Differences in speech articulatory timing and associations with pragmatic language ability in autism / Joseph C.Y. LAU in Research in Autism Spectrum Disorders, 102 (April 2023)  

Public ISBD [article]  inResearch in Autism Spectrum Disorders > 102 (April 2023) . - p.102118 Titre : Differences in speech articulatory timing and associations with pragmatic language ability in autism Type de document : texte imprimé Auteurs : Joseph C.Y. LAU, Auteur ; Molly LOSH, Auteur ; Marisha SPEIGHTS, Auteur Article en page(s) : p.102118 Langues : Anglais (eng) Mots-clés : ASD  Speech articulation  Articulatory timing  Prosody  Pragmatics Index. décimale : PER Périodiques Résumé : Background Speech articulation difficulties have not traditionally been considered to be a feature of Autism Spectrum Disorder (ASD). In contrast, speech prosodic differences have been widely reported in ASD, and may even be expressed in subtle form among clinically unaffected first-degree relatives, representing the expression of underlying genetic liability. Some evidence has challenged this traditional dichotomy, suggesting that differences in speech articulatory mechanisms may be evident in ASD, and potentially related to perceived prosodic differences. Clinical measurement of articulatory skills has traditionally been phoneme-based, rather than by acoustic measurement of motor control. Subtle differences in articulatory/motor control, prosodic characteristics (acoustic), and pragmatic language ability (linguistic) may each be contributors to differences perceived by listeners, but the interrelationship is unclear. In this study, we examined the articulatory aspects of this relationship, in speech samples from individuals with ASD and their parents during narration. Method Using Speechmark® analysis, we examined articulatory landmarks, fine-grained representations of articulatory timing as series of laryngeal and vocal-tract gestures pertaining to prosodic elements crucial for conveying pragmatic information. Results Results revealed articulatory timing differences in individuals with ASD but not their parents, suggesting that although potentially not influenced by broader genetic liability to ASD, subtle articulatory differences may indeed be evident in ASD as the recent literature indicates. A follow-up path analysis detected associations between articulatory timing differences and prosody, and subsequently, pragmatic language ability. Conclusion Together, results suggest a complex relationship where subtle differences in articulatory timing may result in atypical acoustic signals, and serve as a distal mechanistic contributor to pragmatic language ability ASD. En ligne : https://doi.org/10.1016/j.rasd.2023.102118 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=501 [article] Differences in speech articulatory timing and associations with pragmatic language ability in autism [texte imprimé] / Joseph C.Y. LAU, Auteur ; Molly LOSH, Auteur ; Marisha SPEIGHTS, Auteur . - p.102118. Langues : Anglais (eng) in Research in Autism Spectrum Disorders > 102 (April 2023) . - p.102118 Mots-clés : ASD  Speech articulation  Articulatory timing  Prosody  Pragmatics Index. décimale : PER Périodiques Résumé : Background Speech articulation difficulties have not traditionally been considered to be a feature of Autism Spectrum Disorder (ASD). In contrast, speech prosodic differences have been widely reported in ASD, and may even be expressed in subtle form among clinically unaffected first-degree relatives, representing the expression of underlying genetic liability. Some evidence has challenged this traditional dichotomy, suggesting that differences in speech articulatory mechanisms may be evident in ASD, and potentially related to perceived prosodic differences. Clinical measurement of articulatory skills has traditionally been phoneme-based, rather than by acoustic measurement of motor control. Subtle differences in articulatory/motor control, prosodic characteristics (acoustic), and pragmatic language ability (linguistic) may each be contributors to differences perceived by listeners, but the interrelationship is unclear. In this study, we examined the articulatory aspects of this relationship, in speech samples from individuals with ASD and their parents during narration. Method Using Speechmark® analysis, we examined articulatory landmarks, fine-grained representations of articulatory timing as series of laryngeal and vocal-tract gestures pertaining to prosodic elements crucial for conveying pragmatic information. Results Results revealed articulatory timing differences in individuals with ASD but not their parents, suggesting that although potentially not influenced by broader genetic liability to ASD, subtle articulatory differences may indeed be evident in ASD as the recent literature indicates. A follow-up path analysis detected associations between articulatory timing differences and prosody, and subsequently, pragmatic language ability. Conclusion Together, results suggest a complex relationship where subtle differences in articulatory timing may result in atypical acoustic signals, and serve as a distal mechanistic contributor to pragmatic language ability ASD. En ligne : https://doi.org/10.1016/j.rasd.2023.102118 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=501

