このページは機械翻訳によって英語から日本語に翻訳されました。 英語のオリジナルをご覧ください。
Clinical researchers, computational psychiatry groups, medical-AI builders, and Institute fellows.
A research scaffold for clinical reasoning, computational psychiatry, interoception, and medical AI.
このページで
Medicine is a natural application area for active inference because clinical practice is perception and action under uncertainty: inferring hidden states of a patient from noisy observations, choosing tests and treatments that balance information gain against benefit, and revising models as new data arrive.
Clinical care couples latent-state inference (pathophysiology, psychological state, disease trajectory), uncertain observations, action under risk, and shifting preferences over outcomes. Expected free energy makes the trade-off between epistemic value (informative tests) and pragmatic value (desired outcomes) explicit, which mirrors how clinicians actually decide under measurement cost.
A domain report should separate three layers: formal generative models of symptoms or physiology (including interoceptive and homeostatic models), decision-support systems that select information-gathering or intervention policies, and accountable institutional workflows. Active digital twins, belief-space control for treatment, and introspective medical-AI architectures are the recurring engineering patterns.
The literature is still dominated by theory and simulation. The next pass should classify each source as reviewed literature, simulation, or deployed system, and prioritise computational-psychiatry reviews, interoception and placebo-analgesia models, oncology belief-space control, and clinical-LLM reliability work. Each claim should land in the citation registry before it appears on the public page.
Karl J. Friston (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience. DOI: 10.1038/nrn2787. Thomas Parr, Giovanni Pezzulo, Karl J. Friston (2022). Active Inference: The Free Energy Principle in Mind, Brain, and Behavior. MIT Press. Giovanni Pezzulo, Francesco Rigoli, Karl J. Friston (2017). Active Inference, homeostatic regulation and adaptive behavioural control. Progress in Neurobiology. DOI: 10.1016/j.pneurobio.2017.08.001. Lancelot Da Costa, Thomas Parr, Noor Sajid, Sebastijan Veselic, Victorita Neacsu, Karl J. Friston (2020). Active inference on discrete state-spaces: A synthesis. Journal of Mathematical Psychology. DOI: 10.1016/j.jmp.2020.102447.
キーの表面
Model symptoms, beliefs, uncertainty, and regulation as coupled inference and action under prior preferences.
Frame monitoring and intervention as policy selection by expected free energy, with explicit confidence and review boundaries.
Distinguish reviewed literature, Institute projects, and speculative research leads on every public page.
関連リソース
外部リンクは共有レジストリから解決されるため、訪問者向け目的地は中央集権的かつ確認可能な状態が保たれます。
Audience: Researcher
Public ecosystem shortlink for Institute context, projects, activities, and conceptual maps.
Audience: Developer
Public GitHub organization for Institute repositories and open-source work.
Audience: Developer
Project repository for multiagent Active Inference modeling work.
Audience: Developer
Generalized Notation Notation project repository for model communication.
Audience: Developer
Geospatial modeling repository connected to ecological and bioregional applications.
Audience: Researcher
Research and publication-oriented repository for the Active Inference Journal.
Audience: Contributor
Public Project Preparation shortlink for making proposed work legible and actionable.
Audience: Contributor
Public project measurement shortlink for reporting progress and outputs.
Audience: Researcher
Public Active Inference Ontology shortlink for shared conceptual infrastructure.
Audience: Researcher
External paper repository surfaced from the official research page.
Audience: Researcher
Reachable arXiv reference surfaced from the official research page.
Audience: Researcher
Reachable arXiv reference surfaced from the official research page.
公式ページ
Audience: Researcher
Official research entry point, currently resolving to the research overview.
Audience: Researcher
Official research overview page.
Audience: Developer
Official Knowledge Engineering page.
Audience: Developer
Official Active Blockference page.
Audience: Researcher
Official theoretical neurobiology page.
Audience: Contributor
Public projects shortlink for project directories and activity-linked work.
Audience: Contributor
Public project measurement shortlink for reporting progress and outputs.
Audience: Researcher
Public Active Inference Ontology shortlink for shared conceptual infrastructure.
リポジトリ
Audience: Researcher
Computational meta-analysis of Active Inference literature with nanopublication and knowledge-graph outputs.
Audience: Researcher
Ontology-oriented repository for shared Active Inference concepts and decentralized science knowledge infrastructure.
Audience: Developer
Notebook-based applied Active Inference work connected to blockchain-adjacent and generative modeling examples.
Audience: Developer
Python models and materials for ant-inspired multiagent Active Inference.
Audience: Researcher
Active Inference & Category Theory
Audience: Researcher
Public content repository for the Active Inference Journal and related publication infrastructure.
Audience: Researcher
Active Entity Ontology for Science
Audience: Developer
Public ants repository in the ActiveInferenceInstitute GitHub namespace.