Neural network integration during the perception of in-group and out-group members
نویسندگان
چکیده
Group biases guide social interactions by promoting in-group favouritism, but the neural mechanisms underpinning group biases remain unclear. While neuroscience research has shown that distributed brain circuits are associated with seeing in-group and out-group members as "us" and "them", it is less clear how these networks exchange signals. This fMRI study uses functional connectivity analyses to investigate the contribution of functional integration to group bias modulation of person perception. Participants were assigned to an arbitrary group and during scanning they observed bodies of in-group or out-group members that cued the recall of positive or negative social knowledge. The results showed that functional coupling between perceptual and cognitive neural networks is tuned to particular combinations of group membership and social knowledge valence. Specifically, coupling between body perception and theory-of-mind networks is biased towards seeing a person that had previously been paired with information consistent with group bias (positive for in-group and negative for out-group). This demonstrates how brain regions associated with visual analysis of others and belief reasoning exchange and integrate signals when evaluating in-group and out-group members. The results update models of person perception by showing how and when interplay occurs between perceptual and extended systems when developing a representation of another person.
منابع مشابه
Forecasting Job Burnout among University Faculty Members of Yazd Payame Noor University Using Artificial Neural Network Technique
Background: Faculty members are one of the main factors in the higher education system, that high level of occupational stress caused by educational, research, and executive duties makes them exposed to burnout. The purpose of this study is Forecasting burnout of faculty members of Yazd Payame Noor University using artificial neural network technique. Methods: The present research is descripti...
متن کاملA neural mass model of CA1-CA3 neural network and studying sharp wave ripples
We spend one third of our life in sleep. The interesting point about the sleep is that the neurons are not quiescent during sleeping and they show synchronous oscillations at different regions. Especially sharp wave ripples are observed in the hippocampus. Here, we propose a simple phenomenological neural mass model for the CA1-CA3 network of the hippocampus considering the spike frequency adap...
متن کاملComparing Two Methods of Neural Networks to Evaluate Dead Oil Viscosity
Reservoir characterization and asset management require comprehensive information about formation fluids. In fact, it is not possible to find accurate solutions to many petroleum engineering problems without having accurate pressure-volume-temperature (PVT) data. Traditionally, fluid information has been obtained by capturing samples and then by measuring the PVT properties in a laboratory. In ...
متن کاملIntegration of artificial neural network and geographic information system applications in simulating groundwater quality
Background: Although experiments on water quality are time consuming and expensive, models are often employed as supplement to simulate water quality. Artificial neural network (ANN) is an efficient tool in hydrologic studies, yet it cannot predetermine its results in the forms of maps and geo-referenced data. Methods: In this study, ANN was applied to simulate groundwater quality ...
متن کاملNeural correlates of biased social fear learning and interaction in an intergroup context
Associations linking a fearful experience to a member of a social group other than one's own (out-group) are more resistant to change than corresponding associations to a member of one's own (in-group) (Olsson et al., 2005; Kubota et al., 2012), providing a possible link to discriminative behavior. Using a fear conditioning paradigm, we investigated the neural activity underlying aversive learn...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Neuropsychologia
دوره 106 شماره
صفحات -
تاریخ انتشار 2017