Ontologists, knowledge engineers, and researchers working on formal knowledge representation for Active Inference.
Maintaining and developing the public ontology for Active Inference and decentralized science.
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The Active Inference Ontology project maintains and extends the public ontology for the Active Inference framework. It provides structured, machine-readable definitions of concepts and their relationships, supporting decentralized science, reproducibility, and knowledge reuse.
The Active Inference Ontology formalizes the conceptual structure of Active Inference — defining terms, relationships, and hierarchies that enable consistent reasoning, cross-project compatibility, and machine-readable knowledge. The ontology supports the broader goal of open, decentralized Active Inference science.
The public ontology website and GitHub repository provide access to the current ontology, its documentation, and contribution pathways.
Ontologists, knowledge engineers, and researchers who work with formal knowledge representation are welcome to contribute. Background in description logics, RDF/OWL, or conceptual modeling is helpful.
Graph view
Every term in the Active Inference Ontology, colored by tag, with the connections declared between them. Select a concept to highlight what it links to; the full definitions are in the table below.
Ontology terms
The full public ontology (v5): each term with its tag, definition, and a correct example. The graph above shows how the terms connect.
429 terms across 8 tags
| Term | Tag | Definition | Example |
|---|---|---|---|
| Accuracy | Bayesian Statistics | Broad sense: how “close to the mark” an Estimator is. | A lower-resolution camera or fMRI has lower Accuracy and thus lesser capacity to map fine scale features of stimuli. |
| Ambiguity | Bayesian Statistics | Broad sense: Extent to which stimuli have multiple plausible interpretations, requiring priors &/or Action for disambiguation | The noisy readings from the thermometer resulted in high Ambiguity given the Sensory input to the Agent . |
| Bayesian Inference | Bayesian Statistics | As opposed to frequentist analysis, Bayesian Inference uses a specified Prior or Empirical prior to Update the distributional Posterior | Many of the key ideas of Bayesian Inference existed before Rev. Bayes, and in some cases reflect recent contributions from computational research. |
| Belief | Bayesian Statistics | Broad sense: Felt sense by an Agent of something being true, or confidence it is the case. | The Prior or Empirical prior in Bayesian Inference can be a Belief on Hidden State . |
| Belief updating | Bayesian Statistics | Belief updating is changes in a Bayesian Inference Belief through time. | The incoming Sensory Data resulted in Belief updating |
| Complexity | Bayesian Statistics | The extent to which an Agent must revise a Belief to explain incoming Sensory observations. | Model Complexity can refer to the number of predictor or independent variables or features that a model needs to take into account in order to make accurate prediction. |
| Data | Bayesian Statistics | Data are a set of values of qualitative or quantitative variables about one or more Agent or object . | These three readings from the thermometer constitute Data! |
| Expectation | Bayesian Statistics | Within a Bayesian Inference framework, Expectation is an Estimator about future timesteps | At timestep 1, the Agent made a prediction about Expected Free Energy through time, this was an Expectation about the future. |
| Inference | Bayesian Statistics | Process of reaching a (local or global) conclusion within a Model, for example with Bayesian Inference. | The researcher made Model of Active Vision where the Agent was doing Inference on Action (Action Planning , Action Prediction ) as well as Perception (perceptual inference ). |
| Information | Bayesian Statistics | Measured in bits, the reduction of Uncertainty on a Belief distribution of some type. Usually Syntactic (Shannon) but also can be Semantic (e.g. Bayesian ). | There is more maximum Information in 1 terabyte than in 1 gigabyte. |
| Learning | Bayesian Statistics | Broad sense: Process of an Agent engaged in Updates to Cognition (and possibly) Behavior. | The software agent engaged in Belief updating on internal parameters, this is technically Learning . |
