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Automated behavioral measurement using machine learning is gaining ground in psychological research. Automated approaches have the potential to reduce the labor and time associated with manual behavioral coding, and to enhance measurement objectivity; yet their application in young infants remains limited. We asked whether automated measurement can accurately identify newborn mouth opening—a facial gesture involved in infants’ communication and expression—in videos of 29 newborns (age range 9-29 days, 55.2% female, 58.6% White, 51.7% Hispanic/Latino) during neonatal imitation testing. We employed a 3-dimensional cascade regression computer vision algorithm to automatically track and register newborn faces. The facial landmark coordinates of each frame were input into a Support Vector Machine (SVM) classifier, trained to recognize the presence and absence of mouth opening at the frame-level as identified by expert human coders. The SVM classifier was trained using leave-one-infant-out cross validation (training: N = 22 newborns, 95 videos, 354,468 frames), and the best classifier showed an average validation accuracy of 75%. The final SVM classifier was tested on different newborns from the training set (testing: N = 7 newborns, 29 videos, 118,615 frames) and demonstrated 76% overall accuracy in identifying mouth opening. An intraclass correlation coefficient of .81 among the SVM classifier and human experts indicated that the SVM classifier was, on a practical level, reliable with experts in quantifying newborns’ total rates of mouth opening across videos. Results highlight the potential of automated measurement approaches for objectively identifying the presence and absence of mouth opening in newborn infants. 

We demonstrate a procedure for the anonymization of infant subjects in videos such that salient behavioral information is retained. This method also creates a new identity that is consistent temporally across video frames. We present an overview of this anonymization process, which involves moving through the latent space of a generative model with an infant specific latent space traversal technique. We apply the technique on videos of infants, a historically difficult source of data, and make comparisons to other state-of-the-art anonymization systems. Metrics demonstrate an improved ability to retain emotional content of videos during the anonymization process, even during extreme emotions or poses, while maintaining a consistent identity throughout. 

Oxytocin is a neuropeptide positively associated with prosociality in adults. Here, we studied whether infants' salivary oxytocin can be reliably measured, is developmentally stable, and is linked to social behavior. We longitudinally collected saliva from 62 U.S. infants (44 % female, 56 % Hispanic/Latino, 24 % Black, 18 % non-Hispanic White, 11 % multiracial) at 4, 8, and 14 months of age and offline-video-coded the valence of their facial affect in response to a video of a smiling woman. We also captured infants' affective reactions in terms of excitement/joyfulness during a live, structured interaction with a singing woman in the Early Social Communication Scales at 14 months. We detected stable individual differences in infants' oxytocin levels over time (over minutes and months) and in infants' positive affect over months and across contexts (video-based and in live interactions). We detected no statistically significant changes in oxytocin levels between 4 and 8 months but found an increase from 8 to 14 months. Infants with higher oxytocin levels showed more positive facial affect to a smiling person video at 4 months; however, this association disappeared at 8 months, and reversed at 14 months (i.e., higher oxytocin was associated with less positive facial affect). Infant salivary oxytocin may be a reliable physiological measure of individual differences related to socio-emotional development. 

New technologies that combine digital sensors with automated processing algorithms are now being deployed to study preschool classrooms. This article provides an overview of these new sensing technologies, focusing on automated speaker classification, the analysis of children’s and teachers’ speech, and the detection and analysis of their movements over the course of the school day. Findings from recent studies utilizing these technologies are presented to illustrate the contribution of these sensing technologies to our understanding of classroom processes that predict children’s language and social development. In particular, the potential to collect extended real-time data on the speech and movement of all children and teachers in a classroom provides a broader window on the variability of individual children’s interactions with peers and teachers and their integration into classroom social networks. The article describes current challenges related to the use of sensing technologies in preschool settings, as well as advances that may overcome these challenges and allow for more in-depth investigations of children’s early classroom experiences. 

