First principles in the life sciences: the free-energy principle, organicism, and mechanism

Colombo, Matteo; Wright, Cory

doi:10.1007/s11229-018-01932-w

Cited by 79 publications

(80 citation statements)

References 65 publications

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“…Interesting new work in this field applies variational approaches associated with FEP to issues such as niche construction and organism‐niche dynamics (cf. variational ecology), leading to new ideas about the physics of life (Colombo & Wright, ; Constant, Ramstead, Veissière, Campbell & Friston, ; Ramstead, Constant, Badcock & Friston, ).…”

Section: Predictive Processing and Topics In Cognitive Sciencementioning

confidence: 99%

New directions in predictive processing

2020

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Predictive processing (PP) is now a prominent theoretical framework in the philosophy of mind and cognitive science. This review focuses on PP research with a relatively philosophical focus, taking stock of the framework and discussing new directions. The review contains an introduction that describes the full PP toolbox; an exploration of areas where PP has advanced understanding of perceptual and cognitive phenomena; a discussion of PP's impact on foundational issues in cognitive science; and a consideration of the philosophy of science of PP. The overall picture is that PP is a fruitful framework, with exciting new directions awaiting exploration.

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Section: Predictive Processing and Topics In Cognitive Sciencementioning

confidence: 99%

New directions in predictive processing

2020

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“…Therefore, free energy minimization is sufficient for adaptivity and life can be understood in terms of FEP. (For a similar argument, see (Colombo and Wright 2018). )…”

Section: A Biological Interpretation Of Fepmentioning

confidence: 77%

Computational enactivism under the free energy principle

Korbak

2019

Synthese

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In this paper, I argue that enactivism and computationalism-two seemingly incompatible research traditions in modern cognitive science-can be fruitfully reconciled under the framework of the free energy principle (FEP). FEP holds that cognitive systems encode generative models of their niches and cognition can be understood in terms of minimizing the free energy of these models. There are two philosophical interpretations of this picture. A computationalist will argue that as FEP claims that Bayesian inference underpins both perception and action, it entails a concept of cognition as a computational process. An enactivist, on the other hand, will point out that FEP explains cognitive systems as constantly self-organizing to non-equilibrium steady-state. My claim is that these two interpretations are both true at the same time and that they enlighten each other.

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“…Additionally, diverse concerns with respect to strongly emergent approaches in neuroscience have been raised, including arguments related to their plausibility in regard to empirical evidence (120), or to potential limits of their falsifiability (121). Finally, the fact that consciousness is usually the only proposed example of strong emergence in the natural world (see e.g.…”

Section: Emergencementioning

confidence: 99%

A Complex Systems Perspective on Neuroimaging Studies of Behaviour and Its Disorders

Turkheimer¹,

Rosas²,

Dipasquale³

et al. 2020

Preprint

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The study of complex systems deals with emergent behaviour that arises as a result of nonlinear spatio-temporal interactions between a large number of components both within the system, as well as between the system and its environment. There is a strong case to be made that neural systems as well as their emergent behaviour and disorders, can be studied within the framework of complexity science. In particular, the field of neuroimaging has begun to apply both theoretical and experimental procedures originating in complexity science – usually in parallel with traditional methodologies. Here, we demonstrate that the use of such traditional models may distort the outcomes of neuroimaging experiments – hence affecting their interpretability and raising questions about their reliability.Therefore, we argue in favor of adopting a complex systems-based methodology in the study of neuroimaging, alongside appropriate experimental paradigms, and with minimal influences from non-complex systems approaches. Our exposition includes a review of the fundamental mathematical concepts, combined with practical examples and a compilation of results from the literature.

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First principles in the life sciences: the free-energy principle, organicism, and mechanism

Cited by 79 publications

References 65 publications

New directions in predictive processing

New directions in predictive processing

Computational enactivism under the free energy principle

A Complex Systems Perspective on Neuroimaging Studies of Behaviour and Its Disorders

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