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Journal of Speech, Language, and Hearing Research : JSLHR logoLink to Journal of Speech, Language, and Hearing Research : JSLHR
. 2022 Oct 19;65(11):4300–4305. doi: 10.1044/2022_JSLHR-22-00314

Lexical Alignment and Communicative Success in Autism Spectrum Disorder

Mackenzie Stabile a, Inge-Marie Eigsti a,✉
PMCID: PMC9940884  PMID: 36260779

Abstract

Purpose:

Typical speakers tend to adopt words used by their conversational partners. This “lexical alignment” enhances communication by reducing ambiguity and promoting a shared understanding of the topic under discussion. Lexical alignment has been little studied to date in autism spectrum disorder (ASD); furthermore, it has been studied primarily via structured laboratory tasks that may overestimate performance. This study examined lexical alignment in ASD during discourse and explored associations with communicative success and executive function.

Method:

Thirty-one autistic and nonautistic adolescents were paired with a study-naïve research assistant (RA) to complete a social communication task that involved taking turns verbally instructing (guiding) the partner to navigate on a map. Lexical alignment was operationalized as the proportion of shared vocabulary produced by guides on successive maps. Task accuracy was operationalized as the pixels contained within the intended and drawn routes.

Results:

Results indicated that autistic adolescents had greater difficulty describing navigational routes to RAs, yielding paths that were less accurate. Alignment was reduced in autistic participants, and it was associated with path accuracy for nonautistic, but not autistic, adolescents. The association between lexical alignment and executive function missed significance (p = .05); if significant, the association would indicate that greater executive function difficulty was associated with reduced lexical alignment.

Conclusions:

These findings provide preliminary evidence of reduced lexical alignment in ASD in an unstructured discourse context. Moreover, positive associations between lexical alignment and task performance in the neurotypical group raise the possibility that interventions to promote the use of shared vocabulary might support better communication.

Supplemental Material:

https://doi.org/10.23641/asha.21313719

Autism spectrum disorder (ASD) is characterized by social communication impairments and restricted, repetitive behaviors and interests (American Psychiatric Association, 2013). Language abilities are highly variable in ASD, but pragmatic language difficulties are nearly ubiquitous, including differences in turn-taking; use of nonliteral language; spontaneous bids for communication (Tager-Flusberg, 2001); relevance (as in Grice's maxims; Grice, 1975); providing the appropriate amount of information in conversation (Volden, 2002); responding appropriately to questions or comments (Capps et al., 1998); and aspects of discourse comprehension, such as the use of prosody to disambiguate syntax (Diehl et al., 2008, 2015), incorporating shared knowledge (e.g., “common ground”) into discourse (Schuh et al., 2016), and adjusting speech in light of shared knowledge (De Marchena & Eigsti, 2016). Pragmatic language challenges have far-reaching consequences for autistic individuals, and they have been associated with poor social adjustment and peer difficulties (Coplan & Weeks, 2009), as well as occupational difficulty (Whitehouse et al., 2009). This research note examines a less-familiar pragmatic process, lexical alignment, in autistic adolescents and its associations with task performance and executive function.

Lexical alignment describes the tendency for interlocutors to use the same words in dialogue. Theoretical accounts (e.g., the Interactive Alignment Model; Pickering & Garrod, 2004) argue that lexical alignment enhances communication by promoting a shared understanding between speakers; studies of lexical alignment in nonautistic (neurotypical; NT) individuals support this claim (Fusaroli et al., 2012; Fusaroli & Tylén, 2016; Reitter & Moore, 2014). Prior studies of lexical alignment in ASD used structured picture-matching paradigms, in which responses are constrained to a small, circumscribed set of options. This work suggests no ASD-specific differences in lexical alignment (Branigan et al., 2016; Hopkins et al., 2017; Slocombe et al., 2013). However, one study examined naturalistic dialogue and found reduced alignment in ASD (Fusaroli et al., 2019). This study seeks to broaden our understanding of lexical alignment in verbal individuals with ASD during naturalistic dialogue in the context of a social communication task.

