scholarly journals The neural representational space of social memory

2017 ◽  
Author(s):  
Sarah L. Dziura ◽  
James C. Thompson
Keyword(s):  
Social Networks ◽  
Human Brain ◽  
Incidental Learning ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Temporoparietal Junction ◽  
Representational Space ◽  
Analysis Technique ◽  
The Social ◽  
Multivariate Pattern

AbstractSocial functioning involves learning about the social networks in which we live and interact; knowing not just our friends, but also who is friends with our friends. Here we utilized a novel incidental learning paradigm and representational similarity analysis (RSA), a functional MRI multivariate pattern analysis technique, to examine the relationship between learning social networks and the brain's response to the faces within the networks. We found that accuracy of learning face pair relationships through observation is correlated with neural similarity patterns to those pairs in the left temporoparietal junction (TPJ), the left fusiform gyrus, and the subcallosal ventromedial prefrontal cortex (vmPFC), all areas previously implicated in social cognition. This model was also significant in portions of the cerebellum and thalamus. These results show that the similarity of neural patterns represent how accurately we understand the closeness of any two faces within a network, regardless of their true relationship. Our findings indicate that these areas of the brain not only process knowledge and understanding of others, but also support learning relations between individuals in groups.Significance StatementKnowledge of the relationships between people is an important skill that helps us interact in a highly social world. While much is known about how the human brain represents the identity, goals, and intentions of others, less is known about how we represent knowledge about social relationships between others. In this study, we used functional neuroimaging to demonstrate that patterns in human brain activity represent memory for recently learned social connections.

scholarly journals The Neural Representational Space of Social Memory

Open Mind ◽  
2019 ◽  
Vol 3 ◽  
pp. 1-12 ◽  
Author(s):  
Sarah L. Dziura ◽  
James C. Thompson
Keyword(s):  
Social Networks ◽  
Incidental Learning ◽  
Multivariate Pattern Analysis ◽  
Temporoparietal Junction ◽  
Representational Space ◽  
Analysis Technique ◽  
The Social ◽  
Multivariate Pattern ◽  
The Relationship ◽  
The Brain

Social functioning involves learning about the social networks in which we live and interact; knowing not just our friends, but also who is friends with our friends. This study utilized an incidental learning paradigm and representational similarity analysis (RSA), a functional MRI multivariate pattern analysis technique, to examine the relationship between learning social networks and the brain’s response to the faces within the networks. We found that accuracy of learning face pair relationships through observation is correlated with neural similarity patterns to those pairs in the left temporoparietal junction (TPJ), the left fusiform gyrus, and the subcallosal ventromedial prefrontal cortex (vmPFC), all areas previously implicated in social cognition. This model was also significant in portions of the cerebellum and thalamus. These results show that the similarity of neural patterns represent how accurately we understand the closeness of any two faces within a network. Our findings indicate that these areas of the brain not only process knowledge and understanding of others, but also support learning relations between individuals in groups.

scholarly journals Mental travel in the social domain

2019 ◽  
Author(s):  
Mordechai Hayman ◽  
Shahar Arzy
Keyword(s):  
Social Network ◽  
Anterior Cingulate Cortex ◽  
Brain Activity ◽  
Cingulate Cortex ◽  
Retrosplenial Cortex ◽  
Anterior Cingulate ◽  
Significant Other ◽  
Temporoparietal Junction ◽  
The Social ◽  
Famous Person

“Mental travel” is the ability to imagine oneself in different places and times and to adopt other people’s point of view (POV), also termed “Theory of Mind (ToM)”. While ToM has been extensively investigated, self-projection with respect to ones’ own and others’ social networks has yet to be systematically studied.Here we asked participants to “project” themselves to four different POVs: a significant other, a non-significant other, a famous-person, and their own-self. From each POV they were asked to rate the level of affiliation (closeness) to different individuals in the respective social network while undergoing functional MRI.Participants were always faster making judgments from their own POV compared to other POVs (self-projection effect) and for people who were personally closer to their adopted POV (self-reference effect). Brain activity at the medial prefrontal and anterior cingulate cortex in the self POV condition was found to be higher compared to all other conditions. Activity at the right temporoparietal junction and medial parietal cortex was found to distinguish between the personally related (self, significant- and non-significant others) and unrelated (famous-person) individuals within the social network. Regardless of the POV, the precuneus, anterior cingulate cortex, prefrontal cortex, and temporoparietal junction distinguished between relatively closer and distant people. Representational similarity analysis (RSA) implicated the left retrosplenial cortex as crucial for social distance processing across all POVs.

