This project addresses fundamental questions of cognitive neuroscience regarding the interactions among motivation, attention, learning, working memory, and cognitive control. The knowledge gained from this project and subsequent follow-on studies will have broad impacts on all of these areas of cognitive neuroscience, which are often studied in isolation. Further, understanding the mechanisms underlying relationships among motivation, attention, learning, and memory could have profound effects on the effectiveness of educational practices and other public policies. The procedures and knowledge developed here could readily be used to develop automated tools for individualized classroom or computer-based learning. A reliable method for quantitatively assessing each individual’s sensitivities to positive, negative, and loss-avoidance feedback, and then employing individually-tailored feedback in subsequent learning experiences should facilitate attention control and optimize learning across individuals. This project will enable testing an example of how such a tool might work.
The facility by which an agent learns from its environment is rooted in its willingness to pursue a goal, despite the cost in time and effort needed for its attainment. In weighing the presumptive costs and benefits, how ought this drive to pursue a goal be calculated by the brain in order to motivate the most beneficial behaviors, what are the neurobiological origins of this drive, and how does it go awry in cognitive disorders?
This project combines high-resolution imaging of blood vessels in the brain of an alert mouse with a careful, parametric approach to simple forms of learning.
This project combines behavioral experiments with a philosophical analysis of learning models to determine whether we integrate new information in a way that obeys the principles of rational inference.
This work will bring together scientific principles from peer learning in adults, machine learning, and surgical skill assessment to deliver a scalable methodology to augment technology to support humans learning complex skills.
We will use behavioral, psychophysiological, and neuroimaging techniques to explore neurophysiological markers of hazard detection in experienced versus inexperienced teenage drivers.
This project combines gene expression profiling using a novel high-throughput technique (single-cell RNA sequencing), computational analysis, and current theories of the cellular organization of a brain structure important in learning and memory to understand the mechanisms underlying learning.
This project will combine behavioral measures with a novel PET imaging application to investigate neural mechanisms underlying the relearning of taste preferences and food reward in bariatric surgery.
This project integrates expertise in neuroscience and early childhood education to explain how teacher and child capacities interact to shape cognitive and behavioral development in low-income children.
This project bridges educational theory, augmented reality novel gamification techniques, and multimodal machine learning to develop and refine a new augmented reality learning tool for human anatomy education.