Jill O’Reilly is a Professor of Psychology at the University of Oxford. She is interested in attention, decision making, and cognitive control, in particular in developing mechanistic descriptions of these processes that use computational or mathematical models, and relating them to brain activity as measured with neuroimaging.
Rogier obtained his PhD (highest honors) from the Donders Centre of the Radboud University in 2006 with a thesis on the contributions of human premotor cortex to action. He then moved to University College London to work with Sven Bestmann on computational models to analyze EEG and TMS data. Since 2007 Rogier has been working at the University of Oxford, first as a Marie Curie Intra-European Fellow with Matthew Rushworth and more recently as an independent PI.
Rogier is interested in understanding how the brains of different animal species' are differently organized and how this affects the species' behavioral repertoire. To this end, he and in team build tools for comparative neuroscience and apply them both to understand brain evolution and to help improve translational neuroscience.
Dr. Izquierdo's main research interests center on understanding the brain mechanisms of flexible reinforcement learning and value-based decisions. Specifically, this involves exploring the impact of costs and determining the relative value of options. To that end, her lab studies these processes using a combination of behavioral, molecular, pharmacological, computational, and in vivo imaging and recording methods. More recently her lab has investigated the neurobiological basis for the role of uncertainty, risk, and reinforcement history on learning and choice. A better understanding of the basic neural mechanisms in reinforcement learning and choice behavior may contribute to our knowledge of behavioral and substance addictions, in particular.
Professor Christopher Summerfield’s work lies at the intersection of cognitive science, neuroscience, AI research, and social science. In his Oxford group, they study how humans learn and decide, from a behavioural, comptuational and neural standpoint. They use neural network models as theories of human learning and cognition, and as objects of study in their own right. They also use deep networks as tools for assisting human behaviour, such as accelerating learning and helping humans cooperate and find agreement.