Dyadic Vocal Contingency in Infants at High Risk for Autism Spectrum Disorder

Identifying the earliest emerging signs of autism spectrum disorder (ASD) is a priority for understanding how the disorder develops and unfolds, as well as for shaping and facilitating the provision of early intervention services. Further, there is a gap in our understanding of the mechanisms underlying these prodromal differences in socio-communicative behaviors. Temporal contingency in dyadic interactions may be one such mechanism. The present study examined contingency of infant vocal responsiveness to adult social partners as a possible mechanism underlying the relationship between early attention impairments and later language abilities and symptom presentation in ASD. The sample included 42 infants in total, with equal numbers across three groups: infants at heightened genetic risk for ASD with later ASD diagnoses (HR-ASD), infants at heightened risk without later ASD diagnoses (HR-neg), and infants with no known family history of ASD (LR). Results indicated that, while contingency did not significantly mediate the relationship between early attention and later outcomes, more impaired attention differentiated the HR groups from the LR group at 6 months. Attention was related to language outcomes at 6 months but not 12 months, possibly highlighting a window during which the HR-neg group becomes distinct from the HR-ASD group. At 12 months, the HR-ASD group demonstrated lower probability of vocalizing in response to adult vocalization as compared to both the HR-neg and LR groups. Contingency probability was also significantly predictive of later ASD diagnosis, such that higher contingency predicted lower likelihood of a positive ASD diagnosis. These findings suggest that targeting contingent responsiveness may offer a key opportunity for pre-diagnostic intervention, particularly for infants at heightened genetic susceptibility for ASD. This research also identifies a potential mechanism through which early risk factors for ASD are exacerbated, leading to cascading socio-communicative challenges.Doctor of Philosoph

Self-Organization of Early Vocal Development in Infants and Machines: The Role of Intrinsic Motivation

International audienceWe bridge the gap between two issues in infant development: vocal development and intrinsic motivation. We propose and experimentally test the hypothesis that general mechanisms of intrinsically motivated spontaneous exploration, also called curiosity-driven learning, can self-organize developmental stages during early vocal learning. We introduce a computational model of intrinsically motivated vocal exploration, which allows the learner to autonomously structure its own vocal experiments, and thus its own learning schedule, through a drive to maximize competence progress. This model relies on a physical model of the vocal tract, the auditory system and the agent's motor control as well as vocalizations of social peers. We present computational experiments that show how such a mechanism can explain the adaptive transition from vocal self-exploration with little influence from the speech environment, to a later stage where vocal exploration becomes influenced by vocalizations of peers. Within the initial self-exploration phase, we show that a sequence of vocal production stages self-organizes, and shares properties with data from infant developmental psychology: the vocal learner first discovers how to control phonation, then focuses on vocal variations of unarticulated sounds, and finally automatically discovers and focuses on babbling with articulated proto-syllables. As the vocal learner becomes more proficient at producing complex sounds, imitating vocalizations of peers starts to provide high learning progress explaining an automatic shift from self-exploration to vocal imitation

A Curious Robot Learner for Interactive Goal-Babbling (Strategically Choosing What, How, When and from Whom to Learn)

