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

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. (