Method

Participants included autistic (n = 16) and NT (n = 15) adolescents ages 12–18 years. All participants used English as a primary language and had abbreviated full-scale IQ scores greater than 85. Exclusion criteria included a history of significant neurological impairment and, in the NT group, first-degree relatives with ASD. Groups did not differ on chronological age, gender, or nonverbal IQ (see Table 1). Verbal IQ scores were in the average range, indicating that autistic participants all had age-appropriate general verbal abilities.

Table 1.

Demographic information for participants with autism spectrum disorder (ASD) and neurotypical (NT) development.

Variable ASD NT χ2 or F p Cohen's d
N (M:F) 14 (10:4) 13 (8:5) 0.28 .60
Age (years) 15.8 (2.0); 13–19 14.6 (1.9); 12–17 2.19 .15 0.414
Nonverbal IQ 11.4 (2.0); 8–15 11.2 (2.2); 8–15 0.12 .73 0.095
Verbal IQ 10.0 (2.6); 5–12 12.8 (2.1); 9–16 9.21 .006 1.184
ADOS CSS 6.1 (1.2); 4–8 --
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Note. Data are shown as M (SD); range. M = male; F = female; Nonverbal and verbal IQ = Stanford-Binet; ADOS = Autism Diagnostic Observation Schedule; CSS = Calibrated Symptom Severity.

Participants came to the laboratory for a total of 3–4 hr across one to two sessions. Procedures were approved by the institutional review board at the University of Connecticut. Some video data were lost due to file corruption or technical difficulties. Individuals with data for at least four trials were included, yielding a final sample of 13 NT participants and 14 participants with ASD.

Measures

The autistic participants completed the Autism Diagnostic Observation Schedule (ADOS-2; Lord et al., 2012) to confirm ASD diagnostic status; the parent-reported Social Communication Questionnaire (Rutter et al., 2003) confirmed non-ASD status in the comparison group. Cognitive ability was assessed using the Stanford-Binet Intelligence Scales-5 (SB-5; Roid, 2003). Executive functions were assessed using the parent-report Behavior Rating Inventory of Executive Function-2 (BRIEF; Gioia et al., 2013). The primary measure of discourse functioning was the Edinburgh Maps Task (Anderson et al., 1991). Participants were paired with an undergraduate research assistant (RA) of the same gender, naïve to diagnostic group and study predictions; RAs generally participated only once, though two RAs completed the task twice and one RA participated 3 times. Dyads were seated across from one another, with a cardboard barrier to prevent the exchange of visual information. Partners were asked to navigate a series of six maps, alternating between “Tourist” and “Guide” roles. Guide maps contained a route drawn from a starting point through a series of landmarks to a finish point; the corresponding Tourist map contained similar landmarks without the route. Each pair of maps included three to six nonshared landmarks. The Guide described the route to the Tourist, who attempted to draw the identical route on the Tourist's map. The participant's first role (Guide or Tourist) was counterbalanced across groups.

Procedure

Maps Task Performance Metrics

Trials were transcribed by three undergraduate RAs and the first author using Computerized Language Analysis (CLAN; MacWhinney, 2000). One trial each for 20% of participants was double-transcribed to establish interrater reliability. Word-for-word agreement was calculated at ICC = 92%. A list of unique words (types) and tokens (total number of productions of any unique word) for each trial was calculated using CLAN's freq command, excluding self-corrections and repetitions. Lexical alignment was operationalized as the proportion of shared vocabulary between Guides on successive maps, in effect treating each trial as a discourse turn. For each trial, an alignment value ranging from 0 to 1 was generated for each word as follows (drawing on prior approaches, e.g., Fernández & Grimm, 2014): If the frequency with which the Guide produced a word on Turn n + 1 was greater than or equal to the frequency with which the word was produced by the Guide on Turn n, that word's alignment score was 1. If the word was produced fewer times on Turn n + 1, the word's alignment score was: Turn n + 1 frequency / Trial n frequency. If the word was not produced on Trial n + 1, alignment score was 0. Average alignment across trials was calculated for each partner. Because partners alternated in the Guide role on each trial, this process effectively captured the degree to which words used by one partner were adopted by the other on subsequent turns. To measure path accuracy or the estimated deviation between the target route and the route drawn by the Tourist, Guide and Tourist routes were traced onto paper and scanned in Adobe Photoshop to calculate the differentiation of the two routes. Path accuracy was calculated as pixel count divided by 10,000.

