Abstract

Humans make eye-contact to extract information about other people’s mental states, recruiting dedicated brain networks that process information about the self and others. Recent studies show that eye-contact increases the synchronization between two brains but do not consider its effects on activity within single brains. Here we investigate how eye-contact affects the frequency and direction of the synchronization within and between two brains and the corresponding network characteristics. We also evaluate the functional relevance of eye-contact networks by comparing inter- and intra-brain networks of friends vs. strangers and the direction of synchronization between leaders and followers. We show that eye-contact increases higher inter- and intra-brain synchronization in the gamma frequency band. Network analysis reveals that some brain areas serve as hubs linking within- and between-brain networks. During eye-contact, friends show higher inter-brain synchronization than strangers. Dyads with clear leader/follower roles demonstrate higher synchronization from leader to follower in the alpha frequency band. Importantly, eye-contact affects synchronization between brains more than within brains, demonstrating that eye-contact is an inherently social signal. Future work should elucidate the causal mechanisms behind eye-contact induced synchronization.

Friends making eye-contact have higher inter-brain synchronization than strangers. Eye-contact affects neural synchronization between brains more than within a brain, highlighting that eye-contact is an inherently social signal.

Details

Title
Social synchronization of brain activity increases during eye-contact
Author
Luft Caroline Di Bernardi 1   VIAFID ORCID Logo  ; Zioga Ioanna 2 ; Giannopoulos Anastasios 3   VIAFID ORCID Logo  ; Di Bona Gabriele 4   VIAFID ORCID Logo  ; Binetti, Nicola 1 ; Civilini Andrea 4 ; Latora Vito 5   VIAFID ORCID Logo  ; Mareschal, Isabelle 1 

 University of London, School of Biological and Behavioural Sciences, Queen Mary, London, United Kingdom (GRID:grid.4464.2) (ISNI:0000 0001 2161 2573) 
 University of London, School of Biological and Behavioural Sciences, Queen Mary, London, United Kingdom (GRID:grid.4464.2) (ISNI:0000 0001 2161 2573); Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands (GRID:grid.5590.9) (ISNI:0000000122931605) 
 National Technical University of Athens (NTUA), School of Electrical and Computer Engineering, Athens, Greece (GRID:grid.4241.3) (ISNI:0000 0001 2185 9808) 
 Queen Mary University of London, School of Mathematical Sciences, London, United Kingdom (GRID:grid.4868.2) (ISNI:0000 0001 2171 1133) 
 Queen Mary University of London, School of Mathematical Sciences, London, United Kingdom (GRID:grid.4868.2) (ISNI:0000 0001 2171 1133); Università di Catania and INFN, Dipartimento di Fisica ed Astronomia, Catania, Italy (GRID:grid.8158.4) (ISNI:0000 0004 1757 1969); The British Library, The Alan Turing Institute, London, United Kingdom (GRID:grid.36212.34) (ISNI:0000 0001 2308 1542); Complexity Science Hub, Vienna, Austria (GRID:grid.484678.1) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
23993642
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2659408673
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.