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Cited by 30Embodied and Extended CognitionLanguage and cultural evolutionChild and Animal Learning Development

Federated inference and belief sharing

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Friston, Karl J., Parr, Thomas, Heins, Conor, Constant, Axel, Friedman, Daniel, Isomura, Takuya, Fields, Chris, Verbelen, Tim +3 more·2024·Neuroscience & Biobehavioral Reviews
DOI 10.1016/j.neubiorev.2023.105500PMC PMC11139662
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Abstract

This paper concerns the distributed intelligence or federated inference that emerges under belief-sharing among agents who share a common world-and world model. Imagine, for example, several animals keeping a lookout for predators. Their collective surveillance rests upon being able to communicate their beliefs-about what they see-among themselves. But, how is this possible? Here, we show how all the necessary components arise from minimising free energy. We use numerical studies to simulate the generation, acquisition and emergence of language in synthetic agents. Specifically, we consider inference, learning and selection as minimising the variational free energy of posterior (i.e., Bayesian) beliefs about the states, parameters and structure of generative models, respectively. The common theme-that attends these optimisation processes-is the selection of actions that minimise expected free energy, leading to active inference, learning and model selection (a.k.a., structure learning). We first illustrate the role of communication in resolving uncertainty about the latent states of a partially observed world, on which agents have complementary perspectives. We then consider the acquisition of the requisite language-entailed by a likelihood mapping from an agent's beliefs to their overt expression (e.g., speech)-showing that language can be transmitted across generations by active learning. Finally, we show that language is an emergent property of free energy minimisation, when agents operate within the same econiche. We conclude with a discussion of various perspectives on these phenomena; ranging from cultural niche construction, through federated learning, to the emergence of complexity in ensembles of self-organising systems.

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  • There is no standardized way of defining environments for Active Inference agents.

    “In general, we observe that there is no standardized way of defining environments for Active Inference agents.”

    From Reactive Environments for Active Inference Agents with RxEnvironments.jl (2025) · cites this paper as [10]

    The Software note (page unavailable) says generative models change between applications, and Fig. 4 in “Active inference and belief sharing” describes how this paper specifies its simulation. Neither passage addresses whether a standardized way of defining environments for active-inference agents exists. I searched for standardization, environment specification, and conventions in our copy. (LLM, conf=0.75)

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Cited by

Sorted by citations

Authors
Friston, K. J., Parr, T., Heins, C., Constant, A., Friedman, D., Isomura, T., Fields, C., Verbelen, T., Ramstead, M., Clippinger, J., & Frith, C. D.
Affiliations
  • Wellcome Centre for Human Neuroimaging (GB)
  • University of Konstanz (DE)
  • University of Sussex (GB)
  • University of California, Davis (US)
  • RIKEN Center for Brain Science (JP)
  • Tufts University (US)
  • VERSES (United States) (US)
  • MIT Media Lab (US)
Corresponding author
Friston, Karl J.
ORCID
Venue
Neuroscience & Biobehavioral Reviews
Publisher
Elsevier BV
Published
2024-01
Volume
156
Pages
105500
ISSN
0149-7634 (ISSN-L)
DOI
10.1016/j.neubiorev.2023.105500
PMC
PMC11139662
OpenAlex
W4389341134
Cited by
30
References
17
Citations by year
2026: 7 · 2025: 12 · 2024: 10 · 2023: 1
FWCI
1.13
Type
journal article
Language
en
Open access
Licence
Locations
  • Neuroscience & Biobehavioral Reviews (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion, cc-by)
  • Figshare (submittedVersion, cc-by)
  • KOPS (University of Konstanz) (submittedVersion, cc-by)
Funders
  • European Commission (HORIZON2020, 945539)
  • Horizon 2020 Framework Programme (#945539)
  • Japan Society for the Promotion of Science
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
  • Social Sciences › Cultural Studies › Language and cultural evolution
  • Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
Keywords
belief sharing, active inference, variational free energy, language emergence, language acquisition, model selection, structure learning, distributed intelligence, agent communication, uncertainty resolution, cultural niche construction, federated learning, self-organizing systems, generative models
MeSH terms
Animals, Bayes Theorem, Communication, Language, Speech, Uncertainty

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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