Hearing me hearing you: Reciprocal effects between child and parent language in autism and typical development

Highlights

• Longitudinal corpus of naturalistic parent-child interactions.

• 32 children with ASD and 35 language-matched controls.

• Language development in TD and ASD alike is shaped by child-based and environmental factors.

• Parents adjust linguistic production to the child’s level of production.

Abstract

Language development in typically developing children (TD) has traditionally been investigated in relation to environmental factors, while language in children with autism spectrum disorder (ASD) has primarily been related to child-based factors. We employ a longitudinal corpus of 32 preschoolers with ASD and 35 linguistically matched TD peers recorded over 6 visits (ranging between 2 and 5 years of age) to investigate the relative importance of child-based and environmental factors in language development for both populations. We also investigate the reciprocal interaction between children’s response to parents’ input, and parents’ response to children’s production. We report six major findings. (1) Children’s production of word types, tokens, and MLU increased across visits, and were predicted by their Expressive Language (EL) (positively) and diagnosis (negatively) from Visit 1. (2) Parents’ production also increased across visits, and was predicted by their child’s nonverbal cognition (positively) and diagnosis (negatively) from Visit 1. (3) At all visits and across groups, children and parents matched each other in lexical and syntactic production; (4) Parents who produced longer MLUs during a given visit had children who produced more word types and tokens, and had longer MLUs, at the subsequent visit. (5) When both child EL at Visit 1 and parent MLU were included in the model, both contributed significantly to future child language; however, EL accounted for a greater proportion of the variance. (6) Finally, children’s speech significantly predicted parent speech at the next visit. Taken together, these results draw more attention to the importance of child-based factors in the early language development of TD children, and to the importance of parental language factors in the early language development of children with ASD.

Introduction

Successful language acquisition in childhood is a function of environmental factors and child-based factors, including biological components (Gleitman, 1984, Naigles and Bavin, 2015). Although one of the most enduring goals of research in this field is to discern the relative contributions of each (Gleitman and Wanner, 1982, Slobin, 1985), the focus of research has been largely tied to the population under study. Variability in language development in typically developing (TD) populations has been primarily investigated from the ‘environmental factors’ perspective, consistently finding that children who receive greater amounts of input, more responsive social interactions with caregivers, and more diverse input, demonstrate earlier and/or more complex language use (Gathercole and Hoff, 2007, Rowe and Goldin-Meadow, 2009, Rowe, 2012, Tamis-LeMonda et al., 2014). Studies of child-based variability in TD children have been limited, with twin studies being the major exceptions (Colledge et al., 2002, Reznick et al., 1997). On the other hand, research on the variability of language development in children with autism spectrum disorders (ASD) has emphasized child-based factors such as verbal and nonverbal intelligence and autism severity, which account for significant variance in these children’s language outcomes (Anderson et al., 2007, Bang and Nadig, 2015, Bopp et al., 2009, Ellis Weismer and Kover, 2015, Howlin et al., 2014, Kenworthy et al., 2012, Magiati et al., 2014, Munson et al., 2008, Paul et al., 2008, Szatmari et al., 2009, Venker et al., 2015, Wodka et al., 2013), while investigations of the structure and content of parental input are only just beginning (Bang and Nadig, 2015, Nadig and Bang, 2017, Venker et al., 2015).

In the current paper, we combine these approaches in a longitudinal comparison of the language trajectories of initially language-matched TD children and children with ASD. We assess the influence of the children’s early cognitive abilities, their autism symptomatology, and their parents’ language production, on these children’s language-learning trajectories. Moreover, we investigate an aspect of language-learning that has been often overlooked: the influence of children’s linguistic productions, cognitive ability, and (when relevant) autism severity on their parents’ speech; that is, how children affect their own language-learning environment (Sameroff, 2010, Warlaumont et al., 2014). Note that while we are aware of the importance of timing and responsiveness in parent-child interactions (Warlaumont et al., 2014, Weed et al., 2017), in the current work we focus on verbal aspects only (i.e., utterance length, unique words and total words).

