Does Gestural Communication Influence Later Spoken Language Ability in Minimally Verbal Autistic Children?

PURPOSE: The current study examined the predictive role of gestures and gesture-speech combinations on later spoken language outcomes in minimally verbal (MV) autistic children enrolled in a blended naturalistic developmental/behavioral intervention (Joint Attention, Symbolic Play, Engagement, and Regulation [JASPER] + Enhanced Milieu Teaching [EMT]). METHOD: Participants were 50 MV autistic children (40 boys), ages 54-105 months (M = 75.54, SD = 16.45). MV was defined as producing fewer than 20 spontaneous, unique, and socially communicative words. Autism symptom severity (Autism Diagnostic Observation Schedule-Second Edition) and nonverbal cognitive skills (Leiter-R Brief IQ) were assessed at entry. A natural language sample (NLS), a 20-min examiner-child interaction with specified toys, was collected at entry (Week 1) and exit (Week 18) from JASPER + EMT intervention. The NLS was coded for gestures (deictic, conventional, and representational) and gesture-speech combinations (reinforcing, disambiguating, supplementary, other) at entry and spoken language outcomes: speech quantity (rate of speech utterances) and speech quality (number of different words [NDW] and mean length of utterance in words [MLUw]) at exit using European Distributed Corpora Project Linguistic Annotator and Systematic Analysis of Language Transcripts. RESULTS: Controlling for nonverbal IQ and autism symptom severity at entry, rate of gesture-speech combinations (but not gestures alone) at entry was a significant predictor of rate of speech utterances and MLUw at exit. The rate of supplementary gesture-speech combinations, in particular, significantly predicted rate of speech utterances and NDW at exit. CONCLUSION: These findings highlight the critical importance of gestural communication, particularly gesture-speech (supplementary) combinations in supporting spoken language development in MV autistic children.

Are minimally verbal autistic children's modality and form of communication associated with parent responsivity?

Prior work examined how minimally verbal (MV) children with autism used their gestural communication during social interactions. However, interactions are exchanges between social partners. Examining parent-child social interactions is critically important given the influence of parent responsivity on children's communicative development. Specifically, parent responses that are semantically contingent to the child's communication plays an important role in further shaping children's language learning. This study examines whether MV autistic children's (N = 47; 48-95 months; 10 females) modality and form of communication are associated with parent responsivity during an in-home parent-child interaction (PCI). The PCI was collected using natural language sampling methods and coded for child modality and form of communication and parent responses. Findings from Kruskal-Wallis H tests revealed that there was no significant difference in parent semantically contingent responses based on child communication modality (spoken language, gesture, gesture-speech combinations, and AAC) and form of communication (precise vs. imprecise). Findings highlight the importance of examining multiple modalities and forms of communication in MV children with autism to obtain a more comprehensive understanding of their communication abilities; and underscore the inclusion of interactionist models of communication to examine children's input on parent responses in further shaping language learning experiences.

NFTracer: Tracing NFT Impact Dynamics in Transaction-flow Substitutive Systems with Visual Analytics.

Impact dynamics are crucial for estimating the growth patterns of NFT projects by tracking the diffusion and decay of their relative appeal among stakeholders. Machine learning methods for impact dynamics analysis are incomprehensible and rigid in terms of their interpretability and transparency, whilst stakeholders require interactive tools for informed decision-making. Nevertheless, developing such a tool is challenging due to the substantial, heterogeneous NFT transaction data and the requirements for flexible, customized interactions. To this end, we integrate intuitive visualizations to unveil the impact dynamics of NFT projects. We first conduct a formative study and summarize analysis criteria, including substitution mechanisms, impact attributes, and design requirements from stakeholders. Next, we propose the Minimal Substitution Model to simulate substitutive systems of NFT projects that can be feasibly represented as node-link graphs. Particularly, we utilize attribute-aware techniques to embed the project status and stakeholder behaviors in the layout design. Accordingly, we develop a multi-view visual analytics system, namely NFTracer, allowing interactive analysis of impact dynamics in NFT transactions. We demonstrate the informativeness, effectiveness, and usability of NFTracer by performing two case studies with domain experts and one user study with stakeholders. The studies suggest that NFT projects featuring a higher degree of similarity are more likely to substitute each other. The impact of NFT projects within substitutive systems is contingent upon the degree of stakeholders' influx and projects' freshness.

Real-time coded measures in natural language samples capture change over time in minimally verbal autistic children.

Prior research supports the use of natural language sampling (NLS) to assess the rate of speech utterances (URate) and the rate of conversational turns (CTRate) in minimally verbal (MV) autistic children. Bypassing time-consuming transcription, previous work demonstrated the ability to derive URate and CTRate using real-time coding methods and provided support for their strong psychometric properties. (1) Unexplored is how URate and CTRate using real-time coding methods capture change over time and (2) whether specific child factors predict changes in URate and CTRate in 50 MV autistic children (40 males; M = 75.54, SD = 16.45 (age in months)). A NLS was collected at Time 1 (T1) and Time 2 (T2) (4.5 months between T1 and T2) and coding was conducted in ELAN Linguistic Annotator software using a real-time coding approach to derive URate and CTRate. Findings from paired samples Wilcoxon tests revealed a significant increase in child URate (not examiner URate) and child and examiner CTRate from T1 to T2. Child chronological age, Mullen expressive language age equivalent scores, and URate and CTRate at T1 were predictive of URate and CTRate at T2. Findings support using NLS-derived real-time coded measures of URate and CTRate to efficiently capture change over time in MV autistic children. Identifying child factors that predict changes in URate and CTRate can help in the tailoring of goals to children's individual needs and strengths.

