Decoding the Neural Representation of Self and Person Knowledge with Multivariate Pattern Analysis and Data Driven Approaches

Multivariate pattern analysis and data driven approaches to understanding how the human brain encodes sensory information and higher level conceptual knowledge have become increasingly dominant in visual and cognitive neuroscience, however it is only in recent years that these methods have been applied to the domain of social information processing. This review examines recent research in the field of social cognitive neuroscience focusing on how multivariate pattern analysis (e.g., pattern classification, representational similarity analysis) and data-driven methods (e.g, reverse correlation, intersubject correlation) have been used to decode and characterize high-level information about the self, other persons and social groups. We begin with a review of what is known about how self-referential processing and person perception are represented in the medial prefrontal cortex based on conventional activation-based neuroimaging approaches. This is followed by a non-technical overview of current multivariate pattern-based and data-driven neuroimaging methods designed to characterize and/or decode neural representations. The remainder of the review focuses on examining how these methods have been applied to the topic of self, person perception and the perception of social groups. Throughout, we highlight recent trends (e.g., analysis of social networks, decoding race and social groups, the use of naturalistic stimuli) and discuss several theoretical challenges that arise from the application of these new methods to the question of how the brain represents knowledge about the self and others.