How variable is language development in TD children? Broadly speaking, the overall developmental trajectory of TD children seems consistent across many investigations, and these children tend to reach their language-related milestones within the span of a few months. For example, they demonstrate sensitive auditory processing of speech at or before birth and linguistic processing by 6 months of age; they produce their first words around 12 months and show dramatic increases in vocabulary use at 18–24 months; at the same age, they begin to produce word or morpheme combinations, and increase steadily in mean length of utterances (MLU) produced from two to four years, adding morphemes referring to tense and aspect, plurality and modality, elaborating relative clauses and sentence complements (see Hoff, 2013, and references therein).

However, each of these broad general stages manifests large individual differences in age of attainment, as well as efficiency and frequency of use (Fernald et al., 2013, Hoff-Ginsberg, 1985). These individual differences in child behavior are often correlated with consistent variability in numerous aspects of the children’s environments (Hoff, 2006, Huttenlocher et al., 2007), leading many researchers to attribute the former to the latter. For example, children who hear more word tokens subsequently produce more words themselves (Hart and Risley, 1995, Huttenlocher et al., 1991, Mahr and Edwards, 2018, Naigles and Hoff-Ginsberg, 1998); moreover, children who hear a more diverse lexicon, including more word types, more rare words, and higher type-token ratios, also subsequently produce a greater diversity of word types and score higher on standardized tests of vocabulary (Barnes et al., 1983, Hoff, 2003, Huttenlocher et al., 2010, Jones and Rowland, 2017, Pan et al., 2005, Rowe and Goldin-Meadow, 2009, Rowe, 2012, Song et al., 2014)). Finally, the syntactic complexity and diversity of parental input has also been found to positively impact children’s lexical and syntactic development (Gleitman et al., 1984, Hoff, 2003, Hoff and Naigles, 2002, Hoff-Ginsberg, 1985, Huttenlocher et al., 2010, Naigles and Hoff-Ginsberg, 1998, Rowe and Goldin-Meadow, 2009, Szagun and Stumper, 2012).

Far less research has investigated the role of more biologically-based factors on variation in TD children’s language development. The most prominent examples are those comparing monozygotic (MZ) and dizygotic (DZ) twins, where language outcomes that are more similar in the MZ than DZ twins are attributed to genetic influences. Indeed, twin studies have revealed that aspects of two-year-olds’ word comprehension and production (Price et al., 2000, Reznick et al., 1997) have genetic bases that seem to be independent of nonverbal cognition. Composite measures of four-year-olds’ language also demonstrate moderate genetic influences (Kovas et al., 2005); by this age, though, the degree to which these are independent of the (also) genetically-based influences of nonverbal cognition is unclear (Colledge et al., 2002). Intriguingly, a recent study has suggested that correlations between self-reported variation in parents’ linguistic production and parent-reported variation in their children’s language are not necessarily due to just environmental influence on the child development but also to shared genes, that is, to shared innate predispositions (Dale, Tosto, Hayiou-Thomas, & Plomin, 2015). The contribution of genetic factors to TD twins’ early language variability seems clear, although the association of biological factors to specific aspects of TD language is—understandably, given the complexity of the gene-brain-behavior mapping—much less well-elaborated than the associations between environmental factors and children’s language (but see Canfield, Edelson, & Saudino, 2017).

In sum, while TD children progress in broad strokes through the same macroscopic stages at a roughly comparable rate, variability among these children exists, and evidence suggests that both environmental (linguistic input) and child-based factors can explain this variance. Further, there is some indication that these factors may interact with one another.

While research on TD children’s language has focused primarily on environmental factors, research on children with ASD has focused on the more child-based aspects of the autism spectrum disorder itself on language development.