| Outcome | Bayesian Statistics | If we consider the environment as a Generative Process that can be sampled when in a particular state, the statistical result (Data, Sensory observation) of the sampling is known as the outcome. | The Generative Process produces outcomes when we sample from it. |
| Posterior | Bayesian Statistics | The Update to the Prior after Observation has occurred | The Posterior distribution reflects our degrees of Belief about Latent causes after we see Sensory Data. |
| Prediction | Bayesian Statistics | An Estimator about a State in a Model at a future time. | After successfully Learning the structure of the Generative Process the Agent can make a Prediction about the future state of this Generative Process and the associated Sensory Data it will generate. |
| Prior | Bayesian Statistics | The initial or preceding state of a Belief in Bayesian Inference, before Sensory Data (Observation or Evidence ) occurs. | Active Vision uses Prior on Sensory input . |
| State | Bayesian Statistics | is the statistical, computational, or mathematical value for a parameter within the State space of a Model . | Blanket State is a type of State that partition Internal State from External State |
| State space | Bayesian Statistics | Set of variables/parameters that describe a System . | The State space of a Generative Model can be a Continuous state space or Discrete state space . |
| Stationarity | Bayesian Statistics | Of a Random variable , that it is described by parameters that are drawn from a Gaussian distribution and unchanging over the time horizon of analysis. | A common assumption of many time series algorithms is that the data exhibits Stationarity. |
| Surprise | Bayesian Statistics | In Bayesian Inference, Surprise is the negative log evidence, directly corresponding to the inverse of probability (high probability, low surprisal; low probability, high surprisal). The proxy that bounds surprisal is the difference between Prior and Posterior Distribution — how “surprising” Sensory Data are to the Generative Model of the Agent. | Surprisal cannot be minimized directly because it is involves calculating the Evidence term in Bayes’ Rule which generally involves an intractable integral over all possible states an organism can be in. |
| Temporal Depth | Bayesian Statistics | The length of a time window or horizon considered (longer time → deeper / more Temporal Depth ) | In deep temporal models, Temporal Depth or occurs because of the number of Counterfactual possibilities one must account for increases as more future states are modeled (see: https://www.frontiersin.org/articles/10.3389/fpsyg.2018.00579/full) |
| Uncertainty | Bayesian Statistics | In Bayesian Inference , a measure of the Expectation of Surprise (Entropy) of a Random variable (associated with its variance or inverse Precision ) | A measure of unpredictability or expected Surprise (cf, Entropy ). The Uncertainty about a Random variable is often quantified with its Variance (inverse Precision ). |
| Action | Action | Broad sense: The dynamics, mechanisms, and measurements of Behavior | Ants are continually involved in Action in real life (Realism) and/or in a Model (Instrumentalism) |
| Action Planning | Action | The selection of an Affordance based upon Inference of Expected Free Energy | The robot assessed its current and target location, then engaged in Action Planning to decide under time pressure how to navigate. |
| Agency | Action | The ability of an Agent to engage in Action in their Niche and enact Goal-driven selection or Policy selection based upon Preference | An Agent uses their Agency to sculpt their environment which makes it more predictable thereby minimizing the agent’sVariational Free Energy and maintaining its Markov Blanket in the face of random fluctuations. |
| Behavior | Action | The sequence of Action that an Agent is observed to enact. | I expect you to be on your best Behavior! The Active Inference Agent uses its selected Policy to take Action that we call its Behavior. |
| Policy | Action | Sequence of Actions, reflected by series of Active States as implemented in Policy selection which is Action Prediction or Action and Planning as Divergence Minimization | The sequence of Actions that an Agent plans to take is a Policy. |
