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Cited by 206Philosophy of Biology and Scientific ExplanationEvolution and Genetic DynamicsEmbodied and Extended Cognition

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A variational approach to niche construction

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Constant, Axel, Ramstead, Maxwell J. D., Veissière, Samuel P. L., Campbell, John O., Friston, Karl J.·2018·Journal of The Royal Society Interface
DOI 10.1098/rsif.2017.0685PMC PMC5938575
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Abstract

In evolutionary biology, niche construction is sometimes described as a genuine evolutionary process whereby organisms, through their activities and regulatory mechanisms, modify their environment such as to steer their own evolutionary trajectory, and that of other species. There is ongoing debate, however, on the extent to which niche construction ought to be considered a bona fide evolutionary force, on a par with natural selection. Recent formulations of the variational free-energy principle as applied to the life sciences describe the properties of living systems, and their selection in evolution, in terms of variational inference. We argue that niche construction can be described using a variational approach. We propose new arguments to support the niche construction perspective, and to extend the variational approach to niche construction to current perspectives in various scientific fields.

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Figures

Figure 1. Markov blankets. These schematics illustrate the partition of states into internal and hidden or external states that are separated by a Markov blanket (b) comprising sensory and active states. (a) A schematic that captures the relations of reciprocal causation between the organism and its environmental niche [54]. Changes in states of the organism over time are a function of the immediate state of the organism (μ) and its environment (), and, reciprocally, changes in the states of the organism's environmental niche over time are a function of the current state of the environment and the organism. The form of this dynamical coupling can now be specified by appeal to the FEP. This introduces a separation of organismic and environmental states (i.e. internal and external states) via an intervening Markov blanket of active (a) and sensory states (s). Once the Markov blanket is in place, the non-equilibrium steady-state dynamics are prescribed by the Fokker Planck equation that can be expressed in terms of gradient flows on variational free energy. (b) This partition (and flows) as it would be applied to action and perception in the brain, where active and internal states minimize a free-energy functional of internal and blanket states. The ensuing self-organization of internal states then corresponds to perception, while action couples brain states back to external states. However, because of the antisymmetry of the conditional dependencies implicit in the Markov blanket, we can also express external dynamics as a gradient flow of a free-energy functional of external and blanket states. (c) Shows exactly the same dependencies but rearranged so that the internal states are associated with the intracellular states of a cell, while the sensory states become the surface states of the cell membrane overlying active states (e.g. the actin filaments of the cytoskeleton). For simplicity, we have admitted the solenoidal flow in this figure. For a more complete account, see [47]. (Online version in colour.)
Figure 1. Markov blankets. These schematics illustrate the partition of states into internal and hidden or external states that are separated by a Markov blanket (b) comprising sensory and active states. (a) A schematic that captures the relations of reciprocal causation between the organism and its environmental niche [54]. Changes in states of the organism over time are a function of the immediate state of the organism (μ) and its environment (), and, reciprocally, changes in the states of the organism's environmental niche over time are a function of the current state of the environment and the organism. The form of this dynamical coupling can now be specified by appeal to the FEP. This introduces a separation of organismic and environmental states (i.e. internal and external states) via an intervening Markov blanket of active (a) and sensory states (s). Once the Markov blanket is in place, the non-equilibrium steady-state dynamics are prescribed by the Fokker Planck equation that can be expressed in terms of gradient flows on variational free energy. (b) This partition (and flows) as it would be applied to action and perception in the brain, where active and internal states minimize a free-energy functional of internal and blanket states. The ensuing self-organization of internal states then corresponds to perception, while action couples brain states back to external states. However, because of the antisymmetry of the conditional dependencies implicit in the Markov blanket, we can also express external dynamics as a gradient flow of a free-energy functional of external and blanket states. (c) Shows exactly the same dependencies but rearranged so that the internal states are associated with the intracellular states of a cell, while the sensory states become the surface states of the cell membrane overlying active states (e.g. the actin filaments of the cytoskeleton). For simplicity, we have admitted the solenoidal flow in this figure. For a more complete account, see [47]. (Online version in colour.)CC BY · PubMed Central
Figure 2. A duet for one. In this simulation of free-energy minimization, two birds with the same generative models—but different initial expectations—sing for 2 s and then listen for any response. In the right panels (singing alone), the birds cannot hear each other (because they are too far apart) and the successive epochs of songs diverge due to the sensitivity to initial conditions implicit in their (chaotic) generative models. The upper panels show the sonogram heard by the first bird. Because this bird can only hear itself, the sonogram reflects the proprioceptive predictions based upon posterior expectations in the HVC (first-level expectations) and area X (second-level expectations). These anatomical designations are based upon the hierarchical generative model illustrated with the insets (left). The posterior expectations for the first bird are shown in red as a function of time—and the equivalent expectations for the second bird are shown in blue. However, when the two birds can hear each other (singing together), the posterior expectations are encoded by internal states show identical synchrony at both the sensory and extrasensory levels—as shown in the middle panels. Note that the sonogram is now continuous over successive 2 s epochs, because the first bird can hear itself and the second bird. The ensuing synchronization manifold is shown in the lower panels. These plot the second-level (area X) expectations in the second bird against the equivalent expectations in the first. The left panel shows chaotic and uncoupled dynamics when the birds cannot hear each other, while the right panel shows the generalized (identical) synchrony that emerges when the birds exchange sensory signals. The different colours correspond to the three hidden states for each bird. The synchronization manifold for identical synchronization corresponds to the (broken) diagonal line. See [63] for details.
