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.