Carnivalesque Theory and Social Networks

2020 ◽  
pp. 270-288
Author(s):  
Sefer Kalaman ◽  
Mikail Batu
Keyword(s):  
Social Networks ◽  
Content Analysis ◽  
Social Life ◽  
Research Data ◽  
Analysis Technique ◽  
The Social

Carnivalesque theory has been used as a model and a structure in the works carried out in many fields such as communication, literature, and sociology. In fact, Carnivalesque appears in many environments/areas, particularly in the social networks, which are the manifestation of social life. This chapter examines social networks in the context of carnivalesque theory to reveal facts of carnivalesque in Twitter. Content analysis technique was used in the research. Research data came from 10 Twitter accounts which have a maximum number of followers in Turkey. These data were analyzed and examined in terms of grotesque, dialogism, carnival laughter, upside-down world, marketplace, and marketplace speech belonging to the carnivalesque theory. According to the findings, the structure of Twitter, which is one of the most popular social networks in Turkey, is largely similar to the structure of the carnival and features of carnivalesque theory.

Causes of social reward differences encoded in human brain

2012 ◽  
Vol 107 (5) ◽  
pp. 1403-1412 ◽  
Author(s):  
Alexander Vostroknutov ◽  
Philippe N. Tobler ◽  
Aldo Rustichini
Keyword(s):  
Negative Affect ◽  
Human Brain ◽  
Social Perception ◽  
Orbitofrontal Cortex ◽  
Brain Activity ◽  
Social Reward ◽  
Monetary Rewards ◽  
The Social ◽  
The Difference

Rewards may be due to skill, effort, and luck, and the social perception of inequality in rewards among individuals may depend on what produced the inequality. Rewards due to skill produce a conflict: higher outcomes of others in this case are considered deserved, and this counters incentives to reduce inequality. However, they also signal superior skill and for this reason induce strong negative affect in those who perform less, which increases the incentive to reduce the inequality. The neurobiological mechanisms underlying evaluation of rewards due to skill, effort, and luck are still unknown. We scanned brain activity of subjects as they perceived monetary rewards caused by skill, effort, or luck. Subjects could subtract from others. Subtraction was larger, everything else being equal, in luck but increased more as the difference in outcomes grew in skill. Similarly, reward-related activation in medial orbitofrontal cortex was more sensitive to the difference in relative outcomes in skill trials. Orbitofrontal activation reflecting comparative reward advantage predicted by how much subjects reduced unfavorable reward inequality later on in the trial. Thus medial orbitofrontal cortex activity reflects the causes of reward and predicts actions that reduce inequality.

scholarly journals Functional brain network architecture supporting the learning of social networks in humans

2019 ◽  
Author(s):  
Steven Tompson ◽  
Ari E Kahn ◽  
Emily B. Falk ◽  
Jean M Vettel ◽  
Danielle S Bassett
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Network ◽  
Brain Network ◽  
Temporoparietal Junction ◽  
Functional Brain ◽  
Network Learning ◽  
Functional Brain Network ◽  
The Social ◽  
The Impact

Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. From the behavioral data in both tasks, we found that learners were sensitive to the community structure of the networks, as evidenced by a slower reaction time on trials transitioning between clusters than on trials transitioning within a cluster. From the neuroimaging data collected during the social network learning task, we observed that the functional connectivity of the hippocampus and temporoparietal junction was significantly greater when transitioning between clusters than when transitioning within a cluster. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions during the social task than during the non-social task. Collectively, our results identify neurophysiological underpinnings of social versus non-social network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies.

scholarly journals Spontaneous Neural Encoding of Social Network Position

2017 ◽  
Author(s):  
Carolyn M. Parkinson ◽  
Adam M. Kleinbaum ◽  
Thalia Wheatley
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Human Brain ◽  
Brain Evolution ◽  
Neural Encoding ◽  
Form Complex ◽  
Network Information ◽  
Human Brain Evolution ◽  
The Social ◽  
Social Network Position

Humans form complex social networks that include numerous non-reproductive bonds with non-kin. Navigating these networks presents a considerable cognitive challenge thought to have comprised a driving force in human brain evolution. Yet, little is known about how and to what extent the human brain encodes the structure of the social networks in which it is embedded. By combining social network analysis and multi-voxel pattern analysis of functional magnetic resonance imaging (fMRI) data, we show that social network information about direct relationships, bonds between third parties, and aspects of the broader network topology is accurately perceived and automatically activated upon seeing a familiar other.

scholarly journals Decoding fairness motivations from multivariate brain activity patterns

2019 ◽  
Vol 14 (11) ◽  
pp. 1197-1207 ◽  
Author(s):  
Sebastian P H Speer ◽  
Maarten A S Boksem
Keyword(s):  
Brain Activity ◽  
Activity Patterns ◽  
Dictator Game ◽  
Well Being ◽  
Self Control ◽  
Selfish Behavior ◽  
Temporoparietal Junction ◽  
Dorsolateral Prefrontal ◽  
Multivariate Pattern ◽  
Decoding Accuracy

Abstract A preference for fairness may originate from prosocial or strategic motivations: we may wish to improve others’ well-being or avoid the repercussions of selfish behavior. Here, we used functional magnetic resonance imaging to identify neural patterns that dissociate these two motivations. Participants played both the ultimatum and dictator game (UG–DG) as proposers. Because responders can reject the offer in the UG, but not the DG, offers and neural patterns between the games should differ for strategic players but not prosocial players. Using multivariate pattern analysis, we found that the decoding accuracy of neural patterns associated with UG and DG decisions correlated significantly with differences in offers between games in regions associated with theory of mind (ToM), such as the temporoparietal junction, and cognitive control, such as the dorsolateral prefrontal cortex and inferior frontal cortex. We conclude that individual differences in prosocial behavior may be driven by variations in the degree to which self-control and ToM processes are engaged during decision-making such that the extent to which these processes are engaged is indicative of either selfish or prosocial motivations.

scholarly journals Repetition suppression: a means to index neural representations using BOLD?

