Spontaneous neural encoding of social network position

Parkinson, Carolyn; Kleinbaum, Adam M.; Wheatley, Thalia

doi:10.1038/s41562-017-0072

Cited by 218 publications

(146 citation statements)

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“…(d). Characteristics of an familiar individual's social network position, such as centrality, can be decoded from patterns of neural activity associated with short video segments of each individual (Parkinson, Kleinbaum, & Wheatley, 2017) findings serve as strong initial evidence that the information represented in the MFPC during the perception of familiar identities is related to our social knowledge (e.g., traits, liking, and relationships) of these different persons. Another form of person knowledge not traditionally a part of standard social psychological theories of person perception is social network position.…”

Section: Decoding the Neural Representation Of Person Knowledgementioning

confidence: 76%

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

Wagner

Chavez

Broom

2018

WIRES Cognitive Science

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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

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Section: Decoding the Neural Representation Of Person Knowledgementioning

confidence: 76%

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

Wagner

Chavez

Broom

2018

WIRES Cognitive Science

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“…Moving forward, future research may replicate and extend these results with other assessments of social network integration, such as those used in formal social network analyses (Parkinson, Kleinbaum, & Wheatley, 2017).…”

Section: Social Working Memory and The Default Network 27mentioning

confidence: 77%

Theory of Minds: Managing mental state inferences in working memory is associated with the dorsomedial subsystem of the default network and social integration

Meyer

Collier

2019

Preprint

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We often interact with multiple people at a time and consider their various points-of-view to facilitate smooth social interaction. Yet, how our brains track multiple mental states at once, and whether skill in this domain links to navigating real-world social interactions, remains underspecified. To fill this gap, we developed a novel social working memory paradigm in which participants manage two- or four-people’s mental states in working memory, as well as control trials in which they alphabetize two- or four-people’s names in working memory. In Study 1, we found that the dorsomedial subsystem of the default network shows relative increases in activity with more mental states managed in working memory. In contrast, this subsystem shows relative decreases in activity with more non-mental state information (the number of names alphabetized) managed in working memory. In Study 2, only individual differences in managing mental states in working memory, specifically on trials that posed the greatest mental state load to working memory, correlated with social network integration. Collectively, these findings add further support to the hypothesis that social working memory relies on partially distinct brain systems and may be a key ingredient to success in a social world.

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“…In fact, RSA can be used to compare any two different representations, as information about pattern representations are abstracted away from the modality in which they were first acquired, forced into a common space, and then compared. To this point, prior research has used RSA to compare voxelwise patterns to behavioral patterns (Parkinson, Kleinbaum, & Wheatley, 2017), behavioral patterns to other behavioral patterns (Brooks & Freeman, 2018;Stolier, Hehman, Keller, Walker, & Freeman, 2018), and, most importantly for our purposes, representations of brain connectivity patterns (Lee, Miernicki, & Telzer, 2017). We built upon and extended these prior implementations of RSA for our purposes in comparing network connectivity states between task conditions.…”

Section: Star * Methodsmentioning

confidence: 99%

Characterizing the Network Architecture of Emotion Regulation Neurodevelopment

Moreira¹,

McLaughlin

Silvers³

2019

Preprint

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The ability to regulate emotions is key to goal attainment and wellbeing. Although much has been discovered about how the human brain develops to support the acquisition of emotion regulation, very little of this work has leveraged information encoded in whole-brain networks.Here we employed a network neuroscience framework in conjunction with machine learning to: (i) parse the neural underpinnings of chronological age and emotion regulation skill acquisition, and (ii) build a working taxonomy of brain network activity supporting emotion regulation in youth. We were able to predict age and emotion regulation ability using metrics from six wholebrain networks during an emotion regulation task. Further, by leveraging analytic techniques traditionally used in evolutionary biology (e.g., cophenetic correlations), we were able to demonstrate that brain networks evince reliable taxonomic organization to meet emotion regulation demands in youth. This work shows that meaningful information about emotion regulation development is encoded in whole-brain network activity, and opens up a realm of possibilities for network neuroscience applications in the study of emotion regulation development.evolutionary biology (agglomerative clustering, cophenetic correlation coefficient) to build a working taxonomy of brain networks as they pertain to the development of emotion regulation.

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Spontaneous neural encoding of social network position

Cited by 218 publications

References 50 publications

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

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

Theory of Minds: Managing mental state inferences in working memory is associated with the dorsomedial subsystem of the default network and social integration

Characterizing the Network Architecture of Emotion Regulation Neurodevelopment

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