Ontology ~ Notes · Ontology Full Database

Accuracy

Action

Action Planning

Action prediction

Active Inference

Active Learning

Active States

Which states are internal/external? Which are autonomous states?

Action vs Active states?

Partitioning of blanket states into incoming (sense) and outgoing (action) statistical dependencies.

Have outgoing statistical dependencies with external states

Have outgoing statistical dependencies towards external states

Active Vision

Affordance

Agency

Agent

Ambiguity

Cannot describe when one thing becomes another -- e.g. forest and trees, or what makes someone rich, thresholds.

Some kind of undecidable uncertainty?

Only related to Observations~State mappings? Or other parameters? Does this have similar use as informal deployment of the term or not?

Attention

Machine learning // Conscious or Aware attention

Regimes of attention https://www.frontiersin.org/articles/10.3389/fpsyg.2016.01090/full https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00679/full how is this related to motor/visual and salience "Attention"

Can non-cultural ActInf agents have shared regimes of attention

Attention and Information?

Autopoiesis

Bayesian Inference

Behavior

Belief

Belief updating

Blanket States

Dave asks, If we enforce as a definition the observation that "internal states do not influence sensory states," do we exclude some examples of predictive processing internal to the CNS? E.g. a wine-taster systematically scans (differentially activates) various olefactory centers (S. Barwich, Smellosophy: "Olfactory receptors, as the interface of the olfactory system, actively structure stimulus input;" Jordan et al., “Active Sampling State Dynamically Enhances Olfactory Bulb Odor Representation," Neuron 98.

Cognitive psychologist Ulric Neisser coined the term "perceptual cycling," to describe perception as a cyclical process in the brain, suggesting that search patterns in foraging behavior filter input information. Alternating oscillation phases mirror the periodic sampling of sensory input and govern the responsiveness of particular brain regions, including their connectivity. Several neural populations are actively competing at any given time. So the brain is primed by its own mechanisms of input selectivity.

What are Markov Blankets? What is the usage in FEP?

Interface / Boundary states for systems and their environments

(Sense and Action)

Values of parameters of the (Markov) Blanket

Blanket states mediate Internal and External states

Things or "Boundaries between things"

Partitioning, how does this relate to boundaries in the real world?

Thermodynamic & Homeostatic systems. H systems do have T properties. But they are not the same thing. Two poles of the analogy. Media & Message. Memeology.

https://pubmed.ncbi.nlm.nih.gov/33607182/ "Recent characterisations of self-organising systems depend upon the presence of a 'Markov blanket': a statistical boundary that mediates the interactions between the inside and outside of a system."

What's the alternative here?

Cognition

Complexity

Cue

Culture

Data

Decision-making

Ensemble

Epistemic value

https://plato.stanford.edu/entries/epistemology/ --> Epistem = Knowledge

(Pragmatic and Epistemic) & (Extrinsic & Intrinsic) ---> Whats the relationship, are these the same?

Intrinsic motivation involves performing a task because it’s personally rewarding to you.Extrinsic motivation involves completing a task or exhibiting a behavior because of outside causes such as avoiding punishment or receiving a reward. The main difference between intrinsic and extrinsic motivation is that intrinsic motivation comes from within, and extrinsic motivation comes from outside. While both types of motivation are important, they have different effects on how you work. https://www.rochester.edu/emerging-leaders/understanding-intrinsic-and-extrinsic-motivation/

https://www.tandfonline.com/doi/abs/10.1080/17588928.2015.1020053?journalCode=pcns20 Minimizing expected free energy is therefore equivalent to maximizing extrinsic value or expected utility (defined in terms of prior preferences or goals), while maximizing information gain or intrinsic value (or reducing uncertainty about the causes of valuable outcomes).

From Karl: Epistemic value is the information gain or reduction in uncertainty about latent states afforded by the outcomes of a particular policy. It is variously known as Bayesian surprise, epistemic affordance, the value of information, intrinsic motivation and so on. Mathematically, it is the KL divergence between beliefs about latent states before and after the outcomes of a policy. Epistemic value is the value of a policy that is a functional of beliefs about the causes of sensations.

