Individuals with ASD show atypical processing of social and nonsocial rewards. Findings support a broader interpretation of the social motivation hypothesis of ASD whereby general atypical reward processing encompasses social reward, nonsocial reward, and perhaps restricted interests. This meta-analysis also suggests that prior mixed results could be driven by sample age differences, warranting further study of the developmental trajectory for reward processing in ASD.

The INTERSPEECH 2019 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Styrian Dialects Sub-Challenge, three types of Austrian-German dialects have to be classified; in the Continuous Sleepiness Sub-Challenge, the sleepiness of a speaker has to be assessed as regression problem; in the Baby Sound Sub-Challenge, five types of infant sounds have to be classified; and in the Orca Activity Sub-Challenge, orca sounds have to be detected. We describe the Sub-Challenges and baseline feature extraction and classifiers, which include data-learnt (supervised) feature representations by the 'usual' ComParE and BoAW features, and deep unsupervised representation learning using the AUDEEP toolkit.

Background Narrative abilities are linked to social impairment in autism spectrum disorder (ASD), such that reductions in words about cognitive processes (e.g., think , know ) are thought to reflect underlying deficits in social cognition, including Theory of Mind. However, research suggests that typically developing (TD) boys and girls tell narratives in sex-specific ways, including differential reliance on cognitive process words. Given that most studies of narration in ASD have been conducted in predominantly male samples, it is possible that prior results showing reduced cognitive processing language in ASD may not generalize to autistic girls. To answer this question, we measured the relative frequency of two kinds of words in stories told by autistic girls and boys: nouns (words that indicate object-oriented storytelling) and cognitive process words (words like think and know that indicate mentalizing or attention to other peoples’ internal states). Methods One hundred two verbally fluent school-aged children [girls with ASD ( N = 21) and TD ( N = 19), and boys with ASD ( N = 41) and TD ( N = 21)] were matched on age, IQ, and maternal education. Children told a story from a sequence of pictures, and word frequencies (nouns, cognitive process words) were compared. Results Autistic children of both sexes consistently produced a greater number of nouns than TD controls, indicating object-focused storytelling. There were no sex differences in cognitive process word use in the TD group, but autistic girls produced significantly more cognitive process words than autistic boys, despite comparable autism symptom severity. Thus, autistic girls showed a unique narrative profile that overlapped with autistic boys and typical girls/boys. Noun use correlated significantly with parent reports of social symptom severity in all groups, but cognitive process word use correlated with social ability in boys only. Conclusion This study extends prior research on autistic children’s storytelling by measuring sex differences in the narratives of a relatively large, well-matched sample of children with and without ASD. Importantly, prior research showing that autistic children use fewer cognitive process words is true for boys only, while object-focused language is a sex-neutral linguistic marker of ASD. These findings suggest that sex-sensitive screening and diagnostic methods—preferably using objective metrics like natural language processing—may be helpful for identifying autistic girls, and could guide the development of future personalized treatment strategies.

Background: Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition. The degree to which the brain development in ASD deviates from typical brain development, and how this deviation relates to observed behavioral outcomes at the individual level are not well-studied. We hypothesize that the degree of deviation from typical brain development of an individual with ASD would relate to observed symptom severity. Methods: The developmental changes in anatomical (cortical thickness, surface area, and volume) and diffusion metrics (fractional anisotropy and apparent diffusion coefficient) were compared between a sample of ASD (n = 247) and typically developing children (TDC) (n = 220) aged 6-25. Machine learning was used to predict age (brain age) from these metrics in the TDC sample, to define a normative model of brain development. This model was then used to compute brain age in the ASD sample. The difference between chronological age and brain age was considered a developmental deviation index (DDI), which was then correlated with ASD symptom severity.Results: Machine learning model trained on all five metrics accurately predicted age in the TDC (r = 0.88) and the ASD (r = 0.85) samples, with dominant contributions to the model from the diffusion metrics. Within the ASD group, the DDI derived from fractional anisotropy was correlated with ASD symptom severity (r = − 0.2), such that individuals with the most advanced brain age showing the lowest severity, and individuals with the most delayed brain age showing the highest severity. Limitations: This work investigated only linear relationships between five specific brain metrics and only one measure of ASD symptom severity in a limited age range. Reported effect sizes are moderate. Further work is needed to investigate developmental differences in other age ranges, other aspects of behavior, other neurobiological measures, and in an independent sample before results can be clinically applicable. Conclusions: Findings demonstrate that the degree of deviation from typical brain development relates to ASD symptom severity, partially accounting for the observed heterogeneity in ASD. Our approach enables characterization of each individual with reference to normative brain development and identification of distinct developmental subtypes, facilitating a better understanding of developmental heterogeneity in ASD.

