CRCNS: Dense longitudinal neuroimaging to evaluate learning in childhood

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Understanding how learning occurs in early childhood has the potential to transform our understanding of human learning and our approach to building intelligent machines, yet critical windows in early childhood remain under-sampled and consequently provide little insight concerning learning. One fundamental and long-standing question in human learning is the process by which neural specialization for visual letter and digit processing emerges in the first grade. This knowledge is critical for addressing public health concerns related to reading and math literacy because first-grade letter and digit knowledge are the strongest predictors of future reading and math abilities, and children who fall behind in reading and math in elementary school will likely experience medical and financial instability as adults. This project employs a multi-level approach to understanding learning in childhood that will support critical advancements in several disciplines, including human and artificial learning, developmental and cognitive neuroscience, educational neuroscience, neuroimaging methods, computer vision, and learning sciences broadly. The first aim is to create and distribute a large corpus of images from Sesame Street episodes annotated for educational content, such as letters and digits, as well as for other common object categories. The image corpus will be the first to capture the visual statistics of child learners and can be used to train different artificial learning architectures to better understand human learning. The second aim is to collect, preprocess, and distribute a dense longitudinal MRI dataset of brain structure and function sampled at multiple time points throughout the first grade year. The dense longitudinal MRI dataset will provide experimentally measured brain responses to images from the Sesame Street corpus that will be of benefit for understanding human learning and of appropriate scale for constraining artificial learning architectures. The third aim is to evaluate the emergence of selective neural processing for letters and digits as learning occurs throughout the first year of schooling. This aim will address an open question in human learning concerning the process by which neural specialization for letters and digits emerges, namely the role of the motor system in emerging specialization. Understanding the time course of changes in brain function and structure during early learning is critical for developing accurate predictors of long-term life outcomes and for identifying sensitive windows of great plasticity to optimize intervention timelines.