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Related Experiment Video

Updated: Feb 28, 2026

Fluorescence Activated Cell Sorting of Plant Protoplasts

Fluorescence Activated Cell Sorting of Plant Protoplasts

Published on: February 18, 2010

Predicting green: really radical (plant) predictive processing.

Paco Calvo^1,2, Karl Friston³

¹EIDYN Research Centre, and Institute of Molecular Plant Sciences, University of Edinburgh, Edinburgh, UK paco.calvo@ed.ac.uk.

Journal of the Royal Society, Interface

|June 23, 2017

Summary

Plants actively infer environmental information to minimize surprise, demonstrating purposeful behavior through predictive processing. This approach frames plant responses within the biological free-energy principle.

Keywords:

affordance embodiment free energy perceptual/active inference plant intelligence predictive processing

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Last Updated: Feb 28, 2026

Fluorescence Activated Cell Sorting of Plant Protoplasts

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Imaging Spatial Reorganization of a MAPK Signaling Pathway Using the Tobacco Transient Expression System

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Area of Science:

Integrative Biology
Theoretical Ecology
Plant Sciences

Background:

Biological systems self-organize by minimizing surprise over time.
This self-organization relies on generative models predicting environmental states.
Plants exhibit coordinated, rapid responses to environmental cues.

Purpose of the Study:

To explain plant environmental responses using the free-energy principle.
To investigate active inference as a mechanism for plant behavior.
To explore predictive processing in plants and its implications for cognition.

Main Methods:

Theoretical framework based on the free-energy principle.
Conceptual analysis of plant perception and action.
Application of active inference to plant environmental interactions.

Main Results:

Plant behavior can be understood as minimizing free energy through active inference.
Plants proactively sample their environment to gain adaptive information.
This predictive process underlies purposeful and anticipatory plant behavior.

Conclusions:

The free-energy principle provides a unified framework for understanding plant responses.
Active inference offers a radical predictive processing account of plant perception.
This perspective broadens our understanding of life, cognition, and biological self-organization.