scholarly journals The Markov blankets of life: autonomy, active inference and the free energy principle

2018 ◽  
Vol 15 (138) ◽  
pp. 20170792 ◽  
Author(s):  
Michael Kirchhoff ◽  
Thomas Parr ◽  
Ensor Palacios ◽  
Karl Friston ◽  
Julian Kiverstein
Keyword(s):  
Free Energy ◽  
Biological Systems ◽  
Autonomous Systems ◽  
Living Organism ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Markov Blanket ◽  
Markov Blankets ◽  
The Way

This work addresses the autonomous organization of biological systems. It does so by considering the boundaries of biological systems, from individual cells to Home sapiens , in terms of the presence of Markov blankets under the active inference scheme—a corollary of the free energy principle. A Markov blanket defines the boundaries of a system in a statistical sense. Here we consider how a collective of Markov blankets can self-assemble into a global system that itself has a Markov blanket; thereby providing an illustration of how autonomous systems can be understood as having layers of nested and self-sustaining boundaries. This allows us to show that: (i) any living system is a Markov blanketed system and (ii) the boundaries of such systems need not be co-extensive with the biophysical boundaries of a living organism. In other words, autonomous systems are hierarchically composed of Markov blankets of Markov blankets—all the way down to individual cells, all the way up to you and me, and all the way out to include elements of the local environment.

scholarly journals Predicting green: really radical (plant) predictive processing

2017 ◽  
Vol 14 (131) ◽  
pp. 20170096 ◽  
Author(s):  
Paco Calvo ◽  
Karl Friston
Keyword(s):  
Free Energy ◽  
Biological Systems ◽  
Local Environment ◽  
Sensory Stimulation ◽  
Predictive Processing ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Adaptive Value ◽  
Plant Behaviour

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.

scholarly journals The Emperor's New Markov Blankets

2021 ◽  
pp. 1-63
Author(s):  
Jelle Bruineberg ◽  
Krzysztof Dolega ◽  
Joe Dewhurst ◽  
Manuel Baltieri
Keyword(s):  
Free Energy ◽  
Mathematical Framework ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Variational Free Energy ◽  
Standard Application ◽  
The Mind ◽  
Physical Boundary ◽  
Markov Blankets

Abstract The free energy principle, an influential framework in computational neuroscience and theoretical neurobiology, starts from the assumption that living systems ensure adaptive exchanges with their environment by minimizing the objective function of variational free energy. Following this premise, it claims to deliver a promising integration of the life sciences. In recent work, Markov Blankets, one of the central constructs of the free energy principle, have been applied to resolve debates central to philosophy (such as demarcating the boundaries of the mind). The aim of this paper is twofold. First, we trace the development of Markov blankets starting from their standard application in Bayesian networks, via variational inference, to their use in the literature on active inference. We then identify a persistent confusion in the literature between the formal use of Markov blankets as an epistemic tool for Bayesian inference, and their novel metaphysical use in the free energy framework to demarcate the physical boundary between an agent and its environment. Consequently, we propose to distinguish between ‘Pearl blankets’ to refer to the original epistemic use of Markov blankets and ‘Friston blankets’ to refer to the new metaphysical construct. Second, we use this distinction to critically assess claims resting on the application of Markov blankets to philosophical problems. We suggest that this literature would do well in differentiating between two different research programs: ‘inference with a model’ and ‘inference within a model’. Only the latter is capable of doing metaphysical work with Markov blankets, but requires additional philosophical premises and cannot be justified by an appeal to the success of the mathematical framework alone.

scholarly journals The Markov blanket trick: On the scope of the free energy principle and active inference

2021 ◽  
Author(s):  
Vicente Raja ◽  
Dinesh Valluri ◽  
Edward Baggs ◽  
Anthony Chemero ◽  
Michael L. Anderson
Keyword(s):  
Free Energy ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Markov Blanket

scholarly journals ‘Through others we become ourselves’: The dialectics of predictive coding and active inference

2019 ◽  
Author(s):  
Dimitris Bolis ◽  
Leonhard Schilbach
Keyword(s):  
Free Energy ◽  
Social Interaction ◽  
Predictive Coding ◽  
Real Life ◽  
Other Minds ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Multiscale Dynamics ◽  
Cultural Processes

Thinking Through Other Minds (TTOM) creatively situates the free energy principle within real-life cultural processes, thereby enriching both sociocultural theories and Bayesian accounts of cognition. Here, shifting the attention from thinking to becoming, we suggest complementing such an account by focusing on the empirical, computational and conceptual investigation of the multiscale dynamics of social interaction.

scholarly journals Natural language syntax complies with the free-energy principle

2021 ◽  
Author(s):  
Elliot Murphy ◽  
Emma Holmes ◽  
Karl Friston
Keyword(s):  
Free Energy ◽  
Natural Language ◽  
Explanatory Power ◽  
Speech Segmentation ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Language Functions ◽  
Theoretical Syntax ◽  
Natural Language Syntax

Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating elementary syntactic objects. We argue that recently proposed principles of economy in language design—such as “minimal search” and “least effort” criteria from theoretical syntax—adhere to the FEP. This permits a greater degree of explanatory power to the FEP—with respect to higher language functions—and presents linguists with a grounding in first principles of notions pertaining to computability. More generally, we explore the possibility of migrating certain topics in linguistics over to the domain of fields that investigate the FEP, such as complex polysemy. We aim to align concerns of linguists with the normative model for organic self-organisation associated with the FEP, marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference.

