Predicting green: really radical (plant) predictive processing - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Jun;14(131):20170096.
doi: 10.1098/rsif.2017.0096.

Predicting green: really radical (plant) predictive processing

Paco Calvo  1   2 , Karl Friston  3
Affiliations

Predicting green: really radical (plant) predictive processing

Paco Calvo et al. J R Soc Interface. 2017 Jun.

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.

Keywords: affordance; embodiment; free energy; perceptual/active inference; plant intelligence; predictive processing.

PubMed Disclaimer

Conflict of interest statement

The authors have no competing interests.

Figures

Figure 1.
Figure 1.
Upper panel: schematic of the quantities that define free energy. These include the internal states of a system μ (e.g. a plant) and quantities describing exchange with the world; namely, sensory input formula image and action a that changes the way the environment is sampled. The environment is described by equations of motion, formula image, that specify the dynamics of (hidden) states of the world η. Here, ω denotes random fluctuations. Internal states and action both change to minimize free energy, which is a function of sensory input and a probabilistic representation (recognition density) formula image encoded by internal states. Lower panel: alternative expressions for the free energy illustrating what its minimization entails. For action, free energy can only be suppressed by increasing the accuracy of sensory data (i.e. selectively sampling data that are predicted by the representation). Conversely, optimizing internal states make the representation an approximate conditional density on the causes of sensory input (by minimizing divergence). This optimization makes the free-energy bound on surprise tighter and enables action to avoid surprising sensations. (Online version in colour.)
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Baluška F, Mancuso S, Volkmann D (eds). 2006. Communication in plants: neuronal aspects of plant life. New York, NY: Springer.
    1. Brenner ED, Stahlberg R, Mancuso S, Vivanco JM, Baluška F, van Volkenburgh E. 2007. Plant neurobiology: an integrated view of plant signaling. Trends Plant Sci. 11, 413–419. (10.1016/j.tplants.2006.06.009) - DOI - PubMed
    1. Trewavas A. 2014. Plant behaviour and intelligence. Oxford, UK: Oxford University Press.
    1. Calvo P. 2016. The philosophy of plant neurobiology: a manifesto. Synthese 193, 1323–1343. (10.1007/s11229-016-1040-1) - DOI
    1. Calvo P, Martín E, Symons J. 2014. The emergence of systematicity in minimally cognitive agents. In The architecture of cognition: rethinking Fodor and Pylyshyn's systematicity challenge (eds Calvo P, Symons J), pp. 397–434. Cambridge, MA: MIT Press.

Publication types

LinkOut - more resources