regressions allow for establishing whether or not the outcome measures show a significant relation with the predictors for each of the two groups (e.g., if pragmatic language increases with age). In addition, Cook’s Distance values were calculated for each regression, with all values less than the recommended cut-off of one, suggesting that there were no significant outliers in the current dataset for either the ASD or TD groups. Once establishing that there are reliable linear trajectories for each group, the trajectories of the two groups are compared using ANCOVA models, with pragmatic CASL scores and nonliteral CASL scores as separate outcome variables. Thomas et al. (2009) suggests that each predictor variable needs to be re-scaled to compare the groups starting from the youngest age at which the two groups overlap to be able to interpret differences between groups at the intercept. To prevent extrapolating


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outside the age/score ranges measured, the predictor variables were scaled by subtracting each individual’s score (from both groups) from the youngest age at which the two groups overlapped, so that zero represented the first point of overlap between the two trajectories (Thomas et al., 2009). This scaling is done so that the value of zero for the intercept represents the start of the age range that has been measured, and thus potential differences measured in the intercept of the trajectory (e.g., the ‘‘youngest age of overlap’’ or ‘‘earliest point of overlap’’) refer to measurements at this zero point for each predictor variable. For chronological age and RMTE scores, this was scaled as months from the youngest age of the ASD group. For the vocabulary and syntax predictor variables, where the TD group performed slightly higher than the ASD group, this was instead scaled to months from youngest TD age-equivalence score. To compare cross-sectional developmental trajectories for chronological age, an ANCOVA is conducted with the predictor variables of: diagnosis as a fixed factor (representing potential differences in the intercept – at the youngest age of overlap between groups), as well as age (months from the youngest age of overlap) as a covariate. This model also includes the analysis of the interaction between scaled age scores and diagnosis (representing the slope of the trajectory, and specifically measuring potential differences in the rate of development with age; Thomas et al., 2009). Thus, unlike group matching studies (comparing one mean value averaging across age), the results from this trajectory analysis provide two pieces of information about potential differences between the trajectories for individuals with ASD and TD with regard to age. First, is there a significant difference in the youngest age of overlap between the groups (representing the intercept of this trajectory)? Second, is there a difference in the rate of development between the groups (indicated by a significant interaction between diagnosis and the scaled age variable)? The results from both of these potential differences are included in the below results, though this second result (the rate of development) is of primary interest in the current study. The same models are then repeated separately for the vocabulary, syntax, and RMTE predictors, in place of age in the models.

3. Results 3.1. Pragmatic language trajectories Pragmatic language scores from the CASL increased with chronological age, both for TD children, R2 = .66, F(1,66) = 126.20, p < .001, and children with ASD, R2 = .32, F(1,23) = 10.72, p < .01. Comparing the groups, the TD children and children with ASD did not significantly differ at the youngest age of overlap between groups (representing no differences in the intercept of the trajectories), p > .05. Children with ASD exhibited a slower rate of development for pragmatic language with chronological age compared to the TD children; interaction of age x group (representing differences in the slope of the trajectories), F(1,89) = 3.90, p < .05, h2p ¼ :04. See Fig. 1.

[(Fig._1)TD$IG]

(A)

(C)

(B)

(D)

Fig. 1. Developmental trajectories of pragmatic language (raw scores) in children with ASD and TD, with regard to (A) chronological age (in months), (B) syntax age-equivalence scores and (C) vocabulary age-equivalence scores, and (D) theory of mind (TOM) raw scores. The R2 values indicate proportion of variance accounted for by the predictor for each group (TD and ASD).


