Skip to content
Active Inference Ontology Website
Share
Explore
Active Inference Ontology

ActInf Ontology Definitons

Return to the home page.
Ontology ~ Definitions, Examples, Connections view
2
Search
List
Term
Proposed Definition 1
Proposed Definition 2
List
Term
Proposed Definition 1
Proposed Definition 2
Abstract Action
Abstract action prediction
Abstract Bayesian Inference
Abstract epistemic value
Abstract External State
Abstract Generative Model
Abstract Hidden State
Abstract Internal State
Abstract Sensory State
Abstract System
AbstractAccuracy
abstractCounterpart
(abstractCounterpart ?AB ?PHYS) relates a Physical entity to an Abstract one which is an idealized model in some dimension of the Physical entity.
Example: an could be stated to be the counterpart of an actual in a .
Action and Planning as Divergence Minimization
Action at a distance
Action Integral
Active Vision
refers to the process of visual perceptions, in terms of oculomotor
@Sensorimotor
@Behavior
and
@Cognitive System
@Generative Model
refers to regime of visual perceptions through dynamical perturbations of light over the retina. As if feeling the textured light reflected off the niche surfaces
Agency based model
Agency free model
Algorithm
Alignment
analogy
Appraisal theories of emotion
Attenuation of response
Augmented reality
Autopoiesis
Phenomena of a
@System
that recapitulates the material and informational causes of its own composition/existence.
Bayes-optimal control
Bayesian
Bayesian Brain
Bayesian surprise
Bethe approximation
Bottom-up attentional control
chaos
Circular causality
Cognitive Science
Cognitive System
Cognitivism
Collective behavior
Conceptual metaphor
Conditional density
Conditional Probability
Confidence
Congruence
Connectionism
Control (states)
Control theory
Counterfactual
Cybernetics
Decision-making
Within
@Active Inference
, this is the same as
@Policy selection
Density
Deontic Action
Development
Dissisipation
Distribution
divergence
Kullback-Leibler Divergence
Domain
Domain-generality
Domain-specificity
Dynamic Causal Modelling
Dynamic expectation maximization
Dynamicism
Ecology
EcoEvoDevo
Ecology, Evolution, Development
Embedded Embodied Encultured Enactive Inference
Embodied Cybernetic Complexity
Embodied Belief
Emotion
Empirical prior
Enactivism
Entropy
Ergodicity
Estimator
Event-related potential
Evolution
Expectation maximization
Expected Utility Theory
Experience of body ownership
Explaining Away
Explanation
Extended Cognition
Factor graph
Falsification
Far-from-equilibrium
Fokker-Planck Equation
Foraging
Forney
Friction
Friston's Law
functional magnetic resonance imaging
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 machine
Hierarchically Mechanistic Mind
Homeostasis
Homeostatic system
Hyperprior
Hypothesis
Information bottleneck
Instrumentalism
Interface
Interoception
Interoceptive sensitivity
Interpretation
Inverse problem
Latent cause
Lateral geniculate nucleus
Likelihood
Marginal approximation
Markovian Monism
@Markovian Monism
Marr's Levels of Description
Material science
Mean
Mean field approximation
Memory
Message Passing
Mismatch negativity
Mode
Model
Model accuracy
Model Inversion
@Model Inversion
Morphogenesis
Multisensory integration
Network
Neuronal Ensemble
Niche construction
Noisy signal
Non-linear dynamical systems
Optimal control
overfitting
Partition
Policy posterior
Policy prior
Precision
Prediction error
Prediction error minimization
Predictive Coding
Predictive Processing
Principle
@Principle
Probability distribution
Random variable
Realism
@Realism
Receptive field
Recognition density
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 outcome
Sentience
Shared Generative Model
Signal
Simulation
solenoidal
Sophisticated Inference
spike-timing dependent plasticity
Statistical manifold
Stigmergy
Stochastic
Subjective feeling states
Surprisal
Swarm
Symbol
Synergetics
Teams
Theory
Thermodynamic system
Thermostatistics
Thinking Through Other Minds
Top-down attentional control
Umwelt
Unidirectionality
Update
Variance
Variational Niche Construction
Von Economo neurons
Weak mixing
Working memory
World States
Active learning
Attracting set
Bayesian belief updating
Bayesian mechanics
Coarse graining
Continuous state space
Discrete state space
Epistemic foraging
Hermeneutics
Least action
link
Partially Observed Markov Decision Process
Renormalization
Variational message passing
Variational principle
Lagrangian
Path integral
Sensory observation
changing mind (cognition)
changing world (action)
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 system
s 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.”
