Research Interests and Publications
Among the most defining characteristics of our species is our capacity for a rich sense of self and depth of our social cognition. How does the human brain build models of ourselves and other people, and how do we use this information to guide our behavior in the real world? My research aims to better understand the biological mechanisms and individual differences in these domains of psychology. Specifically, I am interested in the shared and dissociable psychological processes that underlie self-representation and social cognition and their representation in the brain. Employing methods such as multimodal neuroimaging (e.g., fMRI and DTI) and machine learning, my work investigates how these processes are reflected in the structure and function of distributed networks of the brain and how they predict individual differences in each domain. As such, research in my lab broadly draws on theoretical and methodological approaches from personality and social psychology, cognitive neuroscience, evolutionary theory, and data science.
Dr. Chavez is not accepting new graduate students for Fall 2020.
Wagner, D.D., Chavez, R.S., & Broom, T.W. (2019). Decoding the neural representation of self and person knowledge with multivariate pattern analysis and data-driven approaches. Wiley Interdisciplinary Reviews: Cognitive Science, 10(1), e1482.
Chavez, R.S., Heatherton, T.H., & Wagner, D.D. (2017). Neural population decoding reveals the intrinsic positivity of the self. Cerebral Cortex, 11(1), 5222-5229.
Chavez, R.S. & Heatherton, T.H. (2017). Structural integrity of frontostriatal connections predicts longitudinal changes in self-esteem. Social Neuroscience, 12(3), 280-286.
Chavez, R.S. & Heatherton, T.H. (2015). Multimodal frontostriatal connectivity underlies individual differences in self-esteem. Social Cognitive and Affective Neuroscience, 10(3), 364-370.
Chavez, R.S. & Heatherton, T.H. (2015). Representational similarity of social and valence information in the medial pFC. Journal of Cognitive Neuroscience, 27(1), 73-82.