Our laboratory is located in the Center for Advanced Brain Imaging at the Georgia Institute of Technology in the School of Psychology. We use a variety of experimental techniques, including: behavioral testing, functional magnetic resonance imaging (fMRI), and patient testing to investigate the neural mechanisms for vision, attention, memory, learning and cognitive control. Check out some of our current projects below.
Task Set Influences on Cognitive Control
Cognitive control refers to the set of processes by which we direct our actions toward a specific goal. At the most basic level, control processes allow us to translate a presented stimulus into an appropriate motor action. However, these processes quickly become more complex when trying to understand more involved behaviors that can depend on present situational context. Hommel (2004) suggests that these more complex behaviors are guided by “event files”, or task sets, that create transient connections between the potential stimuli, responses, and situational contingencies that may be relevant to a task. Furthermore, previous research in our lab, in conjunction with the Hazeltine lab at the University of Iowa (Hazeltine et al, 2011) suggest that these task sets are developed automatically according to perceived boundaries of a task. These task sets serve as a “road map” for control processes during a task, and multiple task sets can be coordinated to produce our observed behaviors. Our lab seeks to understand the patterns of brain activity that relate to these task sets and to understand how different task boundary perceptions result in different activity patterns, even within task designs.
Network Dynamics of Complex Cognition
Previous research between our lab and Shella Keilholz’s lab at Emory (Thompson et al., 2013) has demonstrated that dynamic fluctuations in brain activity and connectivity can predict trial-by-trial performance on a sustained attention task. One of the goals in our lab is to extend this research by using fMRI to investigate if fluctuations in large-scale brain networks predict performance on other, more complex cognitive tasks (e.g., memory, attention, control, etc). By investigating how neural activity and brain connectivity fluctuates randomly and in response to a task, we are hoping to understand the underlying neural architecture of cognition.