Results

Statistical Approach

Statistical analyses were conducted using SPSS. Dependent variables were examined for deviations from normality (Shapiro–Wilk). One dependent variable (path accuracy) deviated from normality due to an outlier that was over 3 SDs above the mean. This data point was included as is in a mixed-effects model that did not require normal distribution. For other analyses, the outlier was replaced with the value of the 95th percentile of the data (e.g., winsorization). A linear mixed-effects model with unstructured covariance structure examined differences in path accuracy as a function of Role (Guide and Tourist) and Group (ASD and NT). Role was a repeated-measures variable, and Group was a between-subjects variable. A one-way analysis of variance (ANOVA) examined group differences in lexical alignment. Effect size was calculated using Cohen's d for ANOVAs and Hedges's g for the linear mixed-effects model. 1 Parametric or nonparametric two-tailed correlations tested relationships among variables. While performance of the RA was of potential interest, this research note focuses on participant variables.

Lexical Alignment

A one-way ANOVA indicated a group difference in average alignment with a large effect size, F(1, 26) = 4.41, p = .046, d = 0.75, with autistic adolescents engaging in less alignment than NT adolescents (ASD: M [SD] = .29 [.05]; NT: M [SD] = .34 [.08]). A bivariate correlation analysis between participant lexical alignment and BRIEF Global Executive Composite scores missed significance, r(25) = −.38, p = .05; if significant, that relationship would indicate that participants with greater executive function difficulties engaged in less lexical alignment.

Path Accuracy

A linear mixed-effects model examined differences in path accuracy (i.e., target vs. actual routes) as a function of participant Role (Guide and Tourist) and Group (ASD and NT). Results indicated a main effect of Group with a large effect size, F(1, 27) = 8.61, p = .007, g = .924, with lower accuracy in ASD/RA pairs compared to NT/RA pairs (ASD: M [SD] = 109 [34]; NT: M [SD] = 83 [21]). There was also a main effect of participant Role with a medium effect size, F(1, 27) = 7.60, p = .01, g = .486, with lower accuracy on trials where the participant was Guide and the RA drew the route, compared to trials where the participant was Tourist and drew the route (participant as Guide: M (SD) = 103 [31]; participant as Tourist: M (SD) = 89 [29]). This likely suggests that the route descriptions provided by the RAs were more effective for drawing accurate routes, though we cannot rule out the possibility that participants were better than college students at drawing routes. There was no significant interaction of Group and Role, F(1, 27) = 0.47, p = .50.

Associations Between Lexical Alignment and Path Accuracy

Bivariate correlation analyses examined associations between lexical alignment and path accuracy. When the participant was Guide, participant lexical alignment was positively associated with RA path accuracy, r(26) = .604, p = .001, suggesting that the participant's convergence on a shared set of lexical items enhanced the accuracy of route drawing by RAs (see Figure 1). When this correlation was examined within groups, participant alignment was associated with RA path accuracy for NT, r(13) = −.67, p = .01, but not for ASD, r(13) = −.30, p = .31, pairs. 2

Figure 1.

Figure 1.

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Association between participant lexical alignment and research assistant (RA) path accuracy.