Background Girls with autism spectrum condition (ASC) are chronically underdiagnosed compared to boys, which may be due to poorly understood sex differences in a variety of domains, including social interest and motivation. In this study, we use natural language processing to identify objective markers of social phenotype that are easily obtained from a brief conversation with a nonexpert. Methods 87 school‐aged children and adolescents with ASC (17 girls, 33 boys) or typical development (TD; 15 girls, 22 boys) were matched on age (mean = 11.35 years), IQ estimates (mean = 107), and – for ASC participants – level of social impairment. Participants engaged in an informal 5‐min ‘get to know you’ conversation with a nonexpert conversation partner. To measure attention to social groups, we analyzed first‐person plural pronoun variants (e.g., ‘we’ and ‘us’) and third‐person plural pronoun variants (e.g., ‘they’ and ‘them’). Results Consistent with prior research suggesting greater social motivation in autistic girls, autistic girls talked more about social groups than did ASC boys. Compared to TD girls, autistic girls demonstrated atypically heightened discussion of groups they were not a part of (‘they’, ‘them’), indicating potential awareness of social exclusion. Pronoun use predicted individual differences in the social phenotypes of autistic girls. Conclusions Relatively heightened but atypical social group focus is evident in autistic girls during spontaneous conversation, which contrasts with patterns observed in autistic boys and TD girls. Quantifying subtle linguistic differences in verbally fluent autistic girls is an important step toward improved identification and support for this understudied sector of the autism spectrum.

This study evaluates whether early vocalizations develop in similar ways in children across diverse cultural contexts. We analyze data from daylong audio-recordings of 49 children (1-36 months) from five different language/cultural backgrounds. Citizen scientists annotated these recordings to determine if child vocalizations contained canonical transitions or not (e.g., "ba" versus "ee").Results revealed that the proportion of clips reported to contain canonical transitions increased with age. Further, this proportion exceeded 0.15 by around 7 months, replicating and extending previous findings on canonical vocalization development but using data from the natural environments of a culturally and linguistically diverse sample. This work explores how crowdsourcing can be used to annotate corpora, helping establish developmental milestones relevant to multiple languages and cultures. Lower inter-annotator reliability on the crowdsourcing platform, relative to more traditional in-lab expert annotators, means that a larger number of unique annotators and/or annotations are required and that crowdsourcing may not be a suitable method for more fine-grained annotation decisions. Audio clips used for this project are compiled into a large-scale infant vocal corpus that is available for other researchers to use in future work.

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This study evaluates whether babbling emerges similarly in children across diverse cultural contexts. We analyze data from daylong audio-recordings of 52 children (1-36 months) from six different language/cultural backgrounds. Citizen scientists annotated these recordings to determine if child vocalizations were canonical or not (e.g., "ba" versus "ee"). Results revealed that canonical babble increased with age. Further, a 0.15 canonical babble ratio emerged around 7 months, replicating and extending previous findings with data from the natural environments of a culturally and linguistically diverse sample. This work exemplifies how crowdsourcing can be used to annotate corpora, helping establish developmental milestones relevant to multiple languages and cultures. Audio clips used for this project are compiled into a large-scale infant babble corpus that is available for other researchers to use in future work.