Ergodicity

Evidence

Expected Free Energy

External States

Realist and Instrumentalist

External/Internal/Interface

Is it important that the partitioning be this way?

Qualify & Quantify

What is the relevance of this partitioning scheme (e.g. with a blanket separating Internal/External states)

The partitions are set by us on something else observed/modeled.

From Colombo & Wright: "For free-energy theorists, the dynamics of such systems will appear to place an upper bound on their informational entropy, and to maximize the evidence for a model M of external states “entailed” by their characteristic properties. This behavior—they would conclude—can be expressed as approximate Bayesian (active) inference about the causes of sensory states in terms of minimizing variational free energy.Footnote6"

What happens to the generative model when we are asleep? Do we have a generative model at that time?

Is it the same model being used differently, but then is it a different model?

Internal & External states -- they are a partitioning, have to be separate.

Free Energy

Free Energy Principle

Friston Blanket

Generalized Free Energy

Generative model

Generative model of what?

Generative model is starting concept of Active Inference -- Distinction from FEP (?) -- Dynamics and behavior are starting from GM, action as well. All talks about terms should include this.

Generative Model &/of a Generative Process

Recognition models and Generative models.

Recognition model is from empirical observations to updates of inferred hidden states. Generative model is from inferred hidden states to plausible emitted observed states. This is the "tale of two densities" because models are distributions which are statistical densities.

"Processing" is often used in a uni-directional Recognition Model type way -- "Predictive Processing" entails and requires a Prediction

What is the relationship between conscious experience & Generative models? Why are some GM experienced or not? Meta-modeling?

GM of Sense + Action

Enactivism + Predictive Processing ---> PP does deal with action. However in Maria's perspective they do not pay as much attention to the environment, e.g. they are more focused on the organismal dynamics perhaps.

Generative Modeling is the key for perception?

Deep GM / Deep Inference.

Where is the body in GM?

Broadest most-applicable definition ----> Then we specify Computational, Embodied, Enacted.

Generative Process

Hidden state

Hierarchical Model

Inference

Information

Information Geometry

Internal States

What is an Internal state?

From Demakas et al 2020: "Imagine that every single state of being has a position in an abstract state space. There are 4 important kinds of states (i.e., dimensions) in this space: sensory states (e.g., the sound of a voice), active states (e.g., listening1), internal states (e.g., thoughts, feelings), and environmental states (e.g., location, context). These states are by definition the partitions afforded by Markov blanket."

Is this the only possible partitioning? Only one possible or only one for ActInf? ActInf with other partitionings?

Do partitions align with natural aspects/features/"systems" in the world?

Relationship with the topology of the action loop?

What are Internal States? Nesting of internal states depending on regime of attention, scale, Homeostatic, Cognitive

Utility of separating formal terms and definitions/notions -- Markov Blanket Action: is ...., separation from applications in domains (cell, psychology, social, computational)

Starting with the purely statistical Markov, Pearl, Friston, Beyond --> applications across domains.

Is enactivism realism?

Computational systems to test space of possible loops/partitions since empirical measurements are not always directly suggestive of particular partition

Tale of Two Densities: "The ‘causal bite’ of the generative model comes from the fact that it plays a role in policy selection by inducing free energy gradients (which then guide changes to beliefs about action). In other words, generative models are normative models of ‘what ought to be the case, given the kind of creature that I am’– they are realised physically through adaptive, belief-guided, normative actions that maintain the creature in its phenotypic states."

Inconstant or incompatible use of realism/instrumentalism, action-perception loops, interpretations of the priority of blanket vs. co-equal partitition, etc....

From Karl: Statistically, the existence of a Markov blanket means external states are conditionally independent of internal states, and vice versa, Given the blanket states. Generally, internal states can only influence active states.

Latent cause

Living system

Markov Blanket

Who is Markov / What is Markovian?

What does "blanket" or "blanketing" mean?

https://en.wikipedia.org/wiki/Markov_property A stochastic process has the Markov property if the conditional probability distribution of future states of the process (conditional on both past and present values) depends only upon the present state; that is, given the present, the future does not depend on the past.

Representing boundaries / boundary conditions -- where we have liminalitiy. Conditional independence separates 'things' out from their environment

Node partitioning scheme (where nodes are statistical variables) -- separating into Internal, External, and Blanket states. Blanket states render the Internal and External states conditionally independent.