Discussion

This study was designed to measure the degree to which autistic teens align to conversational partners during discourse, the correlates of lexical alignment, and the impact of alignment on communication. Fifteen autistic and 15 NT adolescents were paired with undergraduate RAs to complete a social communication task that entailed collaborating to draw routes on a series of maps. The participant and RA took turns acting as Guides to instruct the partner (the Tourist) to navigate a series of landmarks to draw a path on the Tourist map similar to the original path shown on the Guide's map. Lexical alignment between Tourists and Guides was operationalized as the proportion of shared vocabulary produced by Guides on successive maps. Path accuracy, or the degree to which the Tourist and Guide maps corresponded, was operationalized as the number of pixels contained within the space between the Guide map and the route drawn by the Tourist. Associations between lexical alignment, path accuracy, and executive function were explored to better understand the mechanisms that give rise to lexical alignment. We reported primary analyses without verbal IQ as a covariate (see Supplemental Material S1 for analyses with this covariate included) to address the role of language ability in lexical alignment and task performance. Given theoretical arguments against the use of IQ as a covariate in studies of individuals with developmental differences (Dennis et al., 2009; Kover & Atwood, 2013), we limit our discussion to results of the models without verbal IQ as a covariate. Results indicated reduced alignment and path accuracy in autistic teens relative to nonautistic peers. Moreover, path accuracy was lower on trials where participants acted as Guides, suggesting that the adolescents had greater difficulty providing effective or informative directions. Finally, lexical alignment and path accuracy were associated in the NT dyads, such that greater alignment was associated with more accurate routes; this association was not found in the ASD dyads.

In contrast to previous studies of lexical alignment in ASD, this study found reduced lexical alignment among autistic teens relative to NT peers. This discrepancy may be explained by methodological differences; most prior studies utilized structured card-matching tasks, whereas this study examined lexical alignment in a less structured context. It is possible that autistic individuals are more sensitive to interlocutors' word choices when task demands make word choice highly salient (e.g., in a picture naming/matching task) but less so when a task is less obviously contingent upon specific word choices (as in this study). An additional consideration is differences in lexical alignment calculation. Some work examines alignment using natural language processing models that measure the ratio of shared words with a previous utterance to the total words present in the utterance (Fernández & Grimm, 2014). This study necessitated a unique approach, as each trial yielded lengthy turns from Guides with minimal speech from Tourists. Instead, each trial served as a turn, and alignment between turns provided the primary measure. Further study of alignment in ASD during naturalistic conversation using existing NLP tools would illuminate the finding of reduced alignment in ASD.

Analyses examining path accuracy indicated reduced accuracy for ASD/RA pairs relative to NT/RA pairs. Paths were also less accurate when the participant was the Guide, relative to when the RA was the Guide, suggesting that RAs were more successful at communicating navigational routes. Together, these findings suggest that diagnostic group differences were driven by trials in which participants were Guides and that reduced path accuracy of ASD/RA pairs is related to less effective instructions by autistic participants, rather than RAs. While differences in motor control (fine motor development is known to be disrupted in ASD; Choi et al., 2018) could also account for the main effect of group, this is unlikely, considering that group differences were apparent on trials where the RA was drawing the route. Another possible interpretation is that these effects are a result of a “mismatch” in communicative styles between ASD/NT dyads and that dyads containing ASD/ASD pairs might be more successful (Crompton et al., 2020). This is a possibility requiring further research.

Another aim was to examine the effects of alignment on communicative success, operationalized here as path accuracy. Consistent with theories of discourse that emphasize the importance of alignment in communication (e.g., Pickering & Garrod, 2004), participant lexical alignment was associated with greater accuracy in paths drawn by the RAs, although this association was only significant in the NT group. The fact that alignment was not associated with RA path accuracy in the ASD group may reflect the limited range of alignment in this group (.23–.41 in the ASD group vs. .17–.50 in the NT group). It is also possible that the potential benefits of alignment for communication were undermined by other pragmatic challenges, such as insufficiently detailed route descriptions. This would be consistent with past work suggesting that autistic individuals have difficulty providing the appropriate amount of information in conversation (Volden, 2002). Nevertheless, the fact that lexical alignment was associated with greater accuracy for the NT group suggests that explicit instruction to adopt conversational partners' word choice during conversation might contribute to better communicative success; this kind of strategy could be incorporated into social skills interventions.

