Behavioral and Neural Measures of Spoken Word Recognition in Late Language Emergence - ABSTRACT Toddlers with late language emergence (LLE), or late talkers (LTs), are 18-35 month-olds with a limited spoken vocabulary, but average non-linguistic abilities and no overt sensory impairments or other developmental delays such as autism spectrum disorder (Collisson, 2016; Paul & Jennings, 1992). Approximately 15% of toddlers meet LLE criteria (Singleton, 2018; Paul, 1992) and are at elevated risk for persistent, lifelong language and literacy deficits that result in poor social, academic and vocational outcomes (Paul, 1993; Rescorla, 2009; Singleton, 2018). Upwards of 16% of LTs will prospectively be diagnosed with a spoken and/or written language disorder at age 9 (Paul, Murray, Clancy, & Andrews, 1997) while the majority of LTs retain suboptimal language functioning through early adulthood (Rescorla, 2002; Singleton, 2018). The proposed research seeks to (1) identify early behavioral and neural markers of chronic impact in order to optimally allocate scarce early intervention resources, and (2) examine the variation and distribution of behavioral phenotypes which will provide the foundation for more focused and targeted forms of interventions for LTs with a range of clinical and subclinical language outcomes. The project will complement prior work on LLE focused on language production by evaluating the fine- grained timecourse of spoken word recognition (SWR) in LTs and two control groups (typically developing age- matched and language-matched peers) using behavioral (eye tracking) and neural (electroencephalography) measurements. All participants will complete comprehensive clinical characterization including assessments of language and non-linguistic functioning. In Experiment 1, participants will be trained on a simple selection task using eight familiar words that overlap phonologically (e.g., at onset, BUNNY-BUBBLES, at offset, KITTEN- MITTEN) and semantically (e.g., BUNNY-KITTEN). Eye tracking will be used to estimate group and individual differences in lexical activation and competition over time in the selection task. In Experiment 2, neural responses will be recorded in a passive listening paradigm, using EEG, as participants view a silent movie while the familiar words from Experiment 1 are repeated many times. We will train a machine-learning algorithm (support vector machine, or SVM) to decode the full EEG response to specific words for each toddler. Group and individual differences in the relative success of the SVM may reflect the consistency and fidelity of neural responses. ERP (event-related potential) analysis will also be used to examine group and individual differences in mean responses to the spoken words. Group and individual differences in eye tracking, EEG and/or ERP measures will yield new insights into the language processing abilities of LTs and will provide a basis for future work aimed at identifying those LTs at greatest risk for poor outcomes.
