Research over the past 50 years has yielded several promising approaches and many specific intervention techniques designed to enhance the communication and language development of young children with intellectual and developmental delays and disabilities. Yet virtually no systematic research has been conducted on the effects of different treatment intensities. We review how intervention intensity has been defined in the literature and propose a set of terms borrowed from medicine that are intended to capture the dynamic aspects of this concept as an aid to further investigation. On the basis of this approach, we propose four types of knowledge that can be generated through the systematic study of treatment intensity and discuss appropriate methods for investigating the effects of differential treatment intensities. We conclude with three recommendations for the field.

The results offer new insight into the landscape of the early language environment, with clinical implications for identification of children at-risk for impoverished language environments.

Down syndrome (DS) is associated with abnormalities in multiple organ systems and a characteristic phenotype that includes numerous behavioral features. Language, however, is among the most impaired domains of functioning in DS and, perhaps, also the greatest barrier to independent meaningful inclusion in the community. In this article, we review what is known about the extent, nature, and correlates of the language and related problems of individuals with Down syndrome. In doing so, we focus largely on the syndrome-specific features of the language phenotype, although we also consider within-syndrome variation. The review focuses on the prelinguistic foundations of language and the major components of language (i.e., vocabulary, syntax, and pragmatics). We also consider two topics in the treatment and education of individuals with DS: prelinguistic communication intervention and the acquisition of literacy skills.

For generations the study of vocal development and its role in language has been conducted laboriously, with human transcribers and analysts coding and taking measurements from small recorded samples. Our research illustrates a method to obtain measures of early speech development through automated analysis of massive quantities of day-long audio recordings collected naturalistically in children's homes. A primary goal is to provide insights into the development of infant control over infrastructural characteristics of speech through large-scale statistical analysis of strategically selected acoustic parameters. In pursuit of this goal we have discovered that the first automated approach we implemented is not only able to track children's development on acoustic parameters known to play key roles in speech, but also is able to differentiate vocalizations from typically developing children and children with autism or language delay. The method is totally automated, with no human intervention, allowing efficient sampling and analysis at unprecedented scales. The work shows the potential to fundamentally enhance research in vocal development and to add a fully objective measure to the battery used to detect speech-related disorders in early childhood. Thus, automated analysis should soon be able to contribute to screening and diagnosis procedures for early disorders, and more generally, the findings suggest fundamental methods for the study of language in natural environments.