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Pragmatic language increased with syntax age-equivalence scores, both for TD children, R2 = .68, F(1,66) = 139.57, p < .001, and children with ASD, R2 = .70, F(1,23) = 53.21, p < .001. Comparing the groups, the TD children and children with ASD did not significantly differ at the earliest point of overlap in syntax scores between groups, p > .05, and further there was not a significant interaction between syntax abilities and diagnosis, p > .05, suggesting that the groups did not differ in their pragmatic trajectories with regard to syntax. See Fig. 1. Pragmatic language raw scores increased with vocabulary age-equivalence scores, both for TD children, R2 = .72, F(1,64) = 163.32, p < .001, and children with ASD R2 = .61, F(1,23) = 36.41, p < .001. Comparing the groups, the TD children and children with ASD did not significantly differ at the lowest point of overlap in vocabulary scores between groups, p > .05, and there was not a significant interaction between vocabulary and diagnosis, p > .05, suggesting that the groups did not differ in their pragmatic trajectories with regard to vocabulary abilities. See Fig. 1. Pragmatic language raw scores increased with TOM scores, both for TD children, R2 = .32, F(1,66) = 31.43, p < .001, and children with ASD R2 = .37, F(1,23) = 13.52, p < .001. Comparing the groups, the TD children and children with ASD did not significantly differ at the lowest point of overlap in TOM scores between groups, p > .05, and there was not a significant interaction between TOM and diagnosis, p > .05, suggesting that the groups did not differ in their pragmatic trajectories with regard to TOM abilities. See Fig. 1. 3.2. Nonliteral language trajectories Nonliteral language raw scores from the CASL increased with chronological age, both for TD children, R2 = .70, F(1,66) = 151.46, p < .001, and children with ASD, R2 = .21, F(1,23) = 6.17, p < .05. Comparing the groups, the TD children and children with ASD did not significantly differ at the youngest age of overlap between groups, p > .05. Children with ASD exhibited a slower rate of development for nonliteral language with chronological age compared to the TD children; interaction of age x group, F(1,89) = 14.06, p < .001, h2p ¼ :14. See Fig. 2. Nonliteral language raw scores increased with syntax age-equivalence scores for TD children, R2 = .69, F(1,66) = 146.41, p < .001, and children with ASD, R2 = .57, F(1,23) = 29.82, p < .001. Comparing the groups, the TD children and children with ASD did not significantly differ at the lowest point of overlap in syntax scores between groups, p > .05. Children with ASD exhibited a slower rate of development than TD children for nonliteral language with regard to syntax age-equivalence scores, F(1,89) = 3.94, p < .05, h2p ¼ :04. See Fig. 2. Nonliteral language raw scores increased with vocabulary age equivalence scores, both for TD children, R2 = .68, F(1,64) = 133.43, p < .001, and children with ASD, R2 = .66, F(1,23) = 44.18, p < .001. Comparing the groups, the TD children and children with ASD did not significantly differ at the lowest point of overlap in vocabulary scores between groups, p > .05, and there was not a significant interaction between vocabulary abilities and diagnosis, p > .05, suggesting that the groups did not differ in their nonliteral language trajectories with regard to vocabulary abilities. See Fig. 2.

[(Fig._2)TD$IG]

(A)

(C)

(B)

(D)

Fig. 2. Developmental trajectories of nonliteral language (raw scores) in children with ASD and TD with regard to with regard to (A) chronological age (in months), (B) syntax age-equivalence scores and (C) vocabulary age-equivalence scores, and (D) theory of mind (TOM) raw scores. The R2 values indicate proportion of variance accounted for by the predictor for each group (TD and ASD).


