Assessing Access to Language in the Real-World and Neural Language Processing in Preschoolers who are Deaf or Hard of Hearing - Children acquire language through interactions with their immediate environment, especially at home with their caregivers1. This is particularly relevant in preschool years, a critical period where children’s lifelong language abilities are established2. Insufficient quantity and quality of language exchanges with caregivers in early years increase the risk of language delays3,4,5. Recent models of language acquisition emphasize that noise in the home can limit access to language input, harming children's everyday language experiences6. The premise of this work is that children with poor language experiences, defined by limited caregiver input AND reduced access to language, face the highest risk of language delays. This is particularly relevant for children who are Deaf or Hard of Hearing (CDHH) developing spoken language because they have access to degraded auditory input via their hearing devices, further limiting access to language in noise6,7. Further, variability in access to language in the real-world might help explain why a substantial number of CDHH fall behind children with typical hearing (CTH) 8,9,10. This hypothesis remains untested, as methodologies for estimating access to language are currently lacking. In this cross-sectional study, we aim to close this gap in knowledge by developing a novel and ecologically valid Speech Accessibility Index (SAI) in a group of 40 preschool CDHH, and an age-matched control group of CTH. All children will be aged 4.0 to 5.9 years, and will be native English-speakers. We optimize heterogeneity in language experiences and outcomes by recruiting CDHH who are cochlear implant (CI) and/or hearing aid users. The SAI combines laboratory measures of speech-in-noise (SIN) skills with advanced real-world acoustic analyses. We use LENATM technology11 to estimate the percentage of home conversations with accessible language as well as quantity and quality of caregiver input. We evaluate the SAI’s effectiveness by determining its ability to predict our outcome of interest (language skills) along with caregiver input in each group of children (Fig.1, Aim 1). The interplay between language and cognitive skills (i.e., executive function skills) is pivotal for reconciling discrepancies between distorted incoming speech signals (e.g., masked words) and stored phonological sound representations12. Thus, poor language skills resulting from poor language experiences might hinder using prior knowledge to predict masked words, particularly in children with low cognitive skills13. This could limit children’s ability to further acquire language skills in real-world environments6. This is important to consider in CDHH because they heavily rely on these compensatory strategies to access language in noise13. Notably, the brain’s ability to use cognitive resources and prior knowledge to predict masked words can be objectively assessed with electroencephalography (EEG), particularly alpha power (9-12Hz)14,15,16. In adults, alpha inhibition is associated to both engagement of cognitive resources and recruitment of language areas of the brain to predict masked words when contextual cues are available14,15,16,17,18,19,20. Thus, task-induced alpha modulations can be used to track the development of neural mechanisms underlying predictive language processing in noise. However, language-induced alpha modulation patterns of children in real-world environments are unknown. Thus, we also aim to close this gap in knowledge by testing the feasibility of obtaining reliable measures of alpha power in preschool CTH and CDHH (Fig 1, Aim 2). Of note, we do not collect brain responses on CI users, because CI’s electrical noise obscures brain signals. Our study is innovative because we use cutting-edge methods to validate the conceptual framework depicted in Fig 1. Our long-term goal is to use this framework to longitudinally study effects of language experiences on CDHH’s outcomes. Our proposed study aims are: AIM 1: To
