Skip to main content

Oscillatory Rhythmic Entrainment and the Foundations of Language Acquisition

Periodic Reporting for period 3 - BABYRHYTHM (Oscillatory Rhythmic Entrainment and the Foundations of Language Acquisition)

Reporting period: 2019-09-01 to 2021-02-28

We are addressing the issue of how best to predict child language outcomes using measures taken pre-verbally in infancy. The project uses infant brain imaging (EEG) and psychoacoustic measures to generate robust early neural and behavioural markers of vocabulary, phonological and morphological development. During 8 brain imaging sessions taken over the first year of life, we collect a series of EEG markers of auditory, visual and motor responses to language, as well as the precision of rhythmic movements. From 12 months to 2.5 years, we measure a range of language outcomes. We then test which early neural and behavioural markers are the best predictors of later language outcomes for the domains of vocabulary, phonology and syntax/grammar.

It is important for society because half of “late talkers”, infants who are not yet speaking by 2 years of age, will go on to develop language impairments. Currently, we have no reliable means of identifying these infants.

The overall objectives are to generate a coherent theoretically-driven dataset of cross-modal developmental neural and behavioural measures that should be applicable across European languages.
Since the project began we have recruited a cohort of 110 infants and we have taken EEG recordings at 2, 4, 5, 6, 7, 8, 9 and 11 months. Beginning at 12 months, home visits occur (at 12, 15, 18, 24 and 30 months) measuring vocabulary, phonology and grammar/syntax. The first infant that we recruited is already 24 months, the most recent infant recruit will finish the brain imaging components in October 2019. The intensive data collection protocols (each EEG session and home visit takes 2 hours per infant) means that we are keeping up with scoring and data entry but so far we have only conducted some pilot analyses. These are encouraging, as a subset of the cohort (N = 30) are showing significant predictive relations between our EEG measures and vocabulary development. However, until we have a full data set at each measurement point, these analyses remain preliminary.
During our exploratory data analyses, we have been testing out different methods for resynthesizing the speech signal as encoded by the infant’s electrophysiological brain responses (linear temporal response functions, multiway canonical correlation analysis, machine learning approaches like convoluted neural nets and EEG net). These new methods go beyond the state of the art when the proposal was funded in 2016.