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 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 The anatomy of choice: dopamine and decision-making

2014 ◽  
Vol 369 (1655) ◽  
pp. 20130481 ◽  
Author(s):  
Karl Friston ◽  
Philipp Schwartenbeck ◽  
Thomas FitzGerald ◽  
Michael Moutoussis ◽  
Timothy Behrens ◽  
...  
Keyword(s):  
Decision Making ◽  
Free Energy ◽  
Bayesian Inference ◽  
Expected Utility ◽  
Energy Minimization ◽  
Optimal Decision ◽  
Active Inference ◽  
Free Energy Minimization ◽  
Approximate Bayesian ◽  
Approximate Bayesian Inference

This paper considers goal-directed decision-making in terms of embodied or active inference. We associate bounded rationality with approximate Bayesian inference that optimizes a free energy bound on model evidence. Several constructs such as expected utility, exploration or novelty bonuses, softmax choice rules and optimism bias emerge as natural consequences of free energy minimization. Previous accounts of active inference have focused on predictive coding . In this paper, we consider variational Bayes as a scheme that the brain might use for approximate Bayesian inference. This scheme provides formal constraints on the computational anatomy of inference and action, which appear to be remarkably consistent with neuroanatomy. Active inference contextualizes optimal decision theory within embodied inference, where goals become prior beliefs. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (associated with softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution. Crucially, this sensitivity corresponds to the precision of beliefs about behaviour. The changes in precision during variational updates are remarkably reminiscent of empirical dopaminergic responses—and they may provide a new perspective on the role of dopamine in assimilating reward prediction errors to optimize decision-making.

Predictive Processing

2018 ◽  
Author(s):  
Wanja Wiese
Keyword(s):  
Free Energy ◽  
Bayesian Inference ◽  
General Framework ◽  
External World ◽  
Predictive Processing ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Action And Perception ◽  
Conceptual Distinction ◽  
The Mind

This chapter provides an introduction to predictive processing (PP), with a focus on its relation to Bayesian inference and the more general framework provided by Karl Friston’s free-energy principle (FEP). Philosophical implications of PP and FEP are discussed. In particular, it is argued that PP posits genuine representations, and that FEP questions whether the conceptual distinction between action and perception is useful when it comes to understanding their neuro-computational underpinnings. Finally, it is argued that PP supports the view that the mind is to some extent secluded from the external world and that perception is indirect because it is based on genuinely inferential processes.

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 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

Encultured minds, not error reduction minds

2020 ◽  
Vol 43 ◽  
Author(s):  
Robert Mirski ◽  
Mark H. Bickhard ◽  
David Eck ◽  
Arkadiusz Gut
Keyword(s):  
Free Energy ◽  
Error Reduction ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Current Article

Abstract There are serious theoretical problems with the free-energy principle model, which are shown in the current article. We discuss the proposed model's inability to account for culturally emergent normativities, and point out the foundational issues that we claim this inability stems from.

scholarly journals Comparing two sequential Monte Carlo samplers for exact and approximate Bayesian inference on biological models

2017 ◽  
Vol 14 (134) ◽  
pp. 20170340 ◽  
Author(s):  
Aidan C. Daly ◽  
Jonathan Cooper ◽  
David J. Gavaghan ◽  
Chris Holmes
Keyword(s):  
Bayesian Inference ◽  
Bayesian Methods ◽  
Sequential Monte Carlo ◽  
Model Parameters ◽  
Biological Models ◽  
Exact Inference ◽  
Modelling Studies ◽  
Approximate Bayesian ◽  
Approximate Bayesian Inference ◽  
Abc Methods

