Yale University, USA.
Different cognitive processes like perception, attention, learning, and memory – and the underlying brain systems that support them – are often studied in isolation. This is productive and necessary, but my lab takes the complementary perspective of trying to understand how these systems interact. The hope is that this will help elucidate the constraints and functions of individual systems and also help produce a more integrated understanding of mind and brain. We use a variety of techniques, including psychophysical experiments to characterize behaviors of interest, functional magnetic resonance imaging to explore the underlying circuits and representations, case studies of patients with brain damage to provide converging evidence, and computational approaches (machine learning, graph theory, neural networks, real-time analysis) to formalize theories and generate quantitative predictions. As an example, we have worked extensively on ‘statistical learning’, the process by which humans extract regularities from sensory input. High-resolution imaging revealed that subfields of the hippocampus – typically linked to the encoding of discrete experiences – play an important role in such learning of commonalities across experiences, and computational modeling provided a theoretical explanation of the mechanism. We study a variety of other topics as well, including predictive coding, memory-guided attention, and, most recently, the development of brain function in infants and toddlers.