Abstract Homophily, the tendency for individuals to preferentially interact with others similar to themselves is typically documented via self-report and, for children, adult report. Few studies have investigated homophily directly using objective measures of social movement. We quantified homophily in children with developmental disabilities (DD) and typical development (TD) using objective measures of position/orientation in preschool inclusion classrooms, designed to promote interaction between these groups of children. Objective measurements were collected using ultra-wideband radio-frequency tracking to determine social approach and social contact, measures of social movement and interaction. Observations of 77 preschoolers (47 with DD, and 30 TD) were conducted in eight inclusion classrooms on a total of 26 days. We compared DD and TD groups with respect to how children approached and shared time in social contact with peers using mixed-effects models. Children in concordant dyads (DD-DD and TD-TD) both moved toward each other at higher velocities and spent greater time in social contact than discordant dyads (DD-TD), evidencing homophily. DD-DD dyads spent less time in social contact than TD-TD dyads but were comparable to TD-TD dyads in their social approach velocities. Children’s preference for similar peers appears to be a pervasive feature of their naturalistic interactions. 

Tuberous Sclerosis Complex (TSC), is a neurocutaneous disorder, associated with a high prevalence of autism spectrum disorder (ASD; ∼50% of individuals). As TSC is a leading cause of syndromic ASD, understanding language development in this population would not only be important for individuals with TSC but may also have implications for those with other causes of syndromic and idiopathic ASD. In this mini review, we consider what is known about language development in this population and how speech and language in TSC are related to ASD. Although up to 70% of individuals with TSC report language difficulties, much of the limited research to date on language in TSC has been based on summary scores from standardized assessments. Missing is a detailed understanding of the mechanisms driving speech and language in TSC and how they relate to ASD. Here, we review recent work suggesting that canonical babbling and volubility—two precursors of language development that predict the emergence of speech and are delayed in infants with idiopathic ASD—are also delayed in infants with TSC. We then look to the broader literature on language development to identify other early precursors of language development that tend to be delayed in children with autism as a guide for future research on speech and language in TSC. We argue that vocal turn-taking, shared attention, and fast mapping are three such skills that can provide important information about how speech and language develop in TSC and where potential delays come from. The overall goal of this line of research is to not only illuminate the trajectory of language in TSC with and without ASD, but to ultimately find strategies for earlier recognition and treatment of the pervasive language difficulties in this population. 

This work is motivated by the need to automate the analysis of parent-infant interactions to better understand the existence of any potential behavioral patterns useful for the early diagnosis of autism spectrum disorder (ASD). It presents an approach for synthesizing the facial expression exchanges that occur during parent-infant interactions. This is accomplished by developing a novel approach that uses landmarks when synthesizing changing facial expressions. The proposed model consists of two components: (i) The first is a landmark converter that receives a set of facial landmarks and the target emotion as input and outputs a set of new landmarks transformed to match the emotion. (ii) The second component involves an image converter that takes in an input image, a target landmark and a target emotion and outputs a face transformed to match the input emotion. The inclusion of landmarks in the generation process proves useful in the generation of baby facial expressions; babies have somewhat different facial musculature and facial dynamics than adults. This paper presents a realistic-looking matrix of changing facial expressions sampled from a 2-D emotion continuum (valence and arousal) and displays successfully transferred facial expressions from real-life mother-infant dyads to novel ones. 

Abstract Classroom engagement plays a crucial role in preschoolers' development, yet the correlates of engagement, especially among children with autism spectrum disorder (ASD) and developmental delays (DD), remains unknown. This study examines levels of engagement with classroom social partners and tasks among children in three groups ASD, DD, and typical development (TD). Here, we asked whether children's vocal interactions (vocalizations to and from peers and teachers) were associated with their classroom engagement with social partners (peers and teachers) and with tasks, and whether the association between classroom engagement and vocal interactions differed between children in the ASD group and their peers in the DD and TD groups. Automated measures of vocalizations and location quantified children's vocal interactions with peers and teachers over the course of the school year. Automated location and vocalization data were used to capture both (1) children's vocal output to specific peers and teachers, and (2) the vocal input they received from those peers and teachers. Participants were 72 3–5‐year‐olds (M_age = 48.6 months, SD = 7.0, 43% girls) and their teachers. Children in the ASD group displayed lower engagement with peers, teachers, and tasks than children in the TD group; they also showed lower engagement with peers than children in the DD group. Overall, children's own vocalizations were positively associated with engagement with social partners. Thus, although children in the ASD group tend to have lower engagement scores than children in the TD group, active participation in vocal interactions appears to support their classroom engagement with teachers and peers.