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Updated: Dec 13, 2025

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Collective information processing in human phase separation.

Bertrand Jayles1,2, Ramón Escobedo2, Roberto Pasqua3

  • 1Laboratoire de Physique Théorique, CNRS, Université de Toulouse - Paul Sabatier, 31062 Toulouse, France.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|July 28, 2020
PubMed
Summary
This summary is machine-generated.

Information filtering influences collective segregation in human groups. Experiments show a specific perception range (k=7) optimizes group separation, similar to physics phase separation.

Keywords:
collective human behaviourcollective information processingcollective motioncomputational modellingphase separation

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Area of Science:

  • Collective Behavior
  • Social Physics
  • Information Filtering

Background:

  • Digital societies feature interactions via interfaces impacting collective dynamics.
  • Social media filters and recommender systems can cause group polarization and fragmentation.
  • Group segregation processes resemble phase separation phenomena in physics.

Purpose of the Study:

  • To investigate the impact of information filtering on collective segregation behavior in human groups.
  • To determine how perception range (k nearest neighbors) affects segregation dynamics and outcomes.

Main Methods:

  • Conducted experiments with 22 subjects performing a collective segregation task based on assigned colors.
  • Utilized an artificial sensory device to control subjects' perception range (k=1 to 13) of their environment.
  • Developed a model simulating pedestrian motion to interpret segregation experiment results.

Main Results:

  • The perception range (k) significantly controlled the quality of group segregation.
  • An optimal perception range (k=7) was identified, beyond which segregation ability did not improve.
  • Experimental results were accurately reproduced and explained by the developed motion model.

Conclusions:

  • Information filtering, specifically the perception range, plays a crucial role in collective human group segregation.
  • The study demonstrates a strong analogy between human group segregation and phase separation in physics.
  • Findings offer insights into understanding and potentially mitigating social fragmentation in digital environments.