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Predicting green: really radical (plant) predictive processing
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 mod...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5493793/ https://www.ncbi.nlm.nih.gov/pubmed/28637913 http://dx.doi.org/10.1098/rsif.2017.0096 |
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author | Calvo, Paco Friston, Karl |
author_facet | Calvo, Paco Friston, Karl |
author_sort | Calvo, Paco |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5493793 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-54937932017-07-09 Predicting green: really radical (plant) predictive processing Calvo, Paco Friston, Karl J R Soc Interface Review Articles 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. The Royal Society 2017-06 2017-06-21 /pmc/articles/PMC5493793/ /pubmed/28637913 http://dx.doi.org/10.1098/rsif.2017.0096 Text en © 2017 The Author(s). http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Review Articles Calvo, Paco Friston, Karl Predicting green: really radical (plant) predictive processing |
title | Predicting green: really radical (plant) predictive processing |
title_full | Predicting green: really radical (plant) predictive processing |
title_fullStr | Predicting green: really radical (plant) predictive processing |
title_full_unstemmed | Predicting green: really radical (plant) predictive processing |
title_short | Predicting green: really radical (plant) predictive processing |
title_sort | predicting green: really radical (plant) predictive processing |
topic | Review Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5493793/ https://www.ncbi.nlm.nih.gov/pubmed/28637913 http://dx.doi.org/10.1098/rsif.2017.0096 |
work_keys_str_mv | AT calvopaco predictinggreenreallyradicalplantpredictiveprocessing AT fristonkarl predictinggreenreallyradicalplantpredictiveprocessing |