Starting with System of Interest -- To define separation of System and Environment, we define the boundary of the system.

Difference between Physical boundary & Statistical insulation?

Separation of system from environment requires persistent boundary / Blanket. Will depend on the scale of analysis.

Nested Markov Blankets will have different realizations.

Examples of [Internal, External, Blanket states]

Cell [Cytoplasm Internal, Environment External, Membrane = Markov Blanket]

Markov Decision Process

Markovian Monism

Model Inversion

When you calculate a prior from a posterior and a likelihood, is that an example of model inversion?

If you must re-calculate specific priors - and these are priors that CANNOT be altered (the evolutionarily-cast-in-base-pairs homeostatic set points) - does something special happen? - maybe fugue, dissociation, fainting, panic, depressive paralysis, shell-shock, repression, "cognitive dissonance," delusion, hysteria?

Multi-scale system

Narrative (model)

Niche

Non-Equilibrium Steady State

Novelty

Observation

Particle

Perception

Policy

Policy selection

Posterior

Pragmatic value

What are the connections between Pragmatic/Epistemic Value and Affordances?

Generative model is performing action-selection (as constrained/weighted by E affordance matrix). The value of the Action decomposes into P/E Value ---> We also talk about P/E "actions" but this may not be proper use

Where does niche modification come into play --> e.g. preparing a book shelf.

Utility is definied by the specific situation.

How do the stories/beliefs we have in the world influence action selection? Affective inference.

Prediction

Principle

Process Theory

Recognition Models

Regime of Attention

Representation

Risk

Salience

Sense States

What do edges represent? E.g. labeling the edges

https://en.wikipedia.org/wiki/Dynamic_causal_modeling

Have incoming statistical dependencies with external states

Have outgoing statistical dependencies towards internal states

State

State space

Where is time in the state space? Synchronic & Diachronic.

State = Variable? Value the variable can hold? Space = area that the variables can exist with?

How do we represent CHANGE in state spaces? Constant updating? "Betweenness". It is "OF" a (dynamic) system, and "AS" the system itself.

https://en.wikipedia.org/wiki/State-space_representation "The state of the system can be represented as a state vector within that space."

Realist & Instrumentalist --- State space as being actually what occurs, vs. how we model it.

Any time you abstract out a system, there is a state space.

The internal state variables are the smallest possible subset of system variables that can represent the entire state of the system at any given time.

https://en.wikipedia.org/wiki/State_observer

Set of variables/parameters that describe a system.

https://en.wikipedia.org/wiki/Phase_space a phase space is a space in which all possible states of a system are represented, with each possible state corresponding to one unique point in the phase space.

Set of all variables/parameters that contextual or describe an action or outcome.

Stationary processes, Ergodicity, etc.

Surprise

System

System is the physical parts? Systems Engineering

Synergetics Subsystem, 265.05-06, 266.05, 1053.801, 1071.21 System, 168, 223.67, 224.30, 251.26, 261.01, 264.01, 265.04, 361-63, Chapter 4, 430.06, 501.10-11, 505.64, 505.71-74, 524.11, 526.10-19, 526.22-23, 526.25, 526.30-33, 527.25-26, 530.11, 531.04, 532.17, 538.03, 538.11, 542.01-05, 812.01, 831.01, 960.08, 986.730, 986.819, 986.850-57, 987.011-13, 1006.13, 1007.26, 1007.29, 1011.10-11, 1023.10-16, 1044.03-05, 1044.08, 1050.10, 1054.55, 1071.00-28, 1072.21, 1073.12, 1073.14, 1075.23, 1076.11

Open/Closed system from Thermo?

Open, Closed, Active Inference Systems? However we define system we want to make sure it is in the spirit of what it is for

from the SEBoK (1) A set of elements in interaction. (von Bertalanffy 1968) (2) combination of interacting elements organized to achieve one or more stated purposes (ISO/IEC/IEEE 2015) (3) A system is an arrangement of parts or elements that together exhibit behavior or meaning that the individual constituents do not. (INCOSE Fellows, 2019)

Varela & Maturana, Autopoiesis, Open systems. How to think about systems.

