Quantification of collective behaviour via causality analysis

TL;DR

This paper introduces a causality analysis method based on distance correlations to quantify collective behavior, revealing interaction networks in biological groups, demonstrated on fish school data.

Contribution

It presents a novel causality analysis approach for understanding collective behavior through network patterns derived from motion data.

Findings

Networks reflect expected fish social interactions.

Method successfully distinguishes different behavioral states.

Applicable to biological and social systems.

Abstract

Terms such as leader, mediator, and follower sound equal in the description of a pack of wolves, a street protest crowd, or a business team and have very similar meanings. This indicates the presence of some general law or structure that governs collective behaviour. To reveal this, we selected the most common parameter for most levels of the organisation -- motion. A causality analysis of distance correlations was performed to obtain follow-up networks that show who follows whom and how strongly. These networks characterise an observed system in general and work as an automation bridge between the biological experiment and the broad field of network analysis. The proposed method was tested on 3D image data from a controlled experiment on a 6-member school of aquarium fish of Tiger Barb. The network patterns can be easily ethologically interpreted and agreed with expected behaviour.