Dynamic sensorimotor patterns during parent-child interaction: A cross-syndrome study

We are seeking a PhD candidate with a strong interest in one or more of the following areas: developmental science, visual/attentional/language/motor development, machine learning, machine vision, egocentric vision, and/or atypical development to jointly shape a research project focused on understanding the embodied nature of human development.

Human development is a dynamic process that involves complex and rapidly changing interactions between individuals and their environments. Notably, however, most of our current knowledge about early development has come from highly constrained contexts such as screen-based tasks and standardized assessments, and may not accurately capture development in the richer, more variable environments that infants and young children encounter daily. To close this gap, developmental scientists have recently created innovative and transformative approaches to studying infant and toddler development in richer environments, with parent-child interaction at the core, by building on advances in head-mounted eye-tracking/cameras. The aim of this PhD project is to apply this revolutionary approach to understanding development in young children with atypical constraints, in particular genetic syndromes such as Down syndrome and Williams syndrome.

The PhD candidate will capitalise on a unique existing dataset from young children with these genetic syndromes as well as typically developing toddlers. This dataset includes:

• Head-mounted eye-tracking during naturalistic parent-child interaction (both parent and child)
• Attentional screen-based eye-tracking tasks
• Language Environment Analysis (LENA) recordings
• Standardized developmental tests
• Parent interviews
• Questionnaires

Jointly with the supervisors, the PhD candidate will have an opportunity to identify and shape research questions around understanding sensorimotor patterns during parent-child interaction.

Based on the strengths and interests of the PhD candidate, the dataset can be extended by a longitudinal follow up of the same children, inclusion of other atypically developing groups, younger age groups, and/or the same age groups studied in a different context and/or with different methods. Equally, a focus on harnessing machine learning, machine vision, and egocentric vision to provide a more in depth understanding is welcome.

Depending on the exact direction the project will take, further supervisory input may be sought from the School of Optometry and Vision Sciences, the School of Computer Science & Informatics, and/or the School of Engineering.

Informal enquiries can be made to Dr Hana D’Souza ([Email Address Removed]).

Home students are UK Nationals and EU students who can satisfy UK residency requirements (students must have been in the UK for >3 years before start of course).

As only a limited number of studentships are available across the Open School competition and a very high standard of applications is typically received, the successful applicants are likely to have a very good first degree (a First or Upper Second class BSc Honours or equivalent) and/or be distinguished by having relevant research experience.