Hierarchical Markov blankets and adaptive active inference

Kirchhoff, Michael D.

doi:10.1016/j.plrev.2017.12.009

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…We can see from these first few remarks that the FEP is always implicitly formulated in a multiscale, or scale free fashion, which explains its appeal to those investigating multi-scale dynamics[52,53]. Indeed, already at this early stage of presentation, the formulation presented here appeals to a separation of timescales: we distinguish between a faster timescale of states or paths that are effectively treated as random fluctuations, and a slower timescale of states or paths that are treated as states or paths per se.…”

mentioning

confidence: 99%

On Bayesian Mechanics: A Physics of and by Beliefs

Ramstead¹,

Sakthivadivel²,

Heins³

et al. 2022

Preprint

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The aim of this paper is to introduce a field of study that has emerged over the last decade, called Bayesian mechanics. Bayesian mechanics is a probabilistic mechanics, comprising tools that enable us to model systems endowed with a particular partition (i.e., into particles), where the internal states (or the trajectories of internal states) of a particular system encode the parameters of beliefs about quantities that characterise the system. These tools allow us to write down mechanical theories for systems that look as if they are estimating posterior probability distributions over the causes of their sensory states, providing a formal language to model the constraints, forces, fields, and potentials that determine how the internal states of such systems move in a space of beliefs (i.e., on a statistical manifold). Here we will review the state of the art in the literature on the free energy principle, distinguishing between three ways in which Bayesian mechanics has been applied to particular systems (i.e., path-tracking, mode-tracking, and mode-matching). We will go on to examine the duality of the free 1

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mentioning

confidence: 99%

On Bayesian Mechanics: A Physics of and by Beliefs

Ramstead¹,

Sakthivadivel²,

Heins³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The multiscale perspective afforded by the free energy principle means this disambiguation between self and other is constrained by the hierarchical level (i.e. spatiotemporal scale) above (Kirchhoff 2018;Kirchhoff et al 2018;Ramstead et al 2018;Hesp et al 2019;Ramstead et al 2019;Palacios et al 2020)-a necessary facet of 'belonging to something greater'. On a general note, this thesis rejects dualism in the same spirit of recent proposals-from molecular biology (Kuchling et al 2019;Manicka and Levin 2019) to evolution (Ao 2005;Frank 2012;Campbell 2016;Ramirez and Marshall 2017)-that put inference, beliefs 1 and purpose into biological processes.…”

Section: Introductionmentioning

confidence: 99%

Immunoceptive inference: why are psychiatric disorders and immune responses intertwined?

et al. 2021

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There is a steadily growing literature on the role of the immune system in psychiatric disorders. So far, these advances have largely taken the form of correlations between specific aspects of inflammation (e.g. blood plasma levels of inflammatory markers, genetic mutations in immune pathways, viral or bacterial infection) with the development of neuropsychiatric conditions such as autism, bipolar disorder, schizophrenia and depression. A fundamental question remains open: why are psychiatric disorders and immune responses intertwined? To address this would require a step back from a historical mind–body dualism that has created such a dichotomy. We propose three contributions of active inference when addressing this question: translation, unification, and simulation. To illustrate these contributions, we consider the following questions. Is there an immunological analogue of sensory attenuation? Is there a common generative model that the brain and immune system jointly optimise? Can the immune response and psychiatric illness both be explained in terms of self-organising systems responding to threatening stimuli in their external environment, whether those stimuli happen to be pathogens, predators, or people? Does false inference at an immunological level alter the message passing at a psychological level (or vice versa) through a principled exchange between the two systems?

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“…It can be mathematically proven that this 'spreading' behaviour is what leads to diffusion [56]. 8 These first few remarks on fluctuations and their variance highlight how the FEP is formulated implicitly in a multi-scale fashion, which has motivated a growing body of work on multi-scale dynamics under the FEP [59][60][61]. Indeed, already at this early stage of presentation, the formulation presented here appeals to a separation of timescales: we distinguish between a faster timescale of states or paths that are effectively treated as random fluctuations, and a slower timescale of states or paths that are treated as states or paths per se.…”

mentioning

confidence: 99%

On Bayesian mechanics: a physics of and by beliefs

et al. 2023

View full text Add to dashboard Cite

The aim of this paper is to introduce a field of study that has emerged over the last decade, called Bayesian mechanics. Bayesian mechanics is a probabilistic mechanics, comprising tools that enable us to model systems endowed with a particular partition (i.e. into particles), where the internal states (or the trajectories of internal states) of a particular system encode the parameters of beliefs about external states (or their trajectories). These tools allow us to write down mechanical theories for systems that look as if they are estimating posterior probability distributions over the causes of their sensory states. This provides a formal language for modelling the constraints, forces, potentials and other quantities determining the dynamics of such systems, especially as they entail dynamics on a space of beliefs (i.e. on a statistical manifold). Here, we will review the state of the art in the literature on the free energy principle, distinguishing between three ways in which Bayesian mechanics has been applied to particular systems (i.e. path-tracking, mode-tracking and mode-matching). We go on to examine a duality between the free energy principle and the constrained maximum entropy principle, both of which lie at the heart of Bayesian mechanics, and discuss its implications.

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Hierarchical Markov blankets and adaptive active inference

Cited by 7 publications

References 8 publications

On Bayesian Mechanics: A Physics of and by Beliefs

On Bayesian Mechanics: A Physics of and by Beliefs

Immunoceptive inference: why are psychiatric disorders and immune responses intertwined?

On Bayesian mechanics: a physics of and by beliefs

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