| Policy selection | Action | The process of an Agent engaging in Action Planning from set of Affordance, in Active Inference based upon minimization of Expected Free Energy | When the Agent decided to go one way instead of the other, it was due to an internal process of Policy selection, specifically Action and Planning as Divergence Minimization. |
| Preference | Action | parameter in Bayesian Inference Markov Decision Process that ranks or scores the extent to how an Agent values Sensory input . | The Generative Model of the bacteria underwent parameter fitting (Belief updating / Learning ) on Action , guided by a Preference for medium but not high/low sugar concentration. |
| Active Inference | Free Energy | Active Inference is a Process Theory related to Free Energy Principle . | It is fun and rewarding to learn and apply Active Inference as a framework for Perception, Cognition , and Action — it is a Process Theory compatible with the Free Energy Principle. |
| Epistemic value | Free Energy | is the value of Information gain or Expectation of reduction in Uncertainty about a State with respect to a Policy, used in Policy selection | When the environment contains Uncertainty I would like to undertake the action of Foraging for the sake of it to see if I can gather more relevant Observations to explain what is going on around me. Such relevant Observations have Epistemic value for me as an Agent. |
| Expected Free Energy | Free Energy | Measure for performing Inference on Action over a given time horizon (Policy selection , Action and Planning as Divergence Minimization ). | The deep affective Inference Agent used Expected Free Energy calculation as a basis of Policy selection . |
| Free Energy | Free Energy | Free Energy is an Information Theoretic quantity that constitutes an upper bound on Surprisal . | Free Energy can refer to various different formulations and decompositions, that share some important key features. |
| Free Energy Principle | Free Energy | A generalization of Predictive Coding (PC) according to which organisms minimize an upper bound on the Entropy of Sensory input (or sensory signals) (the Free Energy). Under specific assumptions, Free Energy translates toPrediction error . | As a principle, the Free Energy Principle cannot be falsified. |
| Generalized Free Energy | Free Energy | Past Variational Free Energy plus future Expected Free Energy (each totaled over Policy). | Parr and Friston 2019 wrote “Crucially, this means the Generalized Free Energy reduces to the Variational Free Energy for outcomes that had been observed in the past.....Outcomes in the Generalized Free Energy formulation are represented explicitly as Beliefs. This means that the Prior over Sensory outcome is incorporated explicitly in the generative model.” |
| Pragmatic Value | Free Energy | Pragmatic Value is the benefit to an organism of a given Policy or Action , measured in terms of probability of a Policy leading to Expectation of Random variable values that are aligned with the Preference of the Agent | The Agent had Preferences for there to be more beans in the jar, so adding more beans was of Pragmatic Value for them. |
| Variational Free Energy | Free Energy | Measure that performs e.g. Inference on Sensory Data given a Generative Model , F. The two sources that composeVariational Free Energy are Model overfitting and Model accuracy | Variational Free Energy is a tractable way to compute an upper bound on Surprisal of a Generative Model given Data. |
| Active States | Markov Partitioning | In the Friston Blanket formalism, the Blanket State are the Sense State (incoming Sensory input) and Active States (outgoing influence of Policy selection ) | The Active States of the computer program are the statistical outputs that it presents, while the Sensory input Sense State are the incoming statistical dependencies. |
| Blanket State | Markov Partitioning | Set of states in the Markov Blanket Partition that make Internal State and External State have Conditional Probability that are independent. | I don’t care whether it is an Action or Sense State, as long as it is a Blanket State! |
