Literature DB >> 32713296

Collective information processing in human phase separation.

Bertrand Jayles1,2, Ramón Escobedo2, Roberto Pasqua3, Christophe Zanon3, Adrien Blanchet4,5, Matthieu Roy3, Gilles Tredan3, Guy Theraulaz2,5, Clément Sire1.   

Abstract

In our digital societies, individuals massively interact through digital interfaces whose impact on collective dynamics can be important. In particular, the combination of social media filters and recommender systems can lead to the emergence of polarized and fragmented groups. In some social contexts, such segregation processes of human groups have been shown to share similarities with phase separation phenomena in physics. Here, we study the impact of information filtering on collective segregation behaviour of human groups. We report a series of experiments where groups of 22 subjects have to perform a collective segregation task that mimics the tendency of individuals to bond with other similar individuals. More precisely, the participants are each assigned a colour (red or blue) unknown to them, and have to regroup with other subjects sharing the same colour. To assist them, they are equipped with an artificial sensory device capable of detecting the majority colour in their 'environment' (defined as their k nearest neighbours, unbeknownst to them), for which we control the perception range, k = 1, 3, 5, 7, 9, 11, 13. We study the separation dynamics (emergence of unicolour groups) and the properties of the final state, and show that the value of k controls the quality of the segregation, although the subjects are totally unaware of the precise definition of the 'environment'. We also find that there is a perception range k = 7 above which the ability of the group to segregate does not improve. We introduce a model that precisely describes the random motion of a group of pedestrians in a confined space, and which faithfully reproduces and allows interpretation of the results of the segregation experiments. Finally, we discuss the strong and precise analogy between our experiment and the phase separation of two immiscible materials at very low temperature. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.

Entities:  

Keywords:  collective human behaviour; collective information processing; collective motion; computational modelling; phase separation

Mesh:

Year:  2020        PMID: 32713296      PMCID: PMC7423375          DOI: 10.1098/rstb.2019.0801

Source DB:  PubMed          Journal:  Philos Trans R Soc Lond B Biol Sci        ISSN: 0962-8436            Impact factor:   6.237


  10 in total

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3.  A data-driven method for reconstructing and modelling social interactions in moving animal groups.

Authors:  R Escobedo; V Lecheval; V Papaspyros; F Bonnet; F Mondada; C Sire; G Theraulaz
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-07-27       Impact factor: 6.237

4.  Interaction ruling animal collective behavior depends on topological rather than metric distance: evidence from a field study.

Authors:  M Ballerini; N Cabibbo; R Candelier; A Cavagna; E Cisbani; I Giardina; V Lecomte; A Orlandi; G Parisi; A Procaccini; M Viale; V Zdravkovic
Journal:  Proc Natl Acad Sci U S A       Date:  2008-01-28       Impact factor: 11.205

5.  Spatially balanced topological interaction grants optimal cohesion in flocking models.

Authors:  Marcelo Camperi; Andrea Cavagna; Irene Giardina; Giorgio Parisi; Edmondo Silvestri
Journal:  Interface Focus       Date:  2012-08-08       Impact factor: 3.906

6.  Collective information processing and pattern formation in swarms, flocks, and crowds.

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Journal:  Top Cogn Sci       Date:  2009-04-06

7.  Collective behavior.

Authors:  Robert L Goldstone; Todd M Gureckis
Journal:  Top Cogn Sci       Date:  2009-07

Review 8.  Liquid-liquid phase separation in biology.

Authors:  Anthony A Hyman; Christoph A Weber; Frank Jülicher
Journal:  Annu Rev Cell Dev Biol       Date:  2014       Impact factor: 13.827

9.  Disentangling and modeling interactions in fish with burst-and-coast swimming reveal distinct alignment and attraction behaviors.

Authors:  Daniel S Calovi; Alexandra Litchinko; Valentin Lecheval; Ugo Lopez; Alfonso Pérez Escudero; Hugues Chaté; Clément Sire; Guy Theraulaz
Journal:  PLoS Comput Biol       Date:  2018-01-11       Impact factor: 4.475

10.  How does the interaction radius affect the performance of intervention on collective behavior?

Authors:  Caiyun Wang; Jing Han
Journal:  PLoS One       Date:  2018-02-15       Impact factor: 3.240

  10 in total
  2 in total

1.  A data-driven method for reconstructing and modelling social interactions in moving animal groups.

Authors:  R Escobedo; V Lecheval; V Papaspyros; F Bonnet; F Mondada; C Sire; G Theraulaz
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-07-27       Impact factor: 6.237

2.  Multi-scale analysis and modelling of collective migration in biological systems.

Authors:  Andreas Deutsch; Peter Friedl; Luigi Preziosi; Guy Theraulaz
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-07-27       Impact factor: 6.237

  2 in total

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