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.”
normative
active processes
Approximate Posterior
A probability distribution (often denoted by “q”) used in
@Variational
@Bayesian Inference
to approximate the true (but unknown)
@Posterior
distribution.
active processes
changing the mind
changing the world
model evidence
predictive machine
Bayesian Model Selection
Blanket index
categorical
constraint
fitness
flow
Gauge theory
Helmholtz Free Energy
maximum caliber
path
phenotype
population
Quantum
Quantum-like
Qubit
Sensory State
Sensory states
sufficient statistic
time
Active Blockference
Analytical Philosophy
Category Theory
Continental Philosophy
DeSci
Exteroception
Filter
Helmholtz Decomposition
Proprioception
Quantum mechanics
Statistical Parametric Mapping
T-Maze
Accuracy
Broad sense: how “close to the mark” an
@Estimator
is.
Narrow sense: the expected or realized extent of
@Surprise
on an estimation, usually about
@Sense State
reflecting the
@Recognition density
Action
Broad sense: The dynamics, mechanisms, and measurements of
@Behavior
Narrow sense: The sequence of
@Active States
enacted by an
@Agent
via
@Policy selection
from
@Affordance
Action Planning
The selection of an
@Affordance
based upon
@Inference
of
@Expected Free Energy
Action Prediction
@Inference
on current and future
@Expectation
of
@Action
Active Inference
@Active Inference
is a
@Process Theory
related to
@Free Energy Principle
.
Active States
In the
@Friston Blanket
formalism, the
@Blanket State
are the
@Sense State
(incoming
@Sensory input
) and
@Active States
(outgoing influence of
@Policy selection
)
Affordance
Options or capacities for
@Action
by an
@Agent
Agency
The ability of an
@Agent
to engage in
@Action
in their
@Niche
and enact
@Goal-driven selection
or
@Policy selection
based upon
@Preference
Agent
Entity as modeled by
@Active Inference
, with
@Internal State
separated from
@External State
by
@Blanket State
Ambiguity
Broad sense: Extent to which stimuli have multiple plausible interpretations, requiring priors &/or
@Action
for disambiguation
Narrow sense: Specific model parameter used to model
@Uncertainty
, usually about sensory
@Perception
.
Attention
Broad sense:
@Generative Model
that is aware of some
@Stimulus
, reflected by its
@Salience
Narrow sense:
@Attention
modulates the the confidence on the
@Precision
of
@Sense State
, reflecting
@Sensory input
Bayesian Inference
As opposed to frequentist analysis,
@Bayesian Inference
uses a specified
@Prior
or
@Empirical prior
to
@Update
the distributional
@Posterior
Behavior
The sequence of
@Action
that an
@Agent
is observed to enact.
Belief
Broad sense: Felt sense by an
@Agent
of something being true, or confidence it is the case.
Narrow sense: the
@State
of a
@Random variable
in a
@Bayesian Inference
scheme.
Belief updating
@Belief updating
is changes in a
@Bayesian Inference
@Belief
through time.
Blanket State
Set of states in the
@Markov Blanket
@Partition
that make
@Internal State
and
@External State
have
@Conditional Probability
that are independent.
Cognition
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
.)
Complexity
The extent to which an
@Agent
must revise a
@Belief
to explain incoming
@Sensory observation
s.
The
@Kullback-Leibler Divergence
between the
@Prior
and
@Posterior
which is used in Bayesian model selection to find the simplest (least complex) model and avoid
@overfitting
on the noise inherent in
@Sensory observation
s.