| External State | Markov Partitioning | States with Conditional density independent from Internal State, conditioned on Blanket State. | In my Generative Model of temperature, the true temperature in the room is being modeled as an External State (Hidden State), so we will never know it. |
| Friston Blanket | Markov Partitioning | Markov Blanket with partitioned Active States and Sense State . | A Friston Blanket is a type of Markov Blanket where Sense States are associated with incoming Sensory Data and Action states are associated with outgoing Behavior of the Agent. |
| Internal State | Markov Partitioning | States with Conditional density independent from External State , conditioned on Blanket State. | When the Generative Process describes a forest outside, and the Blanket States describe the bark of the tree, the Internal States describe the core of the tree. |
| Markov Blanket | Markov Partitioning | Markov Partitioning Model of System, reflecting Agent as delineated from the Niche via an Interface. The Markov Blanket Blanket State reflect the State(s) upon which Internal State and External State are conditionally independent. | Markov Blankets are features of Maps (e.g. Bayesian Graphical Models), not of Territories (e.g. ant colonies or brains). |
| Markov Decision Process | Markov Partitioning | Bayesian Inference Model where Agent Generative Model can implement Policy selection on Affordances reflected by Active States, while other features of the Generative Process are outside the Control (states) of the Agent . | Whether it is fully observable or not, one common type of model used in control theory is a Markov Decision Process |
| Sense State | Markov Partitioning | In the Friston Blanket formalism, the Blanket State are the Sense State (incoming Sensory input) and Active States (outgoing influence of Policy selection ) | In this model, the Observations coming in from the thermometers are considered as Sense States. |
| Affordance | Agents in the Niche | Options or capacities for Action by an Agent (sometimes called “Affordance 3.0”) | “In this T-maze model, there are 3 Affordance for movement at the junction (Left, Right, Down).” |
| Agent | Agents in the Niche | Entity as modeled by Active Inference , with Internal State separated from External State by Blanket State | “The ant nestmate is the Agent in the Active InferAnts model” |
| Cognition | Agents in the Niche | An Agent modifying the weights of its Internal State for the purpose of Action Planning and/or Belief updating . (This is a @realistCounterpart of Goal-driven selection .) | BacillisAxelis345 engaged in Cognition to decide whether to eat or to escape. |
| Ensemble | Agents in the Niche | Group of more than one Agent. | The ant colony from a Behavioral, orCollective behavior perspective, is an Ensemble of nestmates. |
| Generative Model | Agents in the Niche | A formalism that describes the mapping between Hidden State, and Expectations of Action Prediction , Sensory outcome . | Partially Observed Markov Decision Processes are commonly used for computational modeling of Generative Models. |
| Generative Process | Agents in the Niche | Underlying @dynamical process in the Niche giving rise to Agent Observation and @agent Action Prediction | The Generative Process generates Sensory Data. |
| Niche | Agents in the Niche | Ecology System constituting the Generative Process (as Partitioned from the Agent who instantiates a Generative Model ). | Every ant lives in their ecological Niche. |
| Non-Equilibrium Steady State | Agents in the Niche | Technically, a Non-Equilibrium Steady State requires a solution to the Fokker Planck equation (i.e., density dynamics). A nonequilibrium steady-state solution entails solenoidal (i.e., conservative or divergence free) dynamics that break detailed balance (and underwrite stochastic chaos ). In other words, The dynamics of systems at Non-Equilibrium Steady State are not time reversible (unlike equilibrium steady states, in which the flow is entirely dissipative). | Blood sugar levels are dynamic and fluctuating, however they revisit characteristic states repeatedly, this can be described by a Non-Equilibrium Steady State. |
| Particle | Agents in the Niche | An Agent consisting of Blanket State and Internal State , partitioned off from Niche . | Internal State and Blanket States together constitute the Particle. |
