To study how the mind and brain work, we often isolate specific components of cognition -- such as perception, attention, learning, and memory -- at the risk of missing the forest for the trees. The overarching theme of my research is that these parts of the mind are inherently interactive, and that this interactivity is key for understanding the nature of any particular part. This perspective has been applied to several cognitive phenomena using a combination of functional neuroimaging and psychophysics.
As a case study, a major research focus in the lab concerns ‘statistical learning’, the remarkable ability of humans and other species to detect, represent, and exploit statistical regularities in the world around us without conscious awareness or intent. For example, we effortlessly learn the locations of objects in a room, the boundaries between words, and the sequence of landmarks on the way home. My research addresses fundamental questions about the nature of statistical learning, including: What is the input to statistical learning? What is stored in the mind as a result of statistical learning? How does statistical learning influence perception? While statistical learning develops over time, we are also interested in more immediate types of learning and memory, including perhaps the most basic form: reduced neural responses to repeated vs. novel things. This ‘repetition attenuation’ has been studied as an important consequence of the interaction between perception and memory, and has been used as a tool to understand the nature of visual representations. In a new direction for my research, we have begun exploring the neural scaffolding for interactions between perception, attention, learning, and memory as reflected in ‘functional connectivity’. We have found that seemingly random fluctuations are selectively shared across brain areas both during rest and in the background of tasks, and that the dynamics of these functional networks can be predictive of behavior and affected by cognitive state.