Limitations

There are several limitations of this study. First, the labor-intensive nature of this task and the subsequent transcription and coding limited the sample size; the current findings must be replicated. Our approach to lexical alignment also differed from measures used in previous work. The current findings, which are in conflict with some prior results using more structured tasks but replicate studies of spontaneous discourse, emphasize the importance of examining alignment processes across multiple contexts, with careful consideration of task demands and methods for calculating alignment. Results contribute to our understanding of lexical alignment in ASD; this is an understudied phenomenon and would benefit from additional research to determine situational constraints on alignment.

Future Directions

Linguistic alignment has been shown to enhance communication and result in feelings of affiliation between conversational partners; however, few studies have explored alignment in ASD. Results of this study suggest that autistic individuals may engage in less lexical alignment than NT peers in a social communication context. The study of lexical alignment in ASD to date has primarily relied upon card-matching tasks that may overestimate performance given task constraints. Additional studies exploring alignment in more naturalistic conversational settings would help reconcile the current work with previous studies that report comparable rates of alignment in autistic individuals. Future work might also benefit from inclusion of pairs matched for neurotypes and age to address the possibility that reduced alignment in this study resulted from a mismatch in communication styles. Finally, to minimize the potential influence of methodological variability in interpreting findings across studies, another informative direction would be to utilize existing Natural Language Processing tools (e.g., ALIGN; Duran et al., 2019) to examine alignment in discourse. Such measures enable one to examine trends in alignment over the course of an interaction, in addition to quantifying alignment at the lexical, syntactic, and semantic levels.

Data Availability Statement

The data set from this study is available from the corresponding author upon request.

Supplementary Material

Supplemental Material S1. Results with verbal IQ as a covariate.
Click here for additional data file. (245.2KB, pdf)

Acknowledgments

The authors gratefully acknowledge funding from the National Institutes of Health (R01MH112687-01A1) awarded to I. M. Eigsti and D. Fein. The authors would like to thank the participants and their families for their time and energy.

Funding Statement

The authors gratefully acknowledge funding from the National Institutes of Health (R01MH112687-01A1) awarded to I. M. Eigsti and D. Fein.

Footnotes

1

In comparisons of neurodiverse and neurotypical groups, covariates should, in theory, not be an outcome of dependent or independent variables; when group assignment is not random (e.g., if it is determined by virtue of diagnostic status), inclusion of a covariate that systematically differs between two groups diminishes meaningful variance (see the works of Dennis et al., 2009, and Kover & Atwood, 2013, for discussion). In this study, lexical alignment and path accuracy are expected to reflect exactly the same linguistic processes captured by standardized assessments of language ability (e.g., verbal IQ [VIQ]). To retain the power to detect likely subtle effects and to avoid removing meaningful variance from the contrast of interest, analyses are reported without VIQ as a covariate. Results with this covariate are reported in Supplemental Material S1.

2

When the participant was the Tourist (and, therefore, drawing the routes), RA lexical alignment was uncorrelated with participant path accuracy, r(27) = .08, p = .70.