| Recognition Model | Agents in the Niche | Recognition Model is the kind of Model that affords Variational Inference, which lets us calculate or approximate a probability distribution. Recognition Model is a synonym for Variational Model. | After constructing a Generative Model, an Agent can invert this model to obtain the Recognition Model which allows for the prediction of the Hidden State (causes) that generated some new Sensory Data. |
| Representation | Agents in the Niche | A structural correspondence between some Random variable inside a System and some Random variable outside the System (isomorphism being the strongest kind of correspondence), such that the Systemengages in Inference carried out by the System maintains the correspondence | The sterotypical neural pattern induced by a Stimulus is considered a Representation, at least by those who subscribe to Representationalism. |
| Attention | Perception | Broad sense: Generative Model that is aware of some Stimulus, reflected by its Salience | The Model instantly updated to the new Sensory Data because it was paying maximal Attention to the Stimulus. |
| Evidence | Perception | Data as recognized and interpreted by Generative Model of Agent | Every photon is like a piece of Evidence on the retina. |
| Observation | Perception | The Belief updating of an Internal State registered by a Sensory input, given the weighting assigned to that class of input in comparison with weighting of the competing Priors. (This is a narrow sense of “observation,” where the Agent is “looking for this kind of input.” This sense excludes situations where (a) an incoming stimulus with these attributes has already been explained-away or pre-discounted, or (b) the prior is so strongly weighted as to exclude updating in response to any inputs (other than, perhaps, “catastrophic” ones, as may occur in e.g. fainting, hysterical blindness).) | One Observation can make all the difference. |
| Perception | Perception | Posterior State Inference after each new Observation. | Visual Perception gives us many demonstrations of the characteristics of our Generative Model — for example saccades, the blind spot, and blink supression. |
| Salience | Perception | The extent to which a Cue commands the Attention of an Agent given their Regime of Attention | Salience is related to how relevant a given Stimulus appears to be. |
| Hierarchical Model | Systems | A hierarchy of Estimators, which operate at different spatiotemporal timescales (so they track features at different scales); all carrying out Predictive Processing | A Nested Model is a Hierarchical Model, for example the Hierarchically Mechanistic Mind. |
| Living system | Systems | Agent engaged in Autopoiesis | A body is a Living system. |
| Multi-scale system | Systems | Realism framing of Hierarchical Model | The Generative Model of counties within states within countries, was a Multi-scale system |
| System | Systems | Set of relations described by State space of a Model . | George Mobus argues that Systems have some fundamental properties such as structure and function. |
| Abstract Accuracy | |||
| Abstract Action | |||
| Abstract action prediction | |||
| Abstract Bayesian Inference | |||
| Abstract epistemic value | |||
| Abstract External State | |||
| Abstract Generative Model | |||
| Abstract Internal State | |||
| Abstract Sensory State | |||
| Abstract System | |||
| abstractCounterpart | (abstractCounterpart ?AB ?PHYS) relates a Physical entity to an Abstract one which is an idealized model in some dimension of the Physical entity. | ||
| Action and Planning as Divergence Minimization | |||
| Action at a distance | |||
| Action Integral | |||
| Active Blockference | |||
| Active learning | |||
| active processes | |||
| Active Vision | refers to the process of visual perceptions, in terms of oculomotor Sensorimotor Behavior and Cognitive System Generative Model | Active Vision | |
| affect | |||
| Agency based model | |||
| Agency free model | |||
| Algorithm | |||
| Alignment | |||
| analogy | |||
| Analytical Philosophy | |||
| anticipation | |||
| Appraisal theories of emotion | |||
| Artificial | |||
| Attenuation of response | |||
| Attracting set | |||
| Augmented reality | |||
| Autopoiesis | Phenomena of a System that recapitulates the material and informational causes of its own composition/existence. | In the right niche, cells can be considered to exhibit Autopoiesis at the System level. | |