Researchers have long struggled to capture the distinctive quality and diversity of language in people with ASD (Kanner, 1943, Lord et al., 2004), and have distinguished anywhere from two to seven distinct language phenotypes. For instance, Tek and colleagues defined two groups of participants with ASD along a median split of their verbal skills, and found that the higher performing children were not significantly different from the TD participants (Tek, Mesite, Fein, & Naigles, 2014). Anderson and colleagues defined four groups based on their performance at age 9: children fluently using complex sentences; those using some complex sentences, but not fluently; children only using words in isolation; and those minimally verbal; with similar representation of children with ASD in these groups (Anderson et al., 2007). Wittke and colleagues divided their 5-year-old participants into three groups according to the sheer amount of their verbal productions and the number of grammatical errors they committed, yielding subgroups of language impaired, grammatically impaired, and normal language children (Wittke, Mastergeorge, Ozonoff, Rogers, & Naigles, 2017). Taking a different approach, Pickles and colleagues used latent growth curve analysis on parental reports to infer from the data seven latent classes of linguistic development in participants with ASD between the age of two and six: from nearly typical to ‘catch-up’ to different kinds of delay. Development between the ages of six and nineteen years showed remarkably similar patterns across the classes (Pickles, Anderson, & Lord, 2014). While subgrouping can be important for decisions about interventions, it is not yet clear to what extent the subgroups thus far proposed have independent validity; moreover, it is becoming increasingly recognized that ASD symptomatology—and language impairments—exist on spectra or continua rather than clustering into subgroups (e.g., Archibald and Noonan, 2015, Constantino, 2011, Tomblin, 2015). In our study, we attempt to more fully assess the heterogeneity of the participants by modelling the children’s actual verbal and non-verbal cognitive skills as continuous variables, rather than splitting them into groups.

Studies investigating the early predictors of later language variability in these individuals have typically used standardized tests of language and communication, rather than the more specific language samples and/or linguistic assessments often used by research in TD samples. For example, variability in expressive communication was predicted positively by children’s earlier scores on the receptive communication subscale of the Vineland Adaptive Behavior Scales (VABS; Sparrow, Balla, & Cicchetti, 2005) and negatively by their stereotyped behaviors on an earlier autism diagnostic assessment (Paul et al., 2008, Szatmari et al., 2009). The dominant and independent influences of (higher) nonverbal cognition and (milder) autistic behaviors on subsequent language performance, as measured by standardized tests of structural language, have been replicated by a number of different research groups over the past decade (Bopp et al., 2009, Ellis Weismer and Kover, 2015, Thurm et al., 2015, Wodka et al., 2013, Yoder et al., 2015).

There has been much less research investigating how children with ASD might make use of the linguistic input they are provided with; however, the extant data are mostly consistent with findings from TD children. Venker et al (2015) showed that telegraphic speech (omission of e.g. determiners to produce utterances like “Mommy go” or “cup full”) in parents of toddlers with ASD was a slight negative predictor of lexical diversity in these children a year later, indicating that richer syntactic input supports language-learning in children with ASD as well in TD children. As Nadig and Bang (2017) review, parents who produce more word tokens (Warren et al., 2010), noun types (Swensen, 2007), and utterances with longer MLUs (Bang & Nadig, 2015) have children with ASD who subsequently produce more words. Parents who produce more diverse yes/no and wh-questions have children with ASD who subsequently produce and understand more complex grammar themselves (Goodwin et al., 2015, Swensen et al., 2007). Finally, in a meta-analysis, Sandbank and Yoder (2016) report that four studies show a strong correlation between the length of parents’ utterances and positive language outcomes in children with autism (Burgess et al., 2013, Grelle, 2013, Konstantareas et al., 1988, Nadig and Bang, 2017).

These positive findings are very encouraging; however, many of these studies have had limited sample sizes, and have only rarely included both TD children and children with ASD in the same model to test whether the effects of parental input hold similarly for both groups (Bang & Nadig, 2015). Moreover, there have been concerns about whether the parental inputs provided to children with ASD and those provided to TD children are comparable. These concerns arose because children with ASD are characterized by social disengagement (DSM-V, APA 2015), which affects the duration and kind of interactions they engage in with their caregivers, and so may also influence the content and structure of that input. In other words, the children’s abilities and challenges have the potential to reciprocally affect their parents’ input. Studies comparing parents of age-matched children with ASD and TD children show significant differences in the amount and complexity of parental linguistic production, probably due to the children’s language delays. However, when the children are linguistically well matched no differences in parental linguistic production have been found (Bang & Nadig, 2015). This suggests that the effects of linguistic ability and social reciprocity might be disentangled.

