Quantifying individual differences from complex datasets in developmental psychology

Developmental psychology fundamentally relies on the robust measurement of individual differences - capturing variables at an individual participant level - in order to formulate hypotheses about what children know, how their psychology changes over time, and to characterize the best predictors of their long-term educational, health, and psychological outcomes. But, newborns, infants, and young children are notoriously difficult experiment participants, leading to two major challenges in data analysis: (1) developmental data can be complex and multivariate but simultaneously limited by the need for short testing sessions that collect sparse data (e.g., neural recordings on the scalp for durations of 15 minutes or less); and (2) data are often censored or missing, as many children may stop paying attention to the task, may need to be fed, might create too many motion artifacts, etc. The goal of this project is to capitalize on hundreds of existing data points from the UBC Department of Psychology developmental psychology labs - including eye-tracking and neuroimaging data from infants and toddlers - to leverage existing solutions in machine learning and statistical survival analysis and develop novel analytical pipelines for measuring individual differences in these heterogeneous datasets.