| Bayes-optimal control | |||
| Bayesian | |||
| Bayesian belief updating | |||
| Bayesian Brain | |||
| Bayesian mechanics | |||
| Bayesian Model Selection | |||
| Bayesian surprise | |||
| Bethe approximation | |||
| Blanket index | |||
| Bottom-up attentional control | |||
| categorical | |||
| category | |||
| Category Theory | |||
| changing mind (cognition) | |||
| changing the mind | |||
| changing the world | |||
| changing world (action) | |||
| chaos | |||
| Circular causality | |||
| Co-category | |||
| Coarse graining | |||
| Cognitive Science | |||
| Cognitive System | |||
| Cognitivism | |||
| colimit | |||
| Collective behavior | |||
| Conceptual metaphor | |||
| Conditional density | |||
| Conditional Probability | |||
| Confidence | |||
| Congruence | |||
| Connectionism | |||
| constraint | |||
| Continental Philosophy | |||
| Continuous state space | |||
| Control (states) | |||
| Control theory | |||
| Counterfactual | |||
| Cybernetics | |||
| Decision-making | Within Active Inference , this is the same as Policy selection | ||
| Deflationary | |||
| Density | |||
| Deontic Action | |||
| DeSci | |||
| Development | |||
| Discrete state space | |||
| Dissipation | |||
| Distribution | |||
| divergence | |||
| Domain | |||
| Domain-generality | |||
| Domain-specificity | |||
| Dynamic Causal Modelling | |||
| Dynamic expectation maximization | |||
| Dynamicism | |||
| EcoEvoDevo | Ecology, Evolution, Development | ||
| Ecology | |||
| Effective | |||
| Effectivity | |||
| Embedded Embodied Encultured Enactive Inference | |||
| Embodied Belief | |||
| Embodied Cybernetic Complexity | |||
| Emotion | |||
| Empirical prior | |||
| Enactivism | |||
| Entropy | |||
| Epistemic foraging | |||
| Ergodicity | |||
| Estimator | |||
| Event-related potential | |||
| Evolution | |||
| Expectation maximization | |||
| Expected Utility Theory | |||
| Experience of body ownership | |||
| Explaining Away | |||
| Explanation | |||
| Extended Cognition | |||
| Exteroception | |||
| Factor graph | |||
| Falsification | |||
| Far-from-equilibrium | |||
| Filter | |||
| fitness | |||
| flow | |||
| Fokker-Planck Equation | |||
| Foraging | |||
| Forney | |||
| frequentist | |||
| Friction | |||
| Friston's Law | |||
| functional magnetic resonance imaging (FMRi) | |||
| Functor | |||
| Gauge theory | |||
| Gaussian distribution | |||
| Generalized coordinates | |||
| Generalized Synchrony | |||
| Generative density | |||
| Generative modelling | |||
| Gestalt | |||
| Goal-driven selection | |||
| Gradient Descent | |||
| Graphical | |||
| Group Renormalization Theory | |||
| Guidance signal | |||
| Habit learning/formation | |||
| Hamilton's Principle of Least Action | |||
| Helmholtz Decomposition | |||
| Helmholtz Free Energy | |||
| Helmholtz machine | |||
| Hermeneutics | |||
| Hierarchically Mechanistic Mind | |||
| High road | One of two roads (arguments) that lead to the Free Energy Principle as a possible conclusion which starts with philosophical questions about what properties a thing must have to “exist” (i.e. it must be measurable) and then uses principles of Autopoiesis and non-equilibrium Thermodynamic systems from a statistical perspective to show what kinds of systems could continue to maintain themselves over time (see Friston 2019: Beyond the Desert Landscape and the other road, the Low road ). “The high road stands in for a top-down approach that starts by asking fundamental questions about the necessary properties things must possess if they exist. Using mathematical (variational) principles, once can then show that existence is an embodied exchange of a creatures with its environment - that necessarily entails predictive processing as one aspect of self-evidencing mechanics.” | The High road to the Free Energy Principle starts by talking about random Non-linear dynamical systems in general without a specific focus on biological organisms with brains. | |
| Homeostasis | |||
| Homeostatic system | |||
| Hyperprior | |||
| Hypothesis | |||
| Inflationary | |||
| Information bottleneck | |||
| Instrumentalism | |||
| intelligence | |||
| Interface | |||
| Interoception | |||
| Interoceptive sensitivity | |||
| Interpretation | |||
| Inverse problem | |||
| Kullback-Leibler Divergence | |||