scholarly journals Some Interesting Observations on the Free Energy Principle

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1076
Author(s):  
Karl J. Friston ◽  
Lancelot Da Costa ◽  
Thomas Parr
Keyword(s):  
Free Energy ◽  
Bayesian Inference ◽  
Coupled Systems ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Markov Blanket ◽  
Approximate Bayesian ◽  
Approximate Bayesian Inference ◽  
Early Formulation

Biehl et al. (2021) present some interesting observations on an early formulation of the free energy principle. We use these observations to scaffold a discussion of the technical arguments that underwrite the free energy principle. This discussion focuses on solenoidal coupling between various (subsets of) states in sparsely coupled systems that possess a Markov blanket—and the distinction between exact and approximate Bayesian inference, implied by the ensuing Bayesian mechanics.

scholarly journals Integrated World Modeling Theory (IWMT) Implemented: Towards Reverse Engineering Consciousness with the Free Energy Principle and Active Inference

2020 ◽  
Author(s):  
Adam Safron
Keyword(s):  
Free Energy ◽  
Reference Frames ◽  
Phenomenal Consciousness ◽  
Predictive Processing ◽  
Active Inference ◽  
Perceptual Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
World Modeling ◽  
Modeling Theory

Integrated World Modeling Theory (IWMT) is a synthetic model that attempts to unify theories of consciousness within the Free Energy Principle and Active Inference framework, with particular emphasis on Integrated Information Theory (IIT) and Global Neuronal Workspace Theory (GNWT). IWMT further suggests predictive processing in sensory hierarchies may be well-modeled as (folded, sparse, partially disentangled) variational autoencoders, with beliefs discretely-updated via the formation of synchronous complexes—as self-organizing harmonic modes (SOHMs)—potentially entailing maximal a posteriori (MAP) estimation via turbo coding. In this account, alpha-synchronized SOHMs across posterior cortices may constitute the kinds of maximal complexes described by IIT, as well as samples (or MAP estimates) from multimodal shared latent space, organized according to egocentric reference frames, entailing phenomenal consciousness as mid-level perceptual inference. When these posterior SOHMs couple with frontal complexes, this may enable various forms of conscious access as a kind of mental act(ive inference), affording higher order cognition/control, including the kinds of attentional/intentional processing and reportability described by GNWT. Across this autoencoding heterarchy, intermediate-level beliefs may be organized into spatiotemporal trajectories by the entorhinal/hippocampal system, so affording episodic memory, counterfactual imaginings, and planning.

scholarly journals Does the metaphysical dog wag its formal tail? The free energy principle and philosophical debates about life, mind, and matter.

2022 ◽  
Author(s):  
Wanja Wiese
Keyword(s):  
Free Energy ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Markov Blanket ◽  
Philosophical Debates

According to Bruineberg and colleagues, philosophical arguments on life, mind, and matter that are based on the free energy principle (FEP) (i) essentially draw on the Markov blanket construct and (ii) tend to assume that strong metaphysical claims can be justified on the basis of metaphysically innocuous formal assumptions provided by FEP. I argue against both (i) and (ii).

scholarly journals Combining Active Inference and Hierarchical Predictive Coding: A Tutorial Introduction and Case Study

2019 ◽  
Author(s):  
Beren Millidge
Keyword(s):  
Free Energy ◽  
Reinforcement Learning ◽  
Predictive Coding ◽  
Action Selection ◽  
Proof Of Concept ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Perceptual Model

This paper combines the active inference formulation of action (Friston, 2009) with hierarchical predictive coding models (Friston, 2003) to provide a proof-of-concept implementation of an active inference agent able to solve a common reinforcement learning baseline -- the cart-pole environment in OpenAI gym. It demonstrates empirically that predictive coding and active inference approaches can be successfully scaled up to tasks more challenging than the mountain car (Friston 2009, 2012). We show that hierarchical predictive coding models can be learned from scratch during the task, and can successfully drive action selection via active inference. To our knowledge, it is the first implemented active inference agent to combine active inference with a hierarchical predictive coding perceptual model. We also provide a tutorial walk-through of the free-energy principle, hierarchical predictive coding, and active inference, including an in-depth derivation of our agent.

Social Active Inference

2021 ◽  
pp. 58-72
Author(s):  
El Hassan Bezzazi
Keyword(s):  
Free Energy ◽  
Real Life ◽  
Dynamic Logic ◽  
Negotiation Process ◽  
The Other ◽  
Optimal Decision ◽  
Embodied Perception ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle

The free energy principle and its corollary, active inference, were introduced by Karl Friston as an explanation embodied perception and action in neuroscience, and since, it has been used to address many other issues in different fields mainly related to cognitive science like learning, optimal decision, or interpersonal inference. Negotiation is a process where each negotiator has conflicting motivation is aiming to maximize his utility and where agreement is reached when the opposing interests are balanced. The purpose of this chapter is to illustrate how the free energy principle might be used through active inference in modeling a negotiation process based on an example of real life. The work is an attempt to bring together a dynamic logic framework with appropriate operators to consider motivation among agents on one hand and the active inference framework on the other hand.

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