Figure 2. A duet for one. In this simulation of free-energy minimization, two birds with the same generative models—but different initial expectations—sing for 2 s and then listen for any response. In the right panels (singing alone), the birds cannot hear each other (because they are too far apart) and the successive epochs of songs diverge due to the sensitivity to initial conditions implicit in their (chaotic) generative models. The upper panels show the sonogram heard by the first bird. Because this bird can only hear itself, the sonogram reflects the proprioceptive predictions based upon posterior expectations in the HVC (first-level expectations) and area X (second-level expectations). These anatomical designations are based upon the hierarchical generative model illustrated with the insets (left). The posterior expectations for the first bird are shown in red as a function of time—and the equivalent expectations for the second bird are shown in blue. However, when the two birds can hear each other (singing together), the posterior expectations are encoded by internal states show identical synchrony at both the sensory and extrasensory levels—as shown in the middle panels. Note that the sonogram is now continuous over successive 2 s epochs, because the first bird can hear itself and the second bird. The ensuing synchronization manifold is shown in the lower panels. These plot the second-level (area X) expectations in the second bird against the equivalent expectations in the first. The left panel shows chaotic and uncoupled dynamics when the birds cannot hear each other, while the right panel shows the generalized (identical) synchrony that emerges when the birds exchange sensory signals. The different colours correspond to the three hidden states for each bird. The synchronization manifold for identical synchronization corresponds to the (broken) diagonal line. See [63] for details.CC BY · PubMed Central
Figure 3. Learning and communication. (a) Shows epoch by epoch changes in the posterior expectations (lines) of an order parameter of the first bird (blue) and second bird (green) determining the (chaotic) structure of the songs of the sort shown in figure 2. The shaded areas correspond to 90% (prior) Bayesian confidence intervals. The broken lines (and intervals) report the results of the same simulation, but when the birds could not hear each other. (b) Shows the synchronization of extrasensory (higher) posterior expectations for the first (i) and subsequent (ii) exchanges, respectively. This synchronization is shown by plotting a mixture of expectations (and their temporal derivatives) from the second bird against the equivalent expectations of the first bird, where this mixture is optimized assuming a linear mapping. In this example of perceptual learning via free-energy minimization, the second (green) bird had more precise beliefs about its order parameter and, therefore, effectively, taught the first third. Note that in both scenarios, learning converges to the same value resulting in a generalized synchrony between self and non-self; i.e. beyond self-organization. See Friston & Frith [71] for details.
Figure 3. Learning and communication. (a) Shows epoch by epoch changes in the posterior expectations (lines) of an order parameter of the first bird (blue) and second bird (green) determining the (chaotic) structure of the songs of the sort shown in figure 2. The shaded areas correspond to 90% (prior) Bayesian confidence intervals. The broken lines (and intervals) report the results of the same simulation, but when the birds could not hear each other. (b) Shows the synchronization of extrasensory (higher) posterior expectations for the first (i) and subsequent (ii) exchanges, respectively. This synchronization is shown by plotting a mixture of expectations (and their temporal derivatives) from the second bird against the equivalent expectations of the first bird, where this mixture is optimized assuming a linear mapping. In this example of perceptual learning via free-energy minimization, the second (green) bird had more precise beliefs about its order parameter and, therefore, effectively, taught the first third. Note that in both scenarios, learning converges to the same value resulting in a generalized synchrony between self and non-self; i.e. beyond self-organization. See Friston & Frith [71] for details.CC BY · PubMed Central
Figure 4. Summary schematic based on [92]. Coloured elements are those that we cover in the paper. The dotted arrows represent the directionality of causal processes. The curly brackets point to the variational processes to which we attribute the content of the bracket. Note that the dotted arrows indicating the directionalities of niche construction points both ways (as opposed to the original schematic of [92]). The reason of this is that niche construction, seen through the lens of active inference, entails the reciprocal optimization of the organism(s) and the environment (complementarity) over development (DNC timescale). Therefore, optimization through niche construction at the scale of development is not unidirectional (just like in phylogeny), as the environment per se learns and optimizes with respect to the organisms it hosts. Overtime, changes to the environment ‘sediment’, as it were, and become salient information. This information is passed on as algorithmic information through ecological inheritance (SNC timescale) to further guide reciprocal optimization (i.e. phenotypic plasticity and environmental learning).
Figure 4. Summary schematic based on [92]. Coloured elements are those that we cover in the paper. The dotted arrows represent the directionality of causal processes. The curly brackets point to the variational processes to which we attribute the content of the bracket. Note that the dotted arrows indicating the directionalities of niche construction points both ways (as opposed to the original schematic of [92]). The reason of this is that niche construction, seen through the lens of active inference, entails the reciprocal optimization of the organism(s) and the environment (complementarity) over development (DNC timescale). Therefore, optimization through niche construction at the scale of development is not unidirectional (just like in phylogeny), as the environment per se learns and optimizes with respect to the organisms it hosts. Overtime, changes to the environment ‘sediment’, as it were, and become salient information. This information is passed on as algorithmic information through ecological inheritance (SNC timescale) to further guide reciprocal optimization (i.e. phenotypic plasticity and environmental learning).CC BY · PubMed Central

