An active inference implementation of phototaxis
Manuel Baltieri, Christopher L. Buckley

TL;DR
This paper presents a simple active inference agent model demonstrating how phototaxis behavior can emerge with minimal environmental modeling, highlighting the theory's potential for explaining adaptive action-perception cycles.
Contribution
It introduces an action-oriented active inference model capable of producing phototaxis without detailed environmental models, contrasting previous perception-focused implementations.
Findings
The model successfully demonstrates phototaxis behavior.
Parameter variations lead to different behavioral outcomes.
The approach offers insights into minimalistic active inference implementations.
Abstract
Active inference is emerging as a possible unifying theory of perception and action in cognitive and computational neuroscience. On this theory, perception is a process of inferring the causes of sensory data by minimising the error between actual sensations and those predicted by an inner \emph{generative} (probabilistic) model. Action on the other hand is drawn as a process that modifies the world such that the consequent sensory input meets expectations encoded in the same internal model. These two processes, inferring properties of the world and inferring actions needed to meet expectations, close the sensory/motor loop and suggest a deep symmetry between action and perception. In this work we present a simple agent-based model inspired by this new theory that offers insights on some of its central ideas. Previous implementations of active inference have typically examined a…
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Taxonomy
TopicsEmbodied and Extended Cognition · Neural dynamics and brain function · Philosophy and History of Science