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Nonliteral language raw scores increased with TOM scores, both for TD children, R2 = .44, F(1,66) = 51.70, p < .001, and children with ASD, R2 = .31, F(1,23) = 10.33, p < .01. Comparing the groups, the TD children and children with ASD did not significantly differ at the lowest point of overlap in TOM scores between groups, p > .05, and there was not a significant interaction between TOM abilities and diagnosis, p > .05, suggesting that the groups did not differ in their nonliteral language trajectories with regard to TOM abilities. See Fig. 2. 3.3. Partial correlations To further examine the relationship between TOM abilities and each of the two outcome variables, partial correlations (controlling for both vocabulary and syntax age-equivalence scores) were conducted. When controlling for basic language abilities, TOM abilities significantly correlate with pragmatic language scores for TD children, r(61) = .25, p < .05, but not for children with ASD, p > .05. TOM abilities significantly correlate with nonliteral language abilities for TD children, r(61) = .42, p < .01, and for children with ASD, r(20) = .37, p < .05. 4. Discussion The current study used a cross-sectional trajectory approach, following the methods of Thomas et al. (2009), to examine the development of pragmatic language and nonliteral language (metaphor, sarcasm, indirect requests) for children with ASD and TD children. For both TD children and children with ASD, ages 5–12 years, chronological age, basic language abilities (syntax and vocabulary), and TOM abilities, were significantly predictive of raw scores on both the pragmatic language and nonliteral language subtests of the CASL. Older children with ASD had higher numbers of correct responses on the pragmatic language and nonliteral language subtests of the CASL than younger children with ASD. In addition, children with ASD who had higher vocabulary or syntax age-equivalence scores performed better on nonliteral language and pragmatic language measures than children with ASD who had lower vocabulary or syntax scores. For the trajectory analysis, as predicted, no analysis showed significant differences for the youngest age of overlap between groups (representing the intercept of the trajectory), or for the lowest overlap in either vocabulary or syntax scores. This was largely due to the low scores of the TD group at the youngest ages (e.g., 5 years) on the particular measures used in this study, and this age range (5–12 years) was chosen to be able to specifically focus on the rate of development for these outcome variables. The current results suggest that while the children with ASD showed a slower rate of development of both pragmatic language and nonliteral language compared to TD children with regard to age, the rate of development for the groups did not significantly differ when accounting for vocabulary abilities or TOM abilities. Similarly, the rate of development did not significantly differ for pragmatic language abilities when accounting for syntax abilities. However, for the nonliteral language measure, when accounting for syntax abilities, children with ASD did show a significantly slower rate of development than TD children. 4.1. Pragmatic language development The current study found that syntax abilities (as measured by the syntax construction subtest of the CASL) and vocabulary abilities (receptive vocabulary and riddles subtest of the KBIT-2) were both strong predictors of pragmatic language (as measured by the pragmatic language subtest of the CASL) in children with ASD and TD children. The results of the current study are consistent with prior research studies suggesting that basic language abilities can predict current performance on pragmatic language abilities in children with ASD (Volden et al., 2009). For example, Volden et al. (2009) found that a combination of expressive and receptive basic language abilities significantly predicted pragmatic language abilities in children, ages 6–13 years, with ASD. The results for age suggested that there was not a significant difference at the youngest age of overlap between the two groups, but that the slopes of the trajectories (measuring rate of development with age) were significantly different. The current study adds to the literature with the finding that pragmatic language abilities show a slower rate of development with age for children with ASD, compared to TD children, but the rate of development did not differ between groups with regard to either vocabulary or syntax abilities. This suggests that individuals with ASD show similar trajectories for pragmatic abilities when accounting for their basic language abilities, supporting theories of pragmatic language development that emphasize the important foundation of basic language abilities for individuals with ASD. Additionally, pragmatic language abilities were higher for both children with ASD and TD when they had higher TOM abilities (with no significant difference between groups in the intercept and slope of the trajectories). However, when controlling for basic language abilities, TOM was only related to pragmatic language for the TD group and not the children with ASD. This TOM task measures the understanding of complex mental states (based on labeling the expressions of the eye regions of faces). The results provide some positive evidence for the conclusion that theory of mind abilities potentially contribute to the development of pragmatic language abilities, particularly for TD children. 4.2. Nonliteral language development Nonliteral language abilities showed significant increases with age and both measures of basic language abilities for children with ASD and TD children. When examining the trajectory for chronological age, there was no difference between