2016 ◽  
Vol 371 (1705) ◽  
pp. 20150355 ◽  
Author(s):  
Helen C. Barron ◽  
Mona M. Garvert ◽  
Timothy E. J. Behrens
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
Multivariate Techniques ◽  
Physiological Basis ◽  
Non Invasive ◽  
Repetition Suppression ◽  
Neural Representations ◽  
Neural Processes ◽  
Neural Populations ◽  
Multivariate Pattern

Understanding how the human brain gives rise to complex cognitive processes remains one of the biggest challenges of contemporary neuroscience. While invasive recording in animal models can provide insight into neural processes that are conserved across species, our understanding of cognition more broadly relies upon investigation of the human brain itself. There is therefore an imperative to establish non-invasive tools that allow human brain activity to be measured at high spatial and temporal resolution. In recent years, various attempts have been made to refine the coarse signal available in functional magnetic resonance imaging (fMRI), providing a means to investigate neural activity at the meso-scale, i.e. at the level of neural populations. The most widely used techniques include repetition suppression and multivariate pattern analysis. Human neuroscience can now use these techniques to investigate how representations are encoded across neural populations and transformed by relevant computations. Here, we review the physiological basis, applications and limitations of fMRI repetition suppression with a brief comparison to multivariate techniques. By doing so, we show how fMRI repetition suppression holds promise as a tool to reveal complex neural mechanisms that underlie human cognitive function. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

scholarly journals Individualizing Representational Similarity Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Seth M. Levine ◽  
Jens V. Schwarzbach
Keyword(s):  
Individual Differences ◽  
Cognitive Neuroscience ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Similarity Analysis ◽  
Representational Similarity Analysis ◽  
Analysis Technique ◽  
Neuroimaging Data ◽  
Translational Studies ◽  
Transfer Of Information

Representational similarity analysis (RSA) is a popular multivariate analysis technique in cognitive neuroscience that uses functional neuroimaging to investigate the informational content encoded in brain activity. As RSA is increasingly being used to investigate more clinically-geared questions, the focus of such translational studies turns toward the importance of individual differences and their optimization within the experimental design. In this perspective, we focus on two design aspects: applying individual vs. averaged behavioral dissimilarity matrices to multiple participants' neuroimaging data and ensuring the congruency between tasks when measuring behavioral and neural representational spaces. Incorporating these methods permits the detection of individual differences in representational spaces and yields a better-defined transfer of information from representational spaces onto multivoxel patterns. Such design adaptations are prerequisites for optimal translation of RSA to the field of precision psychiatry.

scholarly journals Absence of covert face valuation in Autism

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fabien Vinckier ◽  
Mathias Pessiglione ◽  
Baudouin Forgeot d’Arc
Keyword(s):  
Functional Neuroimaging ◽  
Brain Activity ◽  
Stereotyped Behavior ◽  
Judgment Task ◽  
Size Estimation ◽  
Clinical Criteria ◽  
Social Reciprocity ◽  
The Social ◽  
The Impact ◽  
The Brain

AbstractAutism is a neurodevelopmental condition defined on clinical criteria related to diminished social reciprocity and stereotyped behavior. An influential view explains autism as a social motivation disorder characterized by less attention paid to the social environment and less pleasure experienced with social rewards. However, experimental attempts to validate this theory, by testing the impact of social reward on behavioral choice and brain activity, has yielded mixed results, possibly due to variations in how explicit instructions were about task goals. Here, we specified the putative motivation deficit as an absence of spontaneous valuation in the social domain, unexplained by inattention and correctible by explicit instruction. Since such deficit cannot be assessed with behavioral measures, we used functional neuroimaging (fMRI) to readout covert subjective values, assigned to social and nonsocial stimuli (faces and objects), either explicitly asked to participants (during a likeability judgment task) or not (during age or size estimation tasks). Value-related neural activity observed for objects, or for faces under explicit instructions, was very similar in autistic and control participants, with an activation peak in the ventromedial prefrontal cortex (vmPFC), known as a key node of the brain valuation system. The only difference observed in autistic participants was an absence of the spontaneous valuation normally triggered by faces, even when they were attended for age estimation. Our findings, therefore, suggest that in autism, social stimuli might fail to trigger the automatic activation of the brain valuation system.

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