ReviewPLoS computational biology2020
The two kinds of free energy and the Bayesian revolution.
Review in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- A Perovskite Memdiode-Based Neuromorphic in Silico Surrogate Model for Emulating Predictive Coding Failure in Diabetic Neuropathy.Neuroinformatics · 2026Article
- Sleep bruxism: a novel theoretical perspective through the lens of the free energy principle (FEP).Frontiers in behavioral neuroscience · 2026Article
- The phantastic organ: predictive processing in the brain.Frontiers in psychology · 2026Review
- Decision, Inference, and Information: Formal Equivalences Under Active Inference.Entropy (Basel, Switzerland) · 2025Review
- Distributionally robust free energy principle for decision-making.Nature communications · 2025Article
- Improving the Minimum Free Energy Principle to the Maximum Information Efficiency Principle.Entropy (Basel, Switzerland) · 2025Article
- Why collective behaviours self-organize to criticality: a primer on information-theoretic and thermodynamic utility measures.Royal Society open science · 2025Article
- A Semantic Generalization of Shannon's Information Theory and Applications.Entropy (Basel, Switzerland) · 2025Review
- Lifespan oscillatory dynamics in lexical production: A population-based MEG resting-state analysis.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Bayesian inference of relative fitness on high-throughput pooled competition assays.PLoS computational biology · 2024Article
- Conscious imagination vs. unconscious imagination: a contribution to the discussion with Amy Kind.Frontiers in psychology · 2024Article
- Bayesian inference of relative fitness on high-throughput pooled competition assays.bioRxiv : the preprint server for biology · 2023Article
- Switching state-space modeling of neural signal dynamics.PLoS computational biology · 2023Article
- Neural Field Continuum Limits and the Structure-Function Partitioning of Cognitive-Emotional Brain Networks.Biology · 2023Review
- Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network.Frontiers in computational neuroscience · 2023Review
- Stochastic surprisal: An inferential measurement of free energy in neural networks.Frontiers in neuroscience · 2023Article
- Jarzyski's Equality and Crooks' Fluctuation Theorem for General Markov Chains with Application to Decision-Making Systems.Entropy (Basel, Switzerland) · 2022Article
- Bounded rational decision-making models suggest capacity-limited concurrent motor planning in human posterior parietal and frontal cortex.PLoS computational biology · 2022Article
- The constrained disorder principle defines living organisms and provides a method for correcting disturbed biological systems.Computational and structural biotechnology journal · 2022Article
- Representing preorders with injective monotones.Theory and decision · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The concept of free energy has its origins in 19th century thermodynamics, but has recently found its way into the behavioral and neural sciences, where it has been promoted for its wide applicability and has even been suggested as a fundamental principle of understanding intelligent behavior and brain function. We argue that there are essentially two different notions of free energy in current models of intelligent agency, that can both be considered as applications of Bayesian inference to the problem of action selection: one that appears when trading off accuracy and uncertainty based on a general maximum entropy principle, and one that formulates action selection in terms of minimizing an error measure that quantifies deviations of beliefs and policies from given reference models. The first approach provides a normative rule for action selection in the face of model uncertainty or when information processing capabilities are limited. The second approach directly aims to formulate the action selection problem as an inference problem in the context of Bayesian brain theories, also known as Active Inference in the literature. We elucidate the main ideas and discuss critical technical and conceptual issues revolving around these two notions of free energy that both claim to apply at all levels of decision-making, from the high-level deliberation of reasoning down to the low-level information processing of perception.
Indexed as
Identifiers
What OpenQuestion holds
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.