Cue
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. ()
Culture
@Culture
is the
@Niche
for
@social
@Agent
, that structures their
@Regime of Attention
Data
Data are a set of values of qualitative or quantitative variables about one or more
@Agent
or
@object
.
Ensemble
Group of more than one
@Agent
.
Epistemic value
is the value of
@Information
gain or
@Expectation
of reduction in
@Uncertainty
about a
@State
with respect to a
@Policy
, used in
@Policy selection
Evidence
@Data
as recognized and interpreted by
@Generative Model
of
@Agent
Expectation
Within a
@Bayesian Inference
framework,
@Expectation
is an
@Estimator
about future timesteps
Expected Free Energy
Measure for performing
@Inference
on
@Action
over a given time horizon (
@Policy selection
,
@Action and Planning as Divergence Minimization
).
The two components of
@Expected Free Energy
are the imperative to satisfy
@Preference
s, and the penalty for failing to minimize
@Expectation
of
@Surprisal
.
External State
@State
s with
@Conditional density
independent from
@Internal State
, conditioned on
@Blanket State
.
Free Energy
Free Energy is an
@Information
Theoretic quantity that constitutes an upper bound on
@Surprisal
.
@Free Energy
can refer to various or multiple sub-types of
@Free Energy
:
@Variational Free Energy
@Expected Free Energy
@Free Energy
of the Expected Future
@Helmholtz Free Energy
Free Energy Principle
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 to
@Prediction error
.
A set of statistical principles that describe how
@Agent
s can maintain their
@self-organization
in the face of random fluctuations from the
@environment
.
Friston Blanket
@Markov Blanket
with partitioned
@Active States
and
@Sense State
.
Generalized Free Energy
Past
@Variational Free Energy
plus future
@Expected Free Energy
(each totaled over
@Policy
).
Generative Model
A formalism that describes the mapping between
@Hidden State
, and
@Expectation
s of
@Action Prediction
,
@Sensory outcome
.
@Recognition Model
@Update
@Internal State
@parameter
that correspond to
@External State
(including
@Hidden State
causes of
@environment
states),
@Blanket State
, and
@Internal State
(meta-modeling). In contrast,
@Generative Model
take those same
@Internal State
@parameter
@Estimator
and emit expected or plausible observations.
Generative Process
Underlying @dynamical process in the
@Niche
giving rise to
@Agent
@Observation
and @agent
@Action Prediction
Enactive ecological process using morphological computing processes where the
@Niche
@Regime of Attention
@morphogenesis and generative model interact to create an embodied learning dyanamic.
Hidden State
Unobserved variable in
@Bayesian Inference
, can reflect a
@Latent cause
.
Hierarchical Model
A hierarchy of
@Estimator
s, which operate at different spatiotemporal timescales (so they track features at different scales); all carrying out
@Predictive Processing
Inference
Process of reaching a (local or global) conclusion within a
@Model
, for example with
@Bayesian Inference
.
The process of using a
@Sensory observation
(observed variable, data) along with a known set of
@parameter
s to determine the state of an unknown,
@Latent cause
(unobserved variable).
Information
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
).
Information Geometry
A
@Statistical manifold
each of whose points corresponds to a
@Probability distribution
(e.g. the expectation and variance of a normal density).
Internal State
@State
s with
@Conditional density
independent from
@External State
, conditioned on
@Blanket State
.
Learning
Broad sense: Process of an
@Agent
engaged in
@Update
s to
@Cognition
(and possibly)
@Behavior
.
Narrow sense: Process of
@Bayesian Inference
where
@Generative Model
parameters undergo
@Belief updating
Living system
@Agent
engaged in
@Autopoiesis
Markov Blanket
@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 Decision Process
@Bayesian Inference
@Model
where
@Agent
@Generative Model
can implement
@Policy selection
on
@Affordance
s reflected by
@Active States
, while other features of the
@Generative Process
are outside the
@Control (states)
of the
@Agent
.
Multi-scale system