Lifelong Tone Language Experience does not Eliminate Deficits in Neural Encoding of Pitch in Autism Spectrum Disorder / Joseph C.Y. LAU in Journal of Autism and Developmental Disorders, 51-9 (September 2021)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 51-9 (September 2021) . - p.3291-3310 Titre : Lifelong Tone Language Experience does not Eliminate Deficits in Neural Encoding of Pitch in Autism Spectrum Disorder Type de document : texte imprimé Auteurs : Joseph C.Y. LAU, Auteur ; Carol K.S. TO, Auteur ; Judy S.K. KWAN, Auteur ; Xin KANG, Auteur ; Molly LOSH, Auteur ; Patrick C.M. WONG, Auteur Article en page(s) : p.3291-3310 Langues : Anglais (eng) Mots-clés : Acoustic Stimulation  Adult  Autism Spectrum Disorder  Child  Humans  Language  Pitch Perception  Autism Spectrum Disorder  Frequency-following responses  Machine-learning  Neural pitch encoding  Tone language Index. décimale : PER Périodiques Résumé : Atypical pitch processing is a feature of Autism Spectrum Disorder (ASD), which affects non-tone language speakers' communication. Lifelong auditory experience has been demonstrated to modify genetically-predisposed risks for pitch processing. We examined individuals with ASD to test the hypothesis that lifelong auditory experience in tone language may eliminate impaired pitch processing in ASD. We examined children's and adults' Frequency-following Response (FFR), a neurophysiological component indexing early neural sensory encoding of pitch. Univariate and machine-learning-based analytics suggest less robust pitch encoding and diminished pitch distinctions in the FFR from individuals with ASD. Contrary to our hypothesis, results point to a linguistic pitch encoding impairment associated with ASD that may not be eliminated even by lifelong sensory experience. En ligne : http://dx.doi.org/10.1007/s10803-020-04796-7 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=453 [article] Lifelong Tone Language Experience does not Eliminate Deficits in Neural Encoding of Pitch in Autism Spectrum Disorder [texte imprimé] / Joseph C.Y. LAU, Auteur ; Carol K.S. TO, Auteur ; Judy S.K. KWAN, Auteur ; Xin KANG, Auteur ; Molly LOSH, Auteur ; Patrick C.M. WONG, Auteur . - p.3291-3310. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 51-9 (September 2021) . - p.3291-3310 Mots-clés : Acoustic Stimulation  Adult  Autism Spectrum Disorder  Child  Humans  Language  Pitch Perception  Autism Spectrum Disorder  Frequency-following responses  Machine-learning  Neural pitch encoding  Tone language Index. décimale : PER Périodiques Résumé : Atypical pitch processing is a feature of Autism Spectrum Disorder (ASD), which affects non-tone language speakers' communication. Lifelong auditory experience has been demonstrated to modify genetically-predisposed risks for pitch processing. We examined individuals with ASD to test the hypothesis that lifelong auditory experience in tone language may eliminate impaired pitch processing in ASD. We examined children's and adults' Frequency-following Response (FFR), a neurophysiological component indexing early neural sensory encoding of pitch. Univariate and machine-learning-based analytics suggest less robust pitch encoding and diminished pitch distinctions in the FFR from individuals with ASD. Contrary to our hypothesis, results point to a linguistic pitch encoding impairment associated with ASD that may not be eliminated even by lifelong sensory experience. En ligne : http://dx.doi.org/10.1007/s10803-020-04796-7 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=453

Pre-trained artificial intelligence language model represents pragmatic language variability central to autism and genetically related phenotypes / Joseph C.Y. LAU in Autism, 29-5 (May 2025)  

