Home Community Decoding Collective Behavior: How Lively Bayesian Inference Powers the Natural Movements of Animal Groups

Decoding Collective Behavior: How Lively Bayesian Inference Powers the Natural Movements of Animal Groups

Decoding Collective Behavior: How Lively Bayesian Inference Powers the Natural Movements of Animal Groups

The phenomenon of collected motion in animals observed in activities like swarming locusts, education fish, flocking birds, and herding ungulates is extensively studied resulting from its visually striking property and its emergence from easy interactions amongst group members. Recent research focuses on more biologically motivated, agent-based approaches that aim to model specific behavioral circuits and decision rules that govern individual behaviors. Researchers have designed a model based on energetic inferencing that bridges theoretical and biological elements of human behavior.

This model class unifies cognitive and physics-based perspectives, offering a comprehensive understanding of adaptive behavior. It focuses on how a person estimates distance to neighbors and uses the main points to make decisions. It has two parts – the dynamic model, which describes how distances change over time, and the commentary model, which explains how individuals sense these distances. Lively inference updates its beliefs and actions to attenuate the surprises. 

The model emphasizes how intricate behavior arises from easy actions driven by predictions and sensory output features. In some scenarios, it converges to traditional force vectors like attraction, repulsion, and alignments, derived as free energy functional, acting because the upper limit on the surprise. Behavioral plasticity is a key mechanism that helps to boost and collectively represent temporary fluctuations. Unlike using additional rules or mechanisms for specific outcomes, plasticity involves conducting gradient descent on free energy for model parameters. This mechanism is integrated into energetic inference, extending its application to model parameter updates.

The researchers hope that their work will function a link between existing theoretical models of collective animal behavior and more neuro/ML-adjacent fields like energetic inference and the Bayesian brain framework. Additionally they emphasize that their chosen model explains key attributes observed in collective systems and effectively reproduces the aptitude to boost and decode the knowledge that earlier models struggled to model without invoking additional mechanisms. 

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Astha Kumari

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Astha Kumari is a consulting intern at MarktechPost. She is currently pursuing Dual degree course within the department of chemical engineering from Indian Institute of Technology(IIT), Kharagpur. She is a machine learning and artificial intelligence enthusiast. She is keen in exploring their real life applications in various fields.

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