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Cites

Show all 103 references

Research works printed but not linked: 3

  1. 10.2307/j.ctvc77f0d · DOI · Paper page
  2. Veissière S. 2016 Varieties of Tulpa experiences: the hypnotic nature of human sociality, personhood, and interphenomenality. Hypnosis and meditation: towards an integrative science of conscious planes, pp. 55–76. Oxford, UK: Oxford University Press Oxford.
  3. Odling-Smee FJ. 2010 Niche inheritance. In Evolution: the extended synthesis (eds M Pigliucci, GB Miller), pp. 175–208. Cambridge, MA: MIT Press.

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Metadata

Authors
Constant, A., Ramstead, M. J. D., Veissière, S. P. L., Campbell, J. O., & Friston, K. J.
Affiliations
  • University of Amsterdam (NL)
  • McGill University (CA)
  • Independent Researcher, Victoria, BC, Canada
  • Wellcome Centre for Human Neuroimaging (GB)
Corresponding author
Constant, Axel
ORCID
Venue
Journal of The Royal Society Interface
Publisher
The Royal Society
Published
2018-04
Volume
15
Issue
141
ISSN
  • 1742-5662 (ISSN-L)
  • 1742-5689
DOI
10.1098/rsif.2017.0685
PMC
PMC5938575
OpenAlex
W2797051230
Cited by
204
References
103
Citations by year
2026: 8 · 2025: 23 · 2024: 23 · 2023: 22 · 2022: 24 · 2021: 27 · 2020: 54 · 2019: 18 · 2018: 5
FWCI
28.44
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of The Royal Society Interface (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PhilPapers (PhilPapers Foundation) (submittedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Funders
  • Wellcome Trust (088130/Z/09/Z)
  • Social Sciences and Humanities Research Council of Canada
Topics
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
Keywords
niche construction, variational inference, natural selection, evolutionary trajectories, biological systems
MeSH terms
Adaptation, Physiological, Bayes Theorem, Biological Evolution, Models, Theoretical

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