References

  1. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.) . American Psychiatric Publishing. https://doi.org/10.1176/appi.books.9780890425596 [Google Scholar]
  2. Anderson, A. H. , Bader, M. , Bard, E. G. , Boyle, E. A. , Doherty, G. , Garrod, S. , Isard, S. , Kowtko, J. , McAllister, J. , Miller, J. , Sotillo, C. , Thompson, H. S. , & Weinert, R. (1991). The HCRC map task corpus. Language and Speech, 34(4), 351–366. https://doi.org/10.1177/002383099103400404 [Google Scholar]
  3. Branigan, H. P. , Tosi, A. , & Gillespie-Smith, K. (2016). Spontaneous lexical alignment in children with an autistic spectrum disorder and their typically developing peers. Journal of Experimental Psychology: Learning Memory and Cognition, 42(11), 1821–1831. https://doi.org/10.1037/xlm0000272 [DOI] [PubMed] [Google Scholar]
  4. Capps, L. , Kehres, J. , & Sigman, M. (1998). Conversational abilities among children with autism and children with developmental delays. Autism, 2(4), 325–344. https://doi.org/10.1177/1362361398024002 [Google Scholar]
  5. Choi, B. , Leech, K. A. , Tager-Flusberg, H. , & Nelson, C. A. (2018). Development of fine motor skills is associated with expressive language outcomes in infants at high and low risk for autism spectrum disorder. Journal of Neurodevelopmental Disorders, 10(1), 14. https://doi.org/10.1186/S11689-018-9231-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Coplan, R. J. , & Weeks, M. (2009). Shy and soft-spoken: Shyness, pragmatic language, and socio-emotional adjustment in early childhood. Infant and Child Development, 18(3), 238–254. https://doi.org/10.1002/icd.622 [Google Scholar]
  7. Crompton, C. J. , Ropar, D. , Evans-Williams, C. V. M. , Flynn, E. G. , & Fletcher-Watson, S. (2020). Autistic peer-to-peer information transfer is highly effective. Autism, 24(7), 1704–1712. https://doi.org/10.1177/1362361320919286 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. De Marchena, A. , & Eigsti, I. M. (2016). The art of common ground: Emergence of a complex pragmatic language skill in adolescents with autism spectrum disorders. Journal of Child Language, 43(1), 43–80. https://doi.org/10.1017/S0305000915000070 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Dennis, M. , Francis, D. J. , Cirino, P. T. , Schachar, R. , Barnes, M. A. , & Fletcher, J. M. (2009). Why IQ is not a covariate in cognitive studies of neurodevelopmental disorders. Journal of the International Neuropsychological Society, 15(3), 331–343. https://doi.org/10.1017/S1355617709090481 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Diehl, J. , Bennetto, L. , Watson, D. , Gunlogson, C. , & McDonough, J. (2008). Resolving ambiguity: A psycholinguistic approach to understanding prosody processing in high-functioning autism. Brain and Language, 106(2), 144–152. https://doi.org/10.1016/J.BANDL.2008.04.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Diehl, J. , Friedberg, C. , Paul, R. , & Snedeker, J. (2015). The use of prosody during syntactic processing in children and adolescents with autism spectrum disorders. Development and Psychopathology, 27(3), 867–884. https://doi.org/10.1017/S0954579414000741 [DOI] [PubMed] [Google Scholar]
  12. Duran, N. D. , Paxton, A. , & Fusaroli, R. (2019). ALIGN: Analyzing linguistic interactions with generalizable techNiques—A Python library. Psychological Methods, 24(4), 419–438. https://doi.org/10.1037/met0000206 [DOI] [PubMed] [Google Scholar]
  13. Fernández, R. , & Grimm, R. M. (2014). Quantifying categorical and conceptual convergence in child–adult dialogue. Proceedings of the Annual Meeting of the Cognitive Science Society . [Google Scholar]
  14. Fusaroli, R. , Bahrami, B. , Olsen, K. , Roepstorff, A. , Rees, G. , Frith, C. , & Tylén, K. (2012). Coming to terms. Psychological Science, 23(8), 931–939. https://doi.org/10.1177/0956797612436816 [DOI] [PubMed] [Google Scholar]
  15. Fusaroli, R. , & Tylén, K. (2016). Investigating conversational dynamics: Interactive alignment, interpersonal synergy, and collective task performance. Cognitive Science, 40(1), 145–171. https://doi.org/10.1111/cogs.12251 [DOI] [PubMed] [Google Scholar]