| Lagrangian | |||
| Latent cause | |||
| Lateral geniculate nucleus | |||
| Least action | |||
| Lens | |||
| Likelihood | |||
| link | |||
| Low road | One of two roads (arguments) that lead to the Free Energy Principle as a possible conclusion which starts with fundamental questions from neuroscience and psychology about the nature of perception in biological organisms within a changing environment (see Friston 2019: Beyond the Desert Landscape and the other road, the High road ). “The low road is to pursue the agenda established by Kant and Helmholtz to generalize - in a bottom up way - the capacity for inference and prediction to see how far it takes us in understand embodied exchange with the environment.” | The Low road to the Free Energy Principle starts by looking at how biological organisms perceive their environment and take actions within it to develop a notion about how they can successfully predict the next state they will be in (Perception as Hypothesis testing). | |
| Marginal approximation | |||
| Markovian Monism | Markovian Monism | ||
| Marr's Levels of Description | |||
| matching | |||
| Material science | |||
| maximum caliber | |||
| Mean | |||
| Mean field approximation | |||
| Memory | |||
| Message Passing | |||
| Mismatch negativity | |||
| Mode | |||
| Model | |||
| Model accuracy | |||
| model evidence | |||
| Model Inversion | Model Inversion | ||
| Monad | |||
| morphism | |||
| Morphogenesis | |||
| Multisensory integration | |||
| Natural | |||
| Nested | |||
| Network | |||
| Neuronal Ensemble | |||
| Niche construction | |||
| Noisy signal | |||
| Non-linear dynamical systems | |||
| normative | |||
| Optimal control | |||
| overfitting | |||
| Partially Observed Markov Decision Process | |||
| Partition | |||
| path | |||
| Path integral | |||
| Path of Least Action | |||
| phenotype | |||
| Policy posterior | |||
| Policy prior | |||
| population | |||
| Precariousness | |||
| Precision | |||
| Prediction error | |||
| Prediction error minimization | |||
| Predictive Coding | |||
| predictive machine | |||
| Predictive Processing | |||
| Principle | Principle | ||
| Probability distribution | |||
| Proprioception | |||
| Quantum | |||
| Quantum mechanics | |||
| Quantum-like | |||
| Qubit | |||
| Random variable | |||
| Realism | Realism | ||
| Receptive field | |||
| Recognition density | |||
| Renormalization | |||
| Representationalism | |||
| Reservoir Computing | |||
| Reward | |||
| Risk | Risk | ||
| Sample space | |||
| Selection bias | |||
| Selection history | |||
| Self-organization | |||
| Selfhood | |||
| Semi-Markovian | |||
| Sense of agency | |||
| Sensorimotor | |||
| Sensory attenuation | |||
| Sensory Data | |||
| Sensory input | |||
| Sensory observation | |||
| Sensory outcome | |||
| Sensory State | |||
| Sensory states | |||
| Sentience | being ‘‘responsive to sensory impressions’’ through adaptive internal processes | ||
| Shared Generative Model | |||
| Signal | |||
| Simulation | |||
| solenoidal | |||
| Sophisticated Inference | |||
| spike-timing dependent plasticity | |||
| Statistical manifold | |||
| Statistical Parametric Mapping | |||
| Stigmergy | |||
| Stochastic | |||
| Subjective feeling states | |||
| sufficient statistic | |||
| Surprisal | |||
| Swarm | |||
| Symbol | |||
| Synergetics | |||
| T-Maze | |||
| Teams | |||
| Theory | |||
| Thermodynamic system | |||
| Thermostatistics | |||
| Thing | |||
| Thinking Through Other Minds | |||
| time | |||
| Top-down attentional control | |||
| tracking | |||
| Umwelt | |||
| Unidirectionality | |||
| Update | |||
| Variance | |||
| Variational message passing | |||
| Variational Niche Construction | |||
| Variational principle | |||
| Von Economo neurons | |||
| Weak mixing | |||
| Working memory | |||
| World States | |||
| Cue | Information | a Stimulus , event, object , or Guidance signal that serves to guide Behavior , such as a retrieval cue, or that acts as a @Signal to the presentation of another stimulus, event, or object, such as an unconditioned stimulus or reinforcement. (dictionary.apa.org) | |
| Information Geometry | Information | A Statistical manifold each of whose points corresponds to a Probability distribution (e.g. the expectation and variance of a normal density). | |
