Re-Examining Labels in Neurocognitive Research: Evidence from Bilingualism and Autism as Spectrum-Trait Cases

Ανδρέου, Μαρία; Skrimpa, Vasileia

doi:10.3390/brainsci12081113

Cited by 3 publications

(3 citation statements)

References 71 publications

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“…The extent to which this is the case also for autistic children remains to be investigated. Such findings raise the issue of the great heterogeneity in ASD, emphasizing on the necessity of a more in‐depth investigation of individual profiling among autistic participants (see Silleresi et al, 2020 for similar results and Andreou & Skrimpa, 2022 for a relevant review).…”

Section: Figurative Language Processing In Asdmentioning

confidence: 93%

Figurative language processing in autism spectrum disorders: A review

Lampri,

Peristeri,

Marinis

et al. 2023

Autism Research

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Impairments in the broader domain of pragmatics are considered to be a defining feature of Autism Spectrum Disorders (ASD). A challenging aspect of pragmatic competence is the ability to process nonliteral language. Interestingly, previous studies in figurative language comprehension in ASD have demonstrated conflicting results regarding participants' performance. The main scientific debate focuses on the underlying skills which facilitate processing of nonliteral speech in ASD. Namely, Theory of Mind (ToM), language abilities and Executive functions (EFs) are regarded as factors affecting autistic individuals' performance. This review addresses figurative language comprehension in ASD in light of the above three interpretive accounts. We reviewed data from recent studies in this field concluding that autistic children indeed encounter systematic difficulties in the processing of non‐literal language. Moreover, only ToM and verbal skills were found to correlate the most with figurative language comprehension in ASD. Notably, we found that differences related to research methodology and tasks' properties may have led to discrepancies between studies' results. Finally, we argue that future studies should encompass in their experimental design figurative comprehension tasks with minimal linguistic demands and also measures of ToM, verbal ability and EFs in order to shed more light in the independent contribution of those skills to the processing of nonliteral language in ASD.

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Section: Figurative Language Processing In Asdmentioning

confidence: 93%

Figurative language processing in autism spectrum disorders: A review

Lampri,

Peristeri,

Marinis

et al. 2023

Autism Research

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“…This burdens language comprehension, verbal short-term and working memory, and further asks participants to formulate responses to fairly complex questions (e.g., When Sally returns, where will she look for her marble?). Difficulties to perform well in these tasks could be related to the participants' verbal rather than mentalizing ability or to the large heterogeneity that characterizes individuals with autism (e.g., Roberts et al, 2004;Andreou and Skrimpa, 2022).…”

Section: Low-verbal Tom Tasks In Autismmentioning

confidence: 99%

Development and validation of a task battery for verbal and non-verbal first- and second-order theory of mind

Marinis

Ανδρέου²,

Bagioka³

et al. 2023

Front. Lang. Sci.

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This paper presents a new toolkit for assessing Theory of Mind (ToM) via performance in first and second-order false belief (FB) tasks. The toolkit includes verbal and non-verbal versions of first and second-order FB tasks; the verbal version is currently available in Greek and German. Scenarios in the toolkit are balanced for factors that may influence performance, like the reason for the FB (deception, change-of-location, unexpected content). To validate our toolkit, we tested the performance of neurotypical adults in the non-verbal and verbal versions in two studies: Study 1 with 50 native speakers of German and Study 2 with 50 native speakers of Greek. The data from both studies yield similar results. Participants performed well in all conditions, showing slightly more difficulties in the second- than first-order FB conditions, and in the non-verbal than the verbal version of the task. This suggests that the task is at the high end of the sensitive range for neurotypical adults, and is expected to be well inside the sensitive range for children and populations that have difficulties in ToM. Factors like deception and type of outcome in the video-scenarios did not influence the behavior of neurotypical adults, suggesting that the task does not have any confounds related to these factors. The order of presentation of the verbal and non-verbal version has an influence on performance; participants beginning with the verbal version performed slightly better than participants beginning with the non-verbal version. This suggests that neurotypical adults used language to mediate ToM performance and learn from a language-mediated task when performing a non-verbal ToM task. To conclude, our results show that the scenarios in the toolkit are of comparable difficulty and can be combined freely to match demands in future research with neurotypical children and autistic individuals, as well as other populations that have been shown to have difficulties in ToM. Differences between baseline and critical conditions can be assumed to reflect ToM abilities, rather than language and task-based confounding factors.

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“…It depends on clinical expertise in administration and scoring, which can hinder objective diagnostic outcomes. Thus, families with children with ASD lacking access to specialists or knowledge to navigate the clinical and administrative process are especially disadvantaged [ 11 , 12 , 13 ]. This study aims to provide a quick and easy machine learning (ML) model for the early screening of individuals with ASD, which can potentially be applied for the early screening and identification of children with ASD.…”

Section: Introductionmentioning

confidence: 99%

Autism Detection in Children: Integrating Machine Learning and Natural Language Processing in Narrative Analysis

Themistocleous,

Andreou,

Peristeri

2024

Behavioral Sciences

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Despite the consensus that early identification leads to better outcomes for individuals with autism spectrum disorder (ASD), recent research reveals that the average age of diagnosis in the Greek population is approximately six years. However, this age of diagnosis is delayed by an additional two years for families from lower-income or minority backgrounds. These disparities result in adverse impacts on intervention outcomes, which are further burdened by the often time-consuming and labor-intensive language assessments for children with ASD. There is a crucial need for tools that increase access to early assessment and diagnosis that will be rigorous and objective. The current study leverages the capabilities of artificial intelligence to develop a reliable and practical model for distinguishing children with ASD from typically-developing peers based on their narrative and vocabulary skills. We applied natural language processing-based extraction techniques to automatically acquire language features (narrative and vocabulary skills) from storytelling in 68 children with ASD and 52 typically-developing children, and then trained machine learning models on the children’s combined narrative and expressive vocabulary data to generate behavioral targets that effectively differentiate ASD from typically-developing children. According to the findings, the model could distinguish ASD from typically-developing children, achieving an accuracy of 96%. Specifically, out of the models used, hist gradient boosting and XGBoost showed slightly superior performance compared to the decision trees and gradient boosting models, particularly regarding accuracy and F1 score. These results bode well for the deployment of machine learning technology for children with ASD, especially those with limited access to early identification services.

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Re-Examining Labels in Neurocognitive Research: Evidence from Bilingualism and Autism as Spectrum-Trait Cases

Cited by 3 publications

References 71 publications

Figurative language processing in autism spectrum disorders: A review

Figurative language processing in autism spectrum disorders: A review

Development and validation of a task battery for verbal and non-verbal first- and second-order theory of mind

Autism Detection in Children: Integrating Machine Learning and Natural Language Processing in Narrative Analysis

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