Because adults are the experts in language use, we tend to think of the parent-child relationship as unidirectional; that is, parents speak and children learn about the local language from that speech (Gathercole & Hoff, 2007). However, a number of proposals about child development as a whole have emphasized the interactional nature of these processes, claiming that cross-generational influence is bidirectional, with children influencing parents as well as parents influencing children (Chapman, 2000, Sameroff, 2010). Direct evidence for this proposal is somewhat mixed, though. For example, early research on child-directed speech seemed to indicate stability over time, suggesting that parents do not adapt to their children’s language growth (Cohen and Beckwith, 1976, Moss, 1967, Nelson and Bonvillian, 1973, Smolak and Weinraub, 1983). However, researchers have often reported that the input to older children is more complex than that to younger children; for example, parental MLUs increase across the span from child ages 1 to 5 years (Newport et al., 1977, Rowe, 2012, Saint-Georges et al., 2013, Schwab and Lew-Williams, 2016), although the sheer quantity of speech does not seem to change (Gilkerson et al., 2017, Rowe, 2012). Moreover, when controlling for child age but not language level, researchers report that parents of TD children produce more complex speech than parents of children with ASD, and parents of higher functioning children with ASD produce more complex speech than parents of lower-functioning children (Nadig & Bang, 2017). When child language level is controlled, though, TD/ASD group differences in parental word tokens, types, MLU, and many aspects of question use are not significant (Bang and Nadig, 2015, Goodwin et al., 2015, Slaughter et al., 2007, Swensen et al., 2007, Swensen, 2007, Wolchik and Harris, 1982), suggesting that parents may respond more to the language level of their child, rather than directly to ASD severity.

Often these comparisons, however, have been confined to single points in developmental time; thus, what is still underinvestigated is how child-based factors such as initial language level, nonverbal cognition, and autism severity might influence parental input over time. Moreover, another way to investigate how parental language might be influenced by child factors is to see whether children’s measures at a given time (N) might predict parents’ language at a later time (N + 1), controlling for parents’ language at time (N). A small number of studies have done so, with (again) mixed results: For example, Huttenlocher et al. (2010) found significant relationships between two-year-old TD children’s lexical diversity and their caregivers’ lexical diversity four months later; however, for measures of grammatical complexity and diversity, only caregivers’ speech positively predicted their children’s subsequent speech (and not vice versa). Venker et al. (2015) similarly reported that caregivers’ speech positively predicted the child’s future linguistic production, but not vice versa. In contrast, Song et al. (2014) found a significant relationship between TD children’s nonverbal cognitive levels and their parents’ lexical diversity in speech one year later. Furthermore, in an intriguing fine-grained analysis of daylong recordings in age-matched children with ASD and TD children (age: 8–48 months), Warlaumont et al. (2014) investigated the temporal contingencies of parent and child vocalizations within the same conversations. They found that child vocalizations that were speech-like were more likely to elicit immediately contingent responses from parents. This relationship was observed for both TD children and children with ASD, although it was stronger for TD children, and for older children. Because prompt parent responses increased the likelihood of the next child vocalization to be speech-like, the authors suggest that parental adaptation to the child might underlie differential developmental trajectories, a claim supported by the results of a computer simulation of longitudinal effects of this feedback loop. The results were then replicated on a second sample (Harbison et al., 2018). In the current study, we extend these findings by investigating reciprocal effects of child language on parent language using the lexical and grammatical measures of word types and tokens, and MLU, and compare these in dyads involving TD children vs. children with ASD. Future work will further focus on the issue of temporal responsiveness (Weed et al., 2017).