  16. Fusaroli, R. , Weed, E. , Fein, D. A. , & Naigles, L. R. (2019). Hearing me hearing you: Reciprocal effects between child and parent language in autism and typical development. Cognition, 183, 1–18. https://doi.org/10.1016/J.COGNITION.2018.10.022 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Gioia, G. A. , Isquith, P. K. , Guy, S. C. , & Kenworthy, L. (2013). Behavior Rating Inventory of Executive Function (2nd ed.). Psychological Assessment Resources. [Google Scholar]
  18. Grice, H. P. (1975). Logic and conversation. Cambridge University Press. [Google Scholar]
  19. Hopkins, Z. , Yuill, N. , & Branigan, H. P. (2017). Inhibitory control and lexical alignment in children with an autism spectrum disorder. The Journal of Child Psychology and Psychiatry, 58(10), 1155–1165. https://doi.org/10.1111/jcpp.12792 [DOI] [PubMed] [Google Scholar]
  20. Kover, S. T. , & Atwood, A. K. (2013). Establishing equivalence: Methodological progress in group-matching design and analysis. American Journal on Intellectual and Developmental Disabilities, 118(1), 3–15. https://doi.org/10.1352/1944-7558-118.1.3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Lord, C. , Rutter, M. , Dilavore, P. , Risi, S. , Gotham, K. , & Bishop, S. (2012). Autism Diagnostic Observation Schedule–2nd Edition (ADOS-2). Western Psychological Corporation. [Google Scholar]
  22. MacWhinney, B. (2000). The CHILDES Project: Tools for analyzing talk (3rd ed.). Lawrence Erlbaum Associates. [Google Scholar]
  23. Pickering, M. J. , & Garrod, S. (2004). Toward a mechanistic psychology of dialogue. Behavioral and Brain Sciences, 27(02), 169–190. https://doi.org/10.1017/s0140525x04000056 [DOI] [PubMed] [Google Scholar]
  24. Reitter, D. , & Moore, J. D. (2014). Alignment and task success in spoken dialogue. Journal of Memory and Language, 76, 29–46. https://doi.org/10.1016/j.jml.2014.05.008 [Google Scholar]
  25. Roid, G. (2003). Stanford-Binet Intelligence Scales (5th ed.). Riverside Publishing. [Google Scholar]
  26. Rutter, M. , Bailey, A. , & Lord, C. (2003). The Social Communication Questionnaire (SCQ). Western Psychological Services. [Google Scholar]
  27. Schuh, J. M. , Eigsti, I.-M. , & Mirman, D. (2016). Discourse comprehension in autism spectrum disorder: Effects of working memory load and common ground. Autism Research, 9(12), 1340–1352. https://doi.org/10.1002/aur.1632 [DOI] [PubMed] [Google Scholar]
  28. Slocombe, K. E. , Alvarez, I. , Branigan, H. P. , Jellema, T. , Burnett, H. G. , Fischer, A. , Li, Y. H. , Garrod, S. , & Levita, L. (2013). Linguistic alignment in adults with and without Asperger's syndrome. Journal of Autism and Developmental Disorders, 43(6), 1423–1436. https://doi.org/10.1007/s10803-012-1698-2 [DOI] [PubMed] [Google Scholar]
  29. Tager-Flusberg, H. , Joseph, R. , & Folstein, S. (2001). Current directions in research on autism. Mental Retardation and Developmental Disabilities Research Reviews, 7(1), 21–29. https://doi.org/10.1002/1098-2779(200102)7:1<21::AID-MRDD1004>3.0.CO;2-3 [DOI] [PubMed] [Google Scholar]
  30. Volden, J. (2002). Features leading to judgements of inappropriacy in the language of speakers with autism: A preliminary study. Journal of Speech-Language Pathology and Audiology, 26(3), 138–146. [Google Scholar]
  31. Whitehouse, A. J. O. , Watt, H. J. , Lilne, E. A. , & Bishop, D. V. M. (2009). Adult psychosocial outcomes of children with specific language impairment, pragmatic language impairment and autism. International Journal of Language & Communication Disorders, 44(4), 511–528. https://doi.org/10.1080/13682820802708098 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Material S1. Results with verbal IQ as a covariate.
Click here for additional data file. (245.2KB, pdf)

Data Availability Statement

The data set from this study is available from the corresponding author upon request.

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