Decoding the neural representation of self and person knowledge with multivariate pattern analysis and data‐driven approaches

Abstract

Multivariate pattern analysis and data‐driven approaches to understand 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 nontechnical 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. In this review, we highlight recent trends (e.g., analysis of social networks, decoding race and social groups, and 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. This article is categorized under: Neuroscience > Cognition

The neural representation of self is recapitulated in the brains of friends: A round-robin fMRI study.

“…The present study differs from these conceptually related findings in that we measure the multivariate similarity of spatial neural patterns during retrieval of self and person knowledge. Although both measures can be broadly construed as measures of similarity, it bears noting that they are fundamentally different signals with one having more to do with neuronal populations encoding features of a stimulus and one referring to the synchrony of neuronal timeseries which captures the rise and fall of activity over time in single regions or voxels as a function of a shared task (for a review of these and other methods see Wagner, Chavez & Broom, 2019).…”

Self-Other Representation in the Social Brain Reflects Social Connection

“…Additionally, social network members may be endowed with enhanced subjective value, which is frequently associated with the ventral mPFC (Wagner et al, 2019). Indeed, the self and subjective value may be intrinsically related (Tamir and Mitchell, 2012;Chavez et al, 2017) and associated with closeness to the self (Schmitz and Johnson, 2007;Murray et al, 2012).…”

A Guide To Representational Similarity Analysis for Social Neuroscience

“…RSA has also been used to reveal how the brain represents the richness and complexity of social knowledge such as the self (Chavez et al, 2017;Feng et al, 2018;Wagner et al, 2018) and others' mental states (Tamir et al, 2016;Thornton et al, 2019a;Thornton et al, 2019b). For instance, Tamir and colleagues asked subjects to rate mental state terms (e.g.…”

“…Another solution would be to use other non-correlation-based representational analyses (e.g. encoding analysis, pattern component analysis, multivariate pattern dependence, repetition suppression) to validate RSA results in the same study (Anzellotti and Coutanche, 2018;Diedrichsen and Kriegeskorte, 2017;Hatfield et al, 2016;Wagner et al, 2018).…”