Realism and Instrumentalism

Nested subsystem (what Fuller calls "the system" as opposed to Universe).

Static, dynamic, open, closed -- what is the common feature? Where is the "between"? In e.g. a thermodynamic system. Where is the overlap among the different uses

One aspect: Two or more system elements and their betweenness

Second aspect: Relational insight

Systems, Agents. Intentionality of "betweenness" of the agent in their niche.

Function, Modularity, Physical Place

Temporal Depth

Uncertainty

Variational

Variational free-energy

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

Action

Action and Planning as Divergence Minimization

Action at a distance

Action Integral

Active Inference

Agency based model

Agency free model

Alignment (of internal states)

Appraisal theories of emotion

Attenuation of response

Augmented reality

Bayes-optimal control

Bayesian

Bayesian Brain

Bayesian surprise

Belief updating

Bottom-up attentional control

Cognitive Science

Cognitive System

Cognitivism

Collective behavior

Conditional density

Conditional Probability

Congruence

Connectionism

Control (states)

Control theory

Counterfactual

Cybernetics

Density

Deontic Action

Development

Dissisipation

Divergence (Kullback–Leibler)

Domain

Domain-generality

Domain-specificity

Dynamic causal modelling

Dynamic expectation maximization

Dynamicism

Ecology

Ecology, Evolution, Development

Embedded Embodied Encultured Enactive Inference

Embodied Cybernetic Complexity

EmbodiedBelief

Emotion

Empirical prior

Enactivism

Entropy

Estimator

Event-related potential (ERP)

Evolution

Expectation maximization

Expected Utility Theory

Experience of body ownership (EBO)

Explaining Away

Explanation

Extended Cognition

Falsification

Far-from-equilibrium

Fokker-Planck Equation

Foraging

Friston's Law

functional magnetic resonance imaging (fMRI)

Gaussian distribution

Generalized coordinates

Generalized Synchrony

Generative density

Generative modelling

Gestalt

Goal-driven selection

Gradient Descent

Group Renormalization Theory

Guidance signal

Habit learning/formation

Hamilton's Principle of Least Action

Helmholtz (inference) machine

Hierarchically Mechanistic Mind

Homeostasis

Homeostatic system

Hyperprior

Hypothesis

Information bottleneck (IB)

Interoception

Interoceptive sensitivity

Inverse problem

Lateral geniculate nucleus

Likelihood

Marr's Levels of Description

Material science

Memory

Message Passing

Mismatch negativity

Model

Model accuracy

Morphogenesis

Multisensory integration

Neuronal Ensemble

Niche construction

Noisy signal

Non-linear dynamical systems

Optimal control

Precision

Prediction error

Prediction error minimization

Predictive Coding

Predictive coding (PC)

Predictive Processing

Prior

Random variable

Receptive field

Recognition density

Representationalism

Reservoir Computing

Reward

Salience

Sample space

Selection bias

Selection history

Self-organization

Selfhood

Semi-Markovian

Sense of agency

Sensory attenuation

Sensory Data

Sensory input

Sensory outcome

Shared Generative Model ('Shared Narrative')

Signal

Simulation

Sophisticated Inference

spike-timing dependent plasticity

Stigmergy

Stochastic

Subjective feeling states

Surprisal

Synergetics

Teams

Theory

Thermodynamic system

Thermostatistics

Thinking Through Other Minds

Top-down attentional control

Umwelt

Unidirectionality or "mere" active inference

Variational Niche Construction

Von Economo neurons (VENs)

Weak mixing

Working memory

World States (World Systems)

Interface

active

area

attitude

backbone

causality

computer

concentration

concept

consensus

conversation

current

default-mode

dynamics

ego

energy

environment

error

feedback

field

framework

free

genetic

hierarchical

idea

increase

influence

interpretation

inverse

language

machine

metaphor

neuronal

object

objective

observer

parameter

part

perceptual inference

perspective

phase

physics

play

probability

Probably Approximately Correct (PAC)

problem

propositional

purpose

question

random

recognition

role

science

selection

self-organization

social

states

technology

understanding

resource

tree

abstractCounterpart

represents