Predicting green: really radical (plant) predictive processing

TLDR

Biological self‑organization is the minimization of surprise over time, and plants rapidly and coordinately respond to environmental contingencies by proactively sampling their surroundings to gather adaptively valuable information. The article aims to show that plants use active inference under the free‑energy principle, positing that self‑organizing systems embody generative models that predict sensory inputs to minimize expected free energy through action. The authors model plant behaviour as active inference, where a generative model predicts environmental sources of sensory stimulation, thereby guiding action to reduce surprise. This framework suggests that plants possess a perception‑like mechanism that supports purposeful, anticipatory behaviour.

Abstract

In this article we account for the way plants respond to salient features of their environment under the free-energy principle for biological systems. Biological self-organization amounts to the minimization of surprise over time. We posit that any self-organizing system must embody a generative model whose predictions ensure that (expected) free energy is minimized through action. Plants respond in a fast, and yet coordinated manner, to environmental contingencies. They pro-actively sample their local environment to elicit information with an adaptive value. Our main thesis is that plant behaviour takes place by way of a process (active inference) that predicts the environmental sources of sensory stimulation. This principle, we argue, endows plants with a form of perception that underwrites purposeful, anticipatory behaviour. The aim of the article is to assess the prospects of a radical predictive processing story that would follow naturally from the free-energy principle for biological systems; an approach that may ultimately bear upon our understanding of life and cognition more broadly.