Les dé s pour voir des robots opérant dans l environnement de tous les jours des humains et sur unelongue durée soulignent l importance de leur adaptation aux changements qui peuvent être imprévisiblesau moment de leur construction. Ils doivent être capable de savoir quelles parties échantillonner, et quelstypes de compétences il a intérêt à acquérir. Une manière de collecter des données est de décider par soi-même où explorer. Une autre manière est de se référer à un mentor. Nous appelons ces deux manièresde collecter des données des modes d échantillonnage. Le premier mode d échantillonnage correspondà des algorithmes développés dans la littérature pour automatiquement pousser l agent vers des partiesintéressantes de l environnement ou vers des types de compétences utiles. De tels algorithmes sont appelésdes algorithmes de curiosité arti cielle ou motivation intrinsèque. Le deuxième mode correspond au guidagesocial ou l imitation, où un partenaire humain indique où explorer et où ne pas explorer.Nous avons construit une architecture algorithmique intrinsèquement motivée pour apprendre commentproduire par ses actions des e ets et conséquences variées. Il apprend de manière active et en ligne encollectant des données qu il choisit en utilisant plusieurs modes d échantillonnage. Au niveau du metaapprentissage, il apprend de manière active quelle stratégie d échantillonnage est plus e cace pour améliorersa compétence et généraliser à partir de son expérience à un grand éventail d e ets. Par apprentissage parinteraction, il acquiert de multiples compétences de manière structurée, en découvrant par lui-même lesséquences développementale.The challenges posed by robots operating in human environments on a daily basis and in the long-termpoint out the importance of adaptivity to changes which can be unforeseen at design time. The robot mustlearn continuously in an open-ended, non-stationary and high dimensional space. It must be able to knowwhich parts to sample and what kind of skills are interesting to learn. One way is to decide what to exploreby oneself. Another way is to refer to a mentor. We name these two ways of collecting data sampling modes.The rst sampling mode correspond to algorithms developed in the literature in order to autonomously drivethe robot in interesting parts of the environment or useful kinds of skills. Such algorithms are called arti cialcuriosity or intrinsic motivation algorithms. The second sampling mode correspond to social guidance orimitation where the teacher indicates where to explore as well as where not to explore. Starting fromthe study of the relationships between these two concurrent methods, we ended up building an algorithmicarchitecture with a hierarchical learning structure, called Socially Guided Intrinsic Motivation (SGIM).We have built an intrinsically motivated active learner which learns how its actions can produce variedconsequences or outcomes. It actively learns online by sampling data which it chooses by using severalsampling modes. On the meta-level, it actively learns which data collection strategy is most e cient forimproving its competence and generalising from its experience to a wide variety of outcomes. The interactivelearner thus learns multiple tasks in a structured manner, discovering by itself developmental sequences.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Expressive language development in minimally verbal autistic children: exploring the role of speech production

Trajectories of expressive language development are highly heterogeneous in autism. I examine the hypothesis that co-morbid speech production difficulties may be a contributing factor for some minimally verbal autistic individuals. Chapters 1 and 2 provide an overview of language variation within autism, and existing intervention approaches for minimally verbal autistic children. These chapters situate this thesis within the existing literature. Chapter 3 describes a longitudinal study of expressive language in minimally verbal 3-5 year olds (n=27), with four assessment points over 12 months. Contrary to expectations, initial communicative intent, parent responsiveness and response to joint attention did not predict expressive language growth or outcome. Speech skills were significant predictors. Chapter 4 describes the design, development and feasibility testing of the BabbleBooster app, a novel, parent-meditated speech skills intervention, in which 19 families participated for 16 weeks. Acceptability feedback was positive but adherence was variable. I discuss how this could be improved in future iterations of the app and intervention protocol. Chapter 5 details how BabbleBooster’s efficacy was evaluated. For interventions with complex or rare populations, a randomized case series design is a useful alternative to an under-powered group trial. There was no evidence that BabbleBooster improved speech production scores, likely due to limited dosage. Future research using this study design could determine optimal treatment intensity and duration with an improved version of the app. Taken together, these studies underscore the contribution of speech production abilities to expressive language development in minimally verbal autistic individuals. I argue that this reflects an additional condition, and is not a consequence of core autism features. The intervention piloted here represents a first step towards developing a scalable tool for parents to support speech development in minimally verbal children, and illustrates the utility of randomized single case series for testing treatment effects in small, heterogeneous cohorts

Word Learning in 6-16 Month Old Infants

Understanding words requires infants to not only isolate words from the speech around them and delineate concepts from their world experience, but also to establish which words signify which concepts, in all and only the right set of circumstances. Previous research places the onset of this ability around infants\u27 first birthdays, at which point they have begun to solidify their native language phonology, and have learned a good deal about categories, objects, and people. In this dissertation, I present research that alters this accepted timeline. In Study 1, I find that by 6 months of age, infants demonstrate understanding of around a dozen words for foods and body parts. Around 13-14 months of age, performance increases significantly. In Study 2, I find that for a set of early non-nouns, e.g. `uh-oh\u27 and `eat\u27, infants do not show understanding until 10 months, but again show a big comprehension boost around 13-14 months. I discuss possible reasons for the onset of noun-comprehension at 6 months, the relative delay in non-noun comprehension, and the performance boost for both word-types around 13-14 months. In Study 3, I replicate and extend Study 1\u27s findings, showing that around 6 months infants also understand food and body-part words when these words are spoken by a new person, but conversely, by 12 months, show poor word comprehension if a single vowel in the word is changed, even when the speaker is highly familiar. Taken together, these results suggest that word learning begins before infants have fully solidified their native language phonology, that certain generalizations about words are available to infants at the outset of word comprehension, and that infants are able to learn words for complex object and event categories before their first birthday. Implications for language acquisition and cognitive development more broadly are discussed