Public ISBD [article]  inAutism > 29-5 (May 2025) . - p.1346-1358 Titre : Pre-trained artificial intelligence language model represents pragmatic language variability central to autism and genetically related phenotypes Type de document : texte imprimé Auteurs : Joseph C.Y. LAU, Auteur ; Emily LANDAU, Auteur ; Qingcheng ZENG, Auteur ; Ruichun ZHANG, Auteur ; Stephanie CRAWFORD, Auteur ; Rob VOIGT, Auteur ; Molly LOSH, Auteur Article en page(s) : p.1346-1358 Langues : Anglais (eng) Mots-clés : artificial intelligence  autism  broad autism phenotype  FMR1 premutation  fragile X  pragmatic language  pre-trained language model Index. décimale : PER Périodiques Résumé : Many individuals with autism experience challenges using language in social contexts (i.e., pragmatic language). Characterizing and understanding pragmatic variability is important to inform intervention strategies and the etiology of communication challenges in autism; however, current manual coding-based methods are often time and labor intensive, and not readily applied in ample sample sizes. This proof-of-concept methodological study employed an artificial intelligence pre-trained language model, Bidirectional Encoder Representations from Transformers, as a tool to address such challenges. We applied Bidirectional Encoder Representations from Transformers to computationally index pragmatic-related variability in autism and in genetically related phenotypes displaying pragmatic differences, namely, in parents of autistic individuals, fragile X syndrome, and FMR1 premutation. Findings suggest that without model fine-tuning, Bidirectional Encoder Representations from Transformers?s Next Sentence Prediction module was able to derive estimates that differentiate autistic from non-autistic groups. Moreover, such computational estimates correlated with manually coded characterization of pragmatic abilities that contribute to conversational coherence, not only in autism but also in the other genetically related phenotypes. This study represents a step forward in evaluating the efficacy of artificial intelligence language models for capturing clinically important pragmatic differences and variability related to autism, showcasing the potential of artificial intelligence to provide automatized, efficient, and objective tools for pragmatic characterization to help advance the field.Lay abstract Autism is clinically defined by challenges with social language, including difficulties offering on-topic language in a conversation. Similar differences are also seen in genetically related conditions such as fragile X syndrome (FXS), and even among those carrying autism-related genes who do not have clinical diagnoses (e.g., the first-degree relatives of autistic individuals and carriers of the FMR1 premutation), which suggests there are genetic influences on social language related to the genes involved in autism. Characterization of social language is therefore important for informing potential intervention strategies and understanding the causes of communication challenges in autism. However, current tools for characterizing social language in both clinical and research settings are very time and labor intensive. In this study, we test an automized computational method that may address this problem. We used a type of artificial intelligence known as pre-trained language model to measure aspects of social language in autistic individuals and their parents, non-autistic comparison groups, and individuals with FXS and the FMR1 premutation. Findings suggest that these artificial intelligence approaches were able to identify differences in social language in autism, and to provide insight into the individuals' ability to keep a conversation on-topic. These findings also were associated with broader measures of participants' social communication ability. This study is one of the first to use artificial intelligence models to capture important differences in social language in autism and genetically related groups, demonstrating how artificial intelligence might be used to provide automatized, efficient, and objective tools for language characterization. En ligne : https://dx.doi.org/10.1177/13623613241304488 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=555 [article] Pre-trained artificial intelligence language model represents pragmatic language variability central to autism and genetically related phenotypes [texte imprimé] / Joseph C.Y. LAU, Auteur ; Emily LANDAU, Auteur ; Qingcheng ZENG, Auteur ; Ruichun ZHANG, Auteur ; Stephanie CRAWFORD, Auteur ; Rob VOIGT, Auteur ; Molly LOSH, Auteur . - p.1346-1358. Langues : Anglais (eng) in Autism > 29-5 (May 2025) . - p.1346-1358 Mots-clés : artificial intelligence  autism  broad autism phenotype  FMR1 premutation  fragile X  pragmatic language  pre-trained language model Index. décimale : PER Périodiques Résumé : Many individuals with autism experience challenges using language in social contexts (i.e., pragmatic language). Characterizing and understanding pragmatic variability is important to inform intervention strategies and the etiology of communication challenges in autism; however, current manual coding-based methods are often time and labor intensive, and not readily applied in ample sample sizes. This proof-of-concept methodological study employed an artificial intelligence pre-trained language model, Bidirectional Encoder Representations from Transformers, as a tool to address such challenges. We applied Bidirectional Encoder Representations from Transformers to computationally index pragmatic-related variability in autism and in genetically related phenotypes displaying pragmatic differences, namely, in parents of autistic individuals, fragile X syndrome, and FMR1 premutation. Findings suggest that without model fine-tuning, Bidirectional Encoder Representations from Transformers?s Next Sentence Prediction module was able to derive estimates that differentiate autistic from non-autistic groups. Moreover, such computational estimates correlated with manually coded characterization of pragmatic abilities that contribute to conversational coherence, not only in autism but also in the other genetically related phenotypes. This study represents a step forward in evaluating the efficacy of artificial intelligence language models for capturing clinically important pragmatic differences and variability related to autism, showcasing the potential of artificial intelligence to provide automatized, efficient, and objective tools for pragmatic characterization to help advance the field.Lay abstract Autism is clinically defined by challenges with social language, including difficulties offering on-topic language in a conversation. Similar differences are also seen in genetically related conditions such as fragile X syndrome (FXS), and even among those carrying autism-related genes who do not have clinical diagnoses (e.g., the first-degree relatives of autistic individuals and carriers of the FMR1 premutation), which suggests there are genetic influences on social language related to the genes involved in autism. Characterization of social language is therefore important for informing potential intervention strategies and understanding the causes of communication challenges in autism. However, current tools for characterizing social language in both clinical and research settings are very time and labor intensive. In this study, we test an automized computational method that may address this problem. We used a type of artificial intelligence known as pre-trained language model to measure aspects of social language in autistic individuals and their parents, non-autistic comparison groups, and individuals with FXS and the FMR1 premutation. Findings suggest that these artificial intelligence approaches were able to identify differences in social language in autism, and to provide insight into the individuals' ability to keep a conversation on-topic. These findings also were associated with broader measures of participants' social communication ability. This study is one of the first to use artificial intelligence models to capture important differences in social language in autism and genetically related groups, demonstrating how artificial intelligence might be used to provide automatized, efficient, and objective tools for language characterization. En ligne : https://dx.doi.org/10.1177/13623613241304488 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=555

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