| Action Prediction | Action | Inference on current and future Expectation of Action | The Generative Model over the next few timesteps with respect to Active States , is the Action Prediction . |
| Process Theory | Free Energy | A hypothesis or Model proposal for how a Principle is realized (e.g. the Free Energy Principle) | Friston in 2018 wrote “The distinction is between a State [theory] and Process Theory ; i.e., the difference between a normative principle that things may or may not conform to, and a Process Theory or hypothesis about how that principle is realized” |
| Variational | Free Energy | Biologically: Said of the behavior of a Neuronal Ensemble that minimizes the error of quantities selected by that ensemble, by implementing an algorithm that approximates calculus of variations. | |
| Culture | Agents in the Niche | Culture is the Niche for social Agent, that structures their Regime of Attention | |
| Narrative | Agents in the Niche | Information used by Agent in context of entity- and event-oriented Cognition, specifically Hierarchical Model (Hierarchically Mechanistic Mind ). | |
| Novelty | Perception | The Internal State assumed by an Agent‘s epistemic Affordance, when unable to immediately (e.g. locally) resolve Uncertainty about the contingencies — i.e. the opportunity to resolve Uncertainty about ‘what would happen if I did that?’ (The Precision of this assumed Internal State has a distinctive, e.g. multimodal, distribution, i.e. exhibits Ambiguity .) | |
| Regime of Attention | Perception | feedback mechanisms among practices in a Culture of scaffolding individuals’ Attention, that guide Agents’ style of Attention; act as determined by bodily, language, and contextual Cues in a given community; and are encoded in higher levels of the cortical hierarchy. | |
| active | |||
| area | |||
| backbone | |||
| brain | |||
| causality | |||
| classical physics | |||
| computer | |||
| concentration | |||
| concept | |||
| condition | |||
| consensus | |||
| conversation | |||
| current | |||
| default-mode | |||
| dynamics | |||
| ecosystem | |||
| ego | |||
| energy | |||
| environment | |||
| error | |||
| feedback | |||
| field | |||
| framework | |||
| free | For no cost. | ||
| genetic | |||
| hierarchical | |||
| idea | |||
| increase | |||
| influence | |||
| inverse | |||
| language | |||
| Logic | |||
| machine | |||
| matrix | |||
| neuronal | |||
| object | |||
| objective | |||
| observer | |||
| Ontology-to-Model Link | |||
| opportunity | |||
| parameter | |||
| part | |||
| perceptual inference | |||
| perspective | |||
| phase | |||
| physics | |||
| play | |||
| probability | |||
| Probably Approximately Correct | |||
| problem | |||
| propositional | |||
| Propositional attitude | |||
| Psychological attitude | |||
| purpose | |||
| question | |||
| random | |||
| recognition | |||
| relative entropy | Synonym for Kullback-Leibler Divergence | ||
| represents | SUMO relation (represents ?THING ?ENTITY) means that ?THING in some way indicates, expresses, connotes, pictures, describes, etc. ?ENTITY. The Predicates containsInformation and realization are subrelations of represents. | ||
| resource | |||
| role | |||
| science | |||
| selection | |||
| situation | |||
| social | |||
| states | |||
| Stimulus | |||
| technology | |||
| trajectory | |||
| transition | |||
| tree | |||
| understanding | |||
| witness |
Key surfaces
Related resources
External links are resolved from the shared registry so visitor-facing destinations stay centralized and checkable.
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Learning repository for getting started with Active Inference materials and pathways.
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Computational meta-analysis of Active Inference literature with nanopublication and knowledge-graph outputs.
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Active Inference and RxInfer.jl
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Notebook-based applied Active Inference work connected to blockchain-adjacent and generative modeling examples.
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Python models and materials for ant-inspired multiagent Active Inference.
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Active Inference & Category Theory
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