scholarly journals Mortality threat mitigates interpersonal competition: an EEG-based hyperscanning study

Author(s):  
Xiaoyu Zhou ◽  
Yafeng Pan ◽  
Ruqian Zhang ◽  
Litian Bei ◽  
Xianchun Li

Abstract Awareness of death has been shown to influence human cognition and behavior. Yet, how mortality threat (MT) impacts our daily social behavior remains elusive. To address this issue, we developed a dyadic experimental model and recruited 86 adults (43 dyads) to complete two computer-based tasks (i.e. competitive and cooperative button-pressing). We manipulated dyads’ awareness of death [MT vs neutral control (NC)] and simultaneously measured their neurophysiological activity using electroencephalography during the task. Several fundamental observations were made. First, the MT group showed significantly attenuated competition and slightly promoted cooperation. Second, compared to NC, MT significantly decreased gamma-band inter-brain synchronization (IBS) in the competitive context, which was associated with increased subjective fear of death within dyads. Notably, those effects were context-specific: we did not observe comparable results in the cooperative context. Finally, a machine-learning approach was successfully used to discriminate between the MT and NC groups based on accumulated IBS. Together, these findings indicate that MT to some extent mitigates interpersonal competition, and such mitigation might be associated with changes in gamma-band IBS.

Author(s):  
Nenad Ivezic ◽  
James H. Garrett

AbstractThe goal of machine learning for artifact synthesis is the acquisition of the relationships among form, function, and behavior properties that can be used to determine more directly form attributes that satisfy design requirements. The proposed approach to synthesis knowledge acquisition and use (SKAU) described in this paper, called NETSYN, creates a function to estimate the probability of each possible value of each design property being used in a given design context. NETSYN uses a connectionist learning approach to acquire and represent this probability estimation function and exhibits good performance when tested on an artificial design problem. This paper presents the NETSYN approach for SKAU, a preliminary test of its capability, and a discussion of issues that need to be addressed in future work.


Author(s):  
Bjorn Burscher ◽  
Rens Vliegenthart ◽  
Claes H. De Vreese

Content analysis of political communication usually covers large amounts of material and makes the study of dynamics in issue salience a costly enterprise. In this article, we present a supervised machine learning approach for the automatic coding of policy issues, which we apply to news articles and parliamentary questions. Comparing computer-based annotations with human annotations shows that our method approaches the performance of human coders. Furthermore, we investigate the capability of an automatic coding tool, which is based on supervised machine learning, to generalize across contexts. We conclude by highlighting implications for methodological advances and empirical theory testing.


Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  

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