Studies assessing domains pertaining to structural language in autism vary in reporting practices and approaches to assessment: A systematic review.

LAY ABSTRACT: Under the Diagnostic and Statistical Manual of Mental Disorders (5th ed.), language impairment can co-occur with autism. It is not yet clear how research defines, reports, and characterizes structural language abilities of autistic individuals eligible for school-based special education services (aged 3-21 years) in the United States. In the United States, students typically must be formally diagnosed to be eligible for services and supports. However, the quality of diagnosis is only as good as the research evidence on which diagnosis depends. To evaluate evidence quality, we examined how studies of school-aged autistic individuals report assessments of language ability. This systematic review included 57 studies using English language age-referenced assessments used to measure structural language. Findings showed many differences across studies in how language abilities were measured and reported. Also, none of the studies fully reported the variables relevant to characterizing language impairment. Outcomes were similar across versions of the Diagnostic and Statistical Manual of Mental Disorders. Findings indicate that researchers and clinicians should pay attention to reporting diagnostic and grouping criteria. Carefully interpreting research evidence is critical for ensuring that diagnostic criteria and supports are representative of and accessible to autistic individuals and relevant parties.

Studies pertaining to language impairment in school-age autistic individuals underreport participant socio-demographics: A systematic review.

LAY ABSTRACT: Although exclusion of racially and ethnically minoritized autistic individuals from research is a long-standing issue, we have yet to determine how exclusion impacts areas of autism research important for identifying language impairment. Diagnosis depends on the quality of the evidence (i.e. research) and is often the pathway to gaining access to services. As a first step, we examined how research studies related to language impairment in school-age autistic individuals report participant socio-demographics. We analyzed reports using age-referenced assessments in English (n = 60), which are commonly used by both practitioners and researchers to diagnose or identify language impairment. Findings showed only 28% of studies reported any information on race and ethnicity; in these studies, most (at least 77%) of the participants were white. In addition, only 56% of studies reported gender or sex and specified what they were reporting (gender, sex, or gender identity). Just 17% reported socio-economic status using multiple indicators. Altogether, findings indicate broad issues with underreporting and exclusion of racially and ethnically minoritized individuals, which might overlay with other aspects of identity including socio-economic status. It is impossible to determine the extent and precise nature of exclusion without intersectional reporting. To ensure that language in autism research is representative of the autistic population, future research must implement reporting guidelines and broaden inclusion of who participates in research studies.

Developmental language disorder in Chinese children: A systematic review of research from 1997 to 2022.

Developmental language disorder (DLD) is one of the most common neurodevelopmental disorders. The influences of DLD on language development have been delineated in detail in English. The same is not true for Chinese, a group of Sinitic languages with distinct typological features that may modify the profile of DLD crosslinguistically. We conducted a systematic search of English and Chinese journal databases and reviewed 59 studies on the manifestations of DLD in Chinese. Methodological quality appraisal of the literature revealed several areas of improvement to enhance transparency and replicability. A bibliometric analysis indicated a steep growth trajectory of this literature. Examination of the participant selection and diagnostic criteria revealed limitations and calls for the development of assessment tools and increased knowledge of evidenced-based diagnostic practice. Areas of deficits demonstrated by Chinese children with DLD were synthesized qualitatively and discussed in light of the literature on clinical markers of DLD in English.

Lexical and Morphosyntactic Profiles of Autistic Youth With Minimal or Low Spoken Language Skills.

PURPOSE: Autistic youth who are minimally or low verbal are underrepresented in research leaving little to no evidence base for supporting them and their families. To date, few studies have examined the types of words and word combinations these individuals use. The purpose of this study was to take a strengths-based approach to outline descriptive profiles of autistic youth who use few words and elucidate the lexical and morphosyntactic features of their spoken language. METHOD: We analyzed language samples from 49 autistic youth ages 6-21 years who used fewer than 200 words. Systematic Analysis of Language Transcripts was used to investigate the relationship between number of different words (NDW) and proportion of nouns and verbs (vs. other word classes), mean length of utterance in morphemes (MLUm), and the frequency of early developing morphosyntactic structures. We used linear regression to quantify the relationship between NDW and lexical and morphosyntactic features. RESULTS: Proportion of nouns and verbs produced did not increase significantly in those with higher NDW. Conversely, MLUm and the frequency of early developing morphosyntactic structures increased significantly in those with higher NDW. CONCLUSIONS: Youth with higher NDW did not produce more nouns and verbs, suggesting lexical profiles that are not aligned with spoken vocabulary level. Youth with higher NDW had higher MLUm and more early morphosyntactic forms, suggesting that morphosyntactic profiles align with spoken vocabulary level. We discuss the implications for improving clinical services related to spoken language.

Ganong effects for frequency may not be robust.

The Ganong effect-more identifications of a certain phoneme in a context where that phoneme would yield a real word than a context where that phoneme would yield a pseudoword-has been widely replicated. Few studies, however, have tested whether this effect occurs for frequency contrasts. In the present study, participants' likelihood of identifying an ambiguous sound as aspirated was tested in acoustically identical continua in contexts where the identification of the sound as aspirated would either yield a lower- or higher-frequency word than the identification of the sound as unaspirated would. No frequency-based Ganong effect was found.