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.
Highlights
The emergent field of ‘plant neurobiology’ [1,2] studies the flexible and adaptive behaviour of plants beyond the domains of plant biochemistry/cellular and molecular biology mechanistic approaches
The overall aim of this paper, once zoo-centric and neuro-centric biases have been set aside, is to assess the prospects of a really radical predictive processing; an approach that may bear upon our understanding of life and cognition more broadly
The main thesis of plant predictive processing (PPP) is that plant behaviour takes place by way of a process that predicts the environmental sources of sensory stimulation
Summary
The emergent field of ‘plant neurobiology’ [1,2] studies the flexible and adaptive behaviour of plants beyond the domains of plant biochemistry/cellular and molecular biology mechanistic approaches. All are just examples of systems that self-organize according to the free-energy principle In other words, they exchange with their environment in a way that minimizes surprise and resolves uncertainty. This gradient descent progresses in generalized coordinates of motion (for example, in terms of position and momentum) [26,27] This lends the suppression of prediction errors a dynamical and anticipatory aspect; in the sense that a sentient system is not reducing prediction error but pursuing trajectories that have the least free energy. We will see a nice example of this below when we consider how plants respond to timedependent changes in salt concentrations One can take this argument to its extreme and posit that any self-organizing system must embody a generative model whose predictions ensure that expected free energy or uncertainty is minimized through action [28]. See figure 1 for a summary of the technical principles that underlie active inference and free-energy minimization
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