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the groups at the youngest age of overlap, but children with ASD showed a slower rate of development with increasing age. A benefit of the trajectory analysis in the current study was that separately including vocabulary or syntax as predictors allowed for revealing how these two aspects of basic language show mostly similar, but also certain different relations, with aspects of nonliteral language development. For the syntax predictor, children with ASD showed a slightly but significantly slower rate of development compared to the TD group for nonliteral language. In contrast, the rate of development for nonliteral language abilities was not significantly different between children with ASD and TD in relation to their vocabulary abilities. The results of the current study are not consistent, however, with one previous study of nonliteral language using a similar cross-sectional developmental trajectory analysis. Rundblad and Annaz (2010b) found that the children with ASD did not show a significant relation between either predictor variable (age or receptive vocabulary) and metaphor performance, with many of the children with ASD scoring zero out of 10 possible points on metaphor comprehension. Additionally, the children with ASD did not show increases in metonym comprehension with chronological age, though metonym scores did increase with vocabulary. Especially given that Rundblad and Annaz (2010b) found different patterns in their trajectory analysis between their two measures of nonliteral language, it is likely that different measures of nonliteral language may show different trajectory results. For the current study, the use of developmentally sensitive measures (e.g., inclusion of a mix of easy and hard items in the CASL measure) and a larger sample of individuals with ASD provided evidence that children with ASD are able to learn nonliteral language, that knowledge of nonliteral language increases with age, and that their nonliteral language development is largely in line with their combined receptive and expressive vocabulary developmental levels. The vocabulary measure used in the current study included verbal comprehension abilities (e.g., answering questions, such as ‘‘what hops, eats carrots, and has long ears?’’) in addition to receptive vocabulary abilities, which differs from the more traditional receptive vocabulary measure used in previous studies (e.g., Rundblad & Annaz, 2010b). Thus, the choice of language variables is likely to impact the results. The present results suggest that when particular children with ASD have managed to achieve significant receptive and expressive vocabulary skills, these skills will dynamically play into greater successes in acquiring new levels of nonliteral language, in a fashion similar to the dynamic supports in TD provided by advancing vocabulary abilities. However, children with ASD did show slower rates of development in their nonliteral language abilities when considering scores on syntax (although syntax was still a significant predictor of nonliteral language scores), which does not directly support the hypothesis of Gernsbacher and Pripas-Kapit (2012). Instead, a combination of basic language skills and TOM abilities may play a role in the development of nonliteral language comprehension. Scores on the RMTE task also showed significant relations to nonliteral language in both the trajectory analysis and the partial correlation analysis. TOM abilities predicted nonliteral language performance for both TD children and children with ASD (with no group difference in the intercept or slopes of the trajectory analysis). The correlation between TOM abilities and nonliteral language remained significant even after controlling for basic language abilities. The current study supports the hypothesis that nonliteral language skills are related to both basic language and TOM abilities. 4.3. Limitations and future directions The sample in the current study was comprised of high functioning and verbal individuals with ASD (diagnosed with autism, Asgerger’s syndrome, or PDD-NOS). Therefore, it is possible that the results may not generalize to lower functioning individuals with ASD. Additionally, current scores on diagnostic observational measures, such as the Autism Diagnostic Observation Scale (ADOS; Lord, Rutter, DiLavore, & Risi, 1999) were not available for the participants with ASD in the current study. Future research should include, when feasible, ADOS testing scores for participants. It is important to note that nonliteral language skills have been shown to not correlate strongly with measures of autistic social symptoms (Norbury, 2004, 2005; Rundblad & Annaz, 2010a, 2010b), and both children with a diagnosis of autism or a diagnosis of Asperger’s syndrome showed difficulties with metaphor comprehension in previous research. Thus, differences in diagnostic inclusion criteria would be unlikely to impact the results of the present research. It may, however, be possible to see different results when measuring receptive skills through procedures that do not require complex language production. Future research should use additional measures of syntax, nonliteral, and pragmatic aspects of language to see if the present results replicate across different measures of language development. Group matching designs, in particular, should be sensitive to how variability in the choices of matching variables may lead to different conclusions about the nature of language abilities for children with ASD (e.g., Norbury, 2005; Whyte et al., 2014). Future research should include longitudinal designs to examine how various aspects of nonliteral and pragmatic language develop over time within individual children. While the cross-sectional trajectories are a helpful tool for examining trends across groups of people, it is likely that sub-groups of individuals with ASD will show different patterns of language development over time. The cross-sectional trajectories, however, can help with informing longitudinal study designs by suggesting measures that are likely to show development across target age ranges. The current study suggests that the CASL may be developmentally sensitive measures for examining pragmatic and nonliteral language abilities for children with ASD, and that the KBIT-2 composite of expressive and receptive vocabulary abilities may be an important predictor of success on figurative and pragmatic language abilities.