Mastery Motivation and Executive Functions as School Readiness Factors: Enhancement of School Readiness in Kenya

The overall goal of this study is to enhance school readiness assessment in Kenya by developing an easy-to-use tablet-based android app that can support teachers and learners during the assessment of Pre-academic skills, Mastery Motivation (MM) and Executive Functions (EF) in the Kenyan context. We operationalised MM and EF as components of Approaches to Learning (ATL): one of the poorly assessed domains of school readiness. This research was based on the theory of ATL and followed a non-experimental longitudinal research design. One study was a Scoping Review that identified the gap in the literature in the assessment of School Readiness domains using game-like apps. This study formed the basis for developing Finding Out Children's Unique Strengths (FOCUS) app for Kenya following Education Design Research Approach. Two studies tested and evaluated the psychometric properties of the FOCUS app in the Kenyan context. Another two empirical studies focused on adapting the Preschool Dimension of Mastery Questionnaire 18 (DMQ 18) and the Childhood Executive Functioning (CHEXI) to complement the assessment of MM and EF, respectively. In addition, one study addressed the role played by MM and EF on school academic performance. A total of 40 teachers, 497 preschool and 535 grade 1 children were involved in this study. Both parametric and non-parametric statistical analyses were used to analyse the generated data. The FOCUS app, CHEXI and DMQ 18 fit well with the data and exhibited strong psychometric properties, thus being suitable for the Kenyan context. Furthermore, both MM and EF were directly and indirectly, involved in grade one children's academic performance. FOCUS app tasks, pre-academic skills, and number and letter search tasks at preprimary II strongly predicted preschool and grade one academic performance. MM assessed using the FOCUS app as a better predictor of academic performance than the DMQ 18. Interventions to improve MM and EF promise to enhance School Readiness in the Kenyan context. The FOCUS app can greatly complement Kenya School Readiness Test to give teachers and parents a broader spectrum to make correct decisions concerning the child

Non Invasive Tools for Early Detection of Autism Spectrum Disorders

Autism Spectrum Disorders (ASDs) describe a set of neurodevelopmental disorders. ASD represents a significant public health problem. Currently, ASDs are not diagnosed before the 2nd year of life but an early identification of ASDs would be crucial as interventions are much more effective than specific therapies starting in later childhood. To this aim, cheap an contact-less automatic approaches recently aroused great clinical interest. Among them, the cry and the movements of the newborn, both involving the central nervous system, are proposed as possible indicators of neurological disorders. This PhD work is a first step towards solving this challenging problem. An integrated system is presented enabling the recording of audio (crying) and video (movements) data of the newborn, their automatic analysis with innovative techniques for the extraction of clinically relevant parameters and their classification with data mining techniques. New robust algorithms were developed for the selection of the voiced parts of the cry signal, the estimation of acoustic parameters based on the wavelet transform and the analysis of the infant’s general movements (GMs) through a new body model for segmentation and 2D reconstruction. In addition to a thorough literature review this thesis presents the state of the art on these topics that shows that no studies exist concerning normative ranges for newborn infant cry in the first 6 months of life nor the correlation between cry and movements. Through the new automatic methods a population of control infants (“low-risk”, LR) was compared to a group of “high-risk” (HR) infants, i.e. siblings of children already diagnosed with ASD. A subset of LR infants clinically diagnosed as newborns with Typical Development (TD) and one affected by ASD were compared. The results show that the selected acoustic parameters allow good differentiation between the two groups. This result provides new perspectives both diagnostic and therapeutic