Assistant Professor, Department of Psychology
Ph.D. University of Texas at Austin
M.A. Charles University in Prague
Research Interests: Cognitive-Neuroscience, Memory
Overview: Memory allows us to use past experiences to navigate novel situations and inform future decisions. Because every event is unique, we need to use memory flexibly, drawing upon multiple relevant experiences to anticipate future judgments. Brain and Memory Lab studies how memories are formed and how they are linked to each other to create internal representations of the world that can guide our behavior. We investigate how different memory systems are implemented in the brain, how they represent information, and how they interact. In the quest for discovery, we rely on computer-based experiments, cognitive models of behavior, and advanced functional MRI methods.
My research focuses on how we build complex knowledge representations—such as schemas, cognitive maps or concepts—from simple learning experiences. Stacking memories as building blocks, we form knowledge that transcend direct experience, allowing us to use the memory for the past to guide behavior in the future. I am especially interested how the hippocampus—a brain structure critical for memory for individual events in our lives—interacts with the prefrontal cortex and other memory systems to support the flexible use of experience. My primary research tools include computer-based experiments, formal models of behavior, and advanced functional MRI methods.
Multivariate neural signatures for health neuroscience: Assessing spontaneous regulation during food choice.
Soc Cogn Affect Neurosci. 2020 Jan 28;:
Authors: Cosme D, Zeithamova D, Stice E, Berkman ET
Establishing links between neural systems and health can be challenging since there isn't a one-to-one mapping between brain regions and psychological states. Building sensitive and specific predictive models of health-relevant constructs using multivariate activation patterns of brain activation is a promising new direction. We illustrate the potential of this approach by building two 'neural signatures' of food craving regulation using multivariate machine learning and, for comparison, a univariate contrast. We applied the signatures to two large validation samples of overweight adults who completed tasks measuring craving regulation ability and valuation during food choice. Across these samples, the machine learning signature was more reliable. This signature decoded craving regulation from food viewing and higher signature expression was associated with less craving. During food choice, expression of the regulation signature was stronger for unhealthy foods and inversely related to subjective value, indicating that participants engaged in craving regulation despite never being instructed to control their cravings. Neural signatures thus have the potential to measure spontaneous engagement of mental processes in the absence of explicit instruction, affording greater ecological validity. We close by discussing the opportunities and challenges of this approach, emphasizing what machine learning tools bring to the field of health neuroscience.
PMID: 31993654 [PubMed - as supplied by publisher]