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4.4. Conclusions The current study examined cross-sectional developmental trajectories for both pragmatic language (understanding contextually appropriate language) and nonliteral language subtests of the CASL (comprised of figurative language, sarcasm, and indirect requests). The children with ASD (ages 5–12 years) showed a slower rate of development for both pragmatic language and nonliteral language with chronological age. However, both chronological age and basic language age-equivalence scores significantly predicted performance on nonliteral and pragmatic language for the children with ASD, as well as for children with TD. The present results provide a strong challenge to prior suggestions (e.g., Loukusa & Moilanen, 2009) that pragmatic and/or nonliteral language skills represent particularly difficult domains for children with ASD, above and beyond basic language difficulties. Instead, the pragmatic trajectories for children with ASD and TD children did not significantly differ when accounting for either vocabulary or syntax abilities. In like fashion, for nonliteral language abilities, developmental trajectories for the TD and ASD did not significantly differ when accounting for vocabulary abilities, though the children with ASD did show slower rates of development for nonliteral language when accounting for syntax abilities. Although the children with ASD may reach vocabulary and syntax benchmarks more slowly than the typically developing children, for any given level of vocabulary or syntax the children with ASD appear to have dynamically applied their basic language skills with similar effectiveness as TD children to the mastery of new levels of pragmatic language and to nonliteral language. These results give new emphasis to the previously tentative conclusion that when children with ASD are able to improve their basic language skills then it is highly likely that the children will also make improvements in pragmatic language and in use of other nonliteral language expressions. Additionally, a combination of basic language abilities, as well as advanced TOM abilities, may aid in the understanding of both pragmatic language and nonliteral language. Thus, when a child is in the progress of newly sorting out multiple relations between a pragmatic linguistic expression and different contexts (or between literal versus nonliteral meanings), these new mappings will be more richly supported as both the basic language abilities and TOM abilities of the child improve. Understanding predictors of individual differences both in the present study and in future longitudinal research can aid the selection of individualized intervention targets in multiple domains of language, including pragmatic language and nonliteral language abilities (Nelson, 2001; Nelson, Welsh, Camarata, Heimann, & Tjus, 2001; Whyte, Nelson, & Khan, 2013). Further, the results of the current study should provide encouragement to future innovations in language therapies and interventions that would aim to jointly facilitate complex syntax growth for individuals with ASD and growth in aspects of pragmatic or nonliteral language. Funding A liberal arts dissertation improvement award from Penn State provided funding for this research. Conflict of interest None declared. Appendix A. Continuing education questions 1. According to past research, typically developing children as young as _____ are able to demonstrate some basic knowledge of nonliteral language. a. 2 to 3 years b. 4 to 6 years c. 7 to 8 years d. 9 to 11 years 2. Differences between group matching designs and the cross-sectional developmental trajectory analysis include: a. Longitudinal data b. The need for a priori group matching c. Cross-sectional data d. Information about group differences in the rate of development e. Both B and D 3. The results for the pragmatic language outcome measure suggests that compared to typically developing children, children with autism spectrum disorders (ASD) show: a. Delays in the onset of pragmatic language with regards to chronological age b. Delays in the rate of pragmatic language with regards to chronological age c. Delays in the rate of pragmatic language with regards to syntax abilities d. Delays in the rate of pragmatic language with regards to vocabulary abilities 4. True or false: The current study found that children with ASD who had higher vocabulary abilities performed better on nonliteral language than children with ASD who had lower vocabulary abilities.


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5. For typically developing children, figurative and pragmatic language abilities are significantly predicted by: a. Age b. Vocabulary abilities c. Syntax abilities d. Theory of mind e. All of the above

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