Bayesian methods are advantageous for biological modelling studies due to their ability to quantify and characterize posterior variability in model parameters. When Bayesian methods cannot be applied, due either to non-determinism in the model or limitations on system observability, approximate Bayesian computation (ABC) methods can be used to similar effect, despite producing inflated estimates of the true posterior variance. Owing to generally differing application domains, there are few studies comparing Bayesian and ABC methods, and thus there is little understanding of the properties and magnitude of this uncertainty inflation. To address this problem, we present two popular strategies for ABC sampling that we have adapted to perform exact Bayesian inference, and compare them on several model problems. We find that one sampler was impractical for exact inference due to its sensitivity to a key normalizing constant, and additionally highlight sensitivities of both samplers to various algorithmic parameters and model conditions. We conclude with a study of the O'Hara–Rudy cardiac action potential model to quantify the uncertainty amplification resulting from employing ABC using a set of clinically relevant biomarkers. We hope that this work serves to guide the implementation and comparative assessment of Bayesian and ABC sampling techniques in biological models.

scholarly journals Modelling ourselves: what the free energy principle reveals about our implicit notions of representation

Synthese ◽  
2021 ◽  
Author(s):  
Matt Sims ◽  
Giovanni Pezzulo
Keyword(s):  
Free Energy ◽  
Philosophy Of Mind ◽  
Predictive Processing ◽  
Energy Principle ◽  
Philosophical Debate ◽  
Free Energy Principle ◽  
Self Organized ◽  
Functional Aspects ◽  
Structural Content ◽  
Unified View

AbstractPredictive processing theories are increasingly popular in philosophy of mind; such process theories often gain support from the Free Energy Principle (FEP)—a normative principle for adaptive self-organized systems. Yet there is a current and much discussed debate about conflicting philosophical interpretations of FEP, e.g., representational versus non-representational. Here we argue that these different interpretations depend on implicit assumptions about what qualifies (or fails to qualify) as representational. We deploy the Free Energy Principle (FEP) instrumentally to distinguish four main notions of representation, which focus on organizational, structural, content-related and functional aspects, respectively. The various ways that these different aspects matter in arriving at representational or non-representational interpretations of the Free Energy Principle are discussed. We also discuss how the Free Energy Principle may be seen as a unified view where terms that traditionally belong to different ontologies—e.g., notions of model and expectation versus notions of autopoiesis and synchronization—can be harmonized. However, rather than attempting to settle the representationalist versus non-representationalist debate and reveal something about what representations are simpliciter, this paper demonstrates how the Free Energy Principle may be used to reveal something about those partaking in the debate; namely, what our hidden assumptions about what representations are—assumptions that act as sometimes antithetical starting points in this persistent philosophical debate.

scholarly journals Is free-energy minimisation the mark of the cognitive?

2021 ◽  
Vol 36 (2) ◽  
Author(s):  
Julian Kiverstein ◽  
Matt Sims
Keyword(s):  
Nervous System ◽  
Free Energy ◽  
Problem Solving ◽  
Living Systems ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Energy Minimisation ◽  
Long Run ◽  
Evolution Of Life ◽  
Mark Of The Cognitive

AbstractA mark of the cognitive should allow us to specify theoretical principles for demarcating cognitive from non-cognitive causes of behaviour in organisms. Specific criteria are required to settle the question of when in the evolution of life cognition first emerged. An answer to this question should however avoid two pitfalls. It should avoid overintellectualising the minds of other organisms, ascribing to them cognitive capacities for which they have no need given the lives they lead within the niches they inhabit. But equally it should do justice to the remarkable flexibility and adaptiveness that can be observed in the behaviour of microorganisms that do not have a nervous system. We should resist seeking non-cognitive explanations of behaviour simply because an organism fails to exhibit human-like feats of thinking, reasoning and problem-solving. We will show how Karl Friston’s Free-Energy Principle (FEP) can serve as the basis for a mark of the cognitive that avoids the twin pitfalls of overintellectualising or underestimating the cognitive achievements of evolutionarily primitive organisms. The FEP purports to describe principles of organisation that any organism must instantiate if it is to remain well-adapted to its environment. Living systems from plants and microorganisms all the way up to humans act in ways that tend in the long run to minimise free energy. If the FEP provides a mark of the cognitive, as we will argue it does, it mandates that cognition should indeed be ascribed to plants, microorganisms and other organisms that lack a nervous system.

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