At SVNDL we want to learn more about how infants and children develop different visual abilities — like detecting a face or keeping track of changing images — and how changes in the brain guide the development of these abilities. One of the ways we find out more about how a baby or child brain functions is from patterns observed in the electrical activity of the brain while the baby or child views images on a computer screen. We use a safe and non-invasive procedure called EEG (electroencephalography) to measure brain activity.

EEG involves placing a mildly wet net of sensors on the head. Each sensor rests on the scalp and can be thought of like a little microphone that picks up the activity from the brain. If you decide to participate, your visit will take about 45-60 minutes, but the procedure itself is no more than 15 minutes. You will be with your child the entire time. Many parents have a fun experience in the lab and enjoy learning more about their baby/child!

If you are interested in participating or would like to get more information about our studies, please contact us!  We are happy to answer any questions you may have. Without your participation we wouldn’t be able to answer these important developmental questions, so we appreciate your support and hope to see you soon!

We are currently looking for: Children aged 3-6 months and 3-7 years. (updated August 2022)

You can call 650-736-2793 or send an email at svndl.studies@lists.stanford.edu.

The researcher currently in charge of infant studies is Dr. Tony Norcia.

For many clinical conditions or forms of eye disease, early detection promises access to the most effective means of treatment. With help from Dr. Jeffrey Goldberg and other colleagues at Stanford’s Department of Ophthalmology, this study aims to develop a screening test for detecting early stage sof glaucoma. EEG brain-wave comparisons between clinical participants and those without eye disease will illuminate patterns in visual processing connected to developing stages of glaucoma. Participants with normal or corrected vision are needed to test different versions of the screening test.

Problem Statement: Deep neural networks have emerged as leading models for predicting neural data from a variety of brain areas and species. Here we would like to explore whether this modeling framework can be used to predict how neural representations of visual stimuli change over development.
Core research idea:  We will approach the modeling in two ways, one is to build models that represent the initial conditions of  the visual cortex prior to the onset of visual experience and the second is to use  the training phase of models that differ in their architectures and training rules as models of human brain development.  Critically, to test these models, we will acquire rich, high temporal resolution data sets from developing human infants using high-density EEG recordings.