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Related Experiment Video

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Studying Food Reward and Motivation in Humans

Studying Food Reward and Motivation in Humans

Published on: March 19, 2014

Intrinsically motivated collective motion.

Henry J Charlesworth¹, Matthew S Turner^2,3

¹Centre for Complexity Science, University of Warwick, Coventry CV4 7AL, United Kingdom.

Proceedings of the National Academy of Sciences of the United States of America

|July 19, 2019

Summary

This summary is machine-generated.

Collective motion in animals emerges from a simple principle: maximizing future visual access. This future state maximization (FSM) explains emergent behaviors like cohesion and coalignment without explicit encoding.

Keywords:

active matter collective motion intelligent matter

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Area of Science:

Complex systems
Theoretical biology
Artificial intelligence

Background:

Collective motion is observed across diverse systems, from animal groups to artificial agents.
Existing models often use high-level features like coalignment and cohesion.
The underlying low-level principles driving these emergent behaviors remain an open question.

Purpose of the Study:

To investigate if collective motion features emerge from a fundamental, low-level principle.
To explore future state maximization (FSM) as a potential unifying control principle.
To connect FSM to evolutionary fitness and cognitive processes.

Main Methods:

Agents were modeled with a simple visual perception (retina-like).
Agents' movement was driven by maximizing expected future visual environments.
Simulated trajectories were analyzed to identify emergent collective behaviors.
A multilayered neural network was trained to mimic the FSM heuristic.

Main Results:

Collective motion, including cohesion, coalignment, and collision suppression, emerged naturally under FSM.
These emergent properties were not explicitly programmed into the agents.
A neural network successfully mimicked the FSM control principle.
The findings suggest FSM is accessible to animal cognition.

Conclusions:

Future state maximization (FSM) provides a parsimonious explanation for emergent collective motion.
FSM offers a potential evolutionary advantage by promoting adaptability in uncertain environments.
This principle may underlie collective behaviors in social animals and inform artificial intelligence designs.