One of the challenges in studying language development is that it is a moving target. Children mature at different rates, and their linguistic ability develops in fits and starts. The language profiles of some children with ASD may look radically different at different points in development, while others may show very little change (Ellis Weismer and Kover, 2015, Frith and Happé, 1994, Pickles et al., 2014). More recent research has shown that the period between 2 and 5 years of age presents the highest variability in language development, while children above age 6 have quite stable developmental trajectories (Pickles et al., 2014). A few studies also find relations between cognitive abilities and severity of clinical features at age 2 and language development (e.g. Ellis Weismer and Kover, 2015, Paul et al., 2008). It is therefore necessary to move beyond static, matched-group comparisons, and adopt analytical methods that account for individual variation in starting conditions and development among participants, as well as the relation between the two (Hayiou-Thomas et al., 2006, Jarrold and Brock, 2004). Multi-level modelling techniques offer tools to do this, in a fashion that is robust to missing and unbalanced data (Gelman and Hill, 2007, McElreath, 2015). Although these techniques have not yet been widely applied in the field of child language development, with the notable exception of Rowe, Raudenbush, and Goldin-Meadow (2012), they are becoming common in other fields such as education (Kiwanuka et al., 2017, Oldfield et al., 2017) and cognitive science (Mirman, 2016).

The first two major goals of the current study were to investigate and compare the roles of child-based factors such as nonverbal IQ and autistic symptomatology, and concurrent environmental factors such as parent word use and MLU, on the language growth of TD children and children with ASD who were not different in language level at study onset. The third major goal was to investigate the question of reciprocal influence; that is, how the children’s language use, clinical and cognitive characteristics might influence parental language concurrently and longitudinally. The literature reviewed above demonstrates that parents adapt to their children’s language. Therefore, to adequately investigate the role of cognitive and clinical features of children with ASD and TD children in the parent-child reciprocal influence, language abilities should be controlled for. We accordingly chose to rely on actual language production in a naturalistic context: a longitudinal corpus of free-play interactions between children with ASD and their parents, and an initially language-matched group of TD children between the ages of 2 and 5. Language production in both children and parents was characterized in terms of number of word tokens, word types, and MLU. Note that a subset of the children (17 ASD out of 32 and 18 TD out of 35) were included in a previous study (Tek et al., 2014). In addition to employing a larger sample of children and in accordance with the points highlighted in the previous paragraphs, the current analysis differs from the previous study in modelling the full range of linguistic, cognitive and clinical features instead of focusing on low vs. high verbal skill. This allows us to better account for individual differences in language development.

In particular, we addressed six research questions.

• Longitudinal trajectories of language development – child-based factors: We first characterized the children’s developmental trajectories on our three language measures across a 2-year span, addressing the question: To what extent is this development related to child-based factors of initial language level, nonverbal cognition, and autism symptomatology?

• Longitudinal trajectories of parental language production – child-based factors: We also characterized the linguistic profiles of the parents over the same time interval and asked: To what extent are the changes observed in the parents’ language use influenced by the same child factors?

• Parent-child matching in concurrent linguistic production: We then analyzed matching within the parent-child dyads. Matching is defined as the tendency of parent-child dyads to present similar levels of linguistic performance, that is, if during a visit the parent displays higher than average linguistic performance, we can expect a similar higher than average performance in the child. We asked: To what degree do parents' and children's productions match each other within the context of a single conversation, and how is this matching influenced by the same child factors?

• Predicting child linguistic development from environmental factors: We next investigated the role of cross-lagged environmental factors in children’s language development, asking to what degree do current measures of parental speech predict the measures of children's speech during the next visit 4 months later?

• Assessing the relative roles of child-based and environmental factors in child linguistic development: We compared the relative variance of child-based and environmental factors, asking: when both child-based and environmental factors are included in the same model, how much variance in child language outcomes is accounted for by each?

• Predicting parental linguistic production from child linguistic production: Finally, to address the question of reciprocal influence, we asked to what degree do the measures of child speech predict parents’ speech at a later point?