ArticleInterface focus2023
On Bayesian mechanics: a physics of and by beliefs.
Article in Interface focus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
36 citing papers in PubMed, 109 citations in OpenAlex.
- Differentiation of cortical areas: effects of free energy minimization with broken symmetry.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Active Inference and Functional Parametrisation: Differential Flatness and Smooth Random Realisation.Entropy (Basel, Switzerland) · 2026Article
- Motor and operative intentionality in computational phenomenology: from shallow to deep active inference models.Neuroscience of consciousness · 2026Article
- A Rosetta Stone hypothesis for neurophenomenology: mathematical predictions from predictive processing.Neuroscience of consciousness · 2026Article
- Decision, Inference, and Information: Formal Equivalences Under Active Inference.Entropy (Basel, Switzerland) · 2025Review
- The myth of the Bayesian brain.European journal of applied physiology · 2025Review
- The Physics and Metaphysics of Social Powers: Bridging Cognitive Processing and Social Dynamics, a New Perspective on Power Through Active Inference.Entropy (Basel, Switzerland) · 2025Article
- Life, its origin, and its distribution: a perspective from the Conway-Kochen Theorem and the Free Energy Principle.Communicative & integrative biology · 2025Review
- How do inner screens enable imaginative experience? Applying the free-energy principle directly to the study of conscious experience.Neuroscience of consciousness · 2025Article
- Flow and intuition: a systems neuroscience comparison.Neuroscience of consciousness · 2025Article
- From pixels to planning: scale-free active inference.Frontiers in network physiology · 2025Article
- Minimization of prediction errors during cerebral embryogenesis and the emergence of agency.Frontiers in systems neuroscience · 2025Article
- An entropic explanation of insistence on sameness in autism.Frontiers in computational neuroscience · 2025Article
- Deep computational neurophenomenology: a methodological framework for investigating the how of experience.Neuroscience of consciousness · 2025Article
- An Active-Inference Approach to Second-Person Neuroscience.Perspectives on psychological science : a journal of the Association for Psychological Science · 2024Review
- Cortical development in the structural model and free energy minimization.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- When the interoceptive and conceptual clash: The case of oppositional phenomenal self-modelling in Tourette syndrome.Cognitive, affective & behavioral neuroscience · 2024Review
- Making the Thermodynamic Cost of Active Inference Explicit.Entropy (Basel, Switzerland) · 2024Article
- Self-Improvising Memory: A Perspective on Memories as Agential, Dynamically Reinterpreting Cognitive Glue.Entropy (Basel, Switzerland) · 2024Article
- Complexity biomechanics: a case study of dragonfly wing design from constituting composite material to higher structural levels.Interface focus · 2024Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 4 countries.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.