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Active Inference Ontology
Ontology ~ Notes
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Term
Tag
Notes_1
Notes_2
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Notes_12
Notes_13
1
Accuracy
Core
2
Action
Core
3
Action Planning
Core
4
Action prediction
Core
5
Active Inference
Core
6
Active Learning
Core
7
Active States
Core
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
8
Active Vision
Core
9
Affordance
Core
10
Agency
Core
11
Agent
Core
12
Ambiguity
Core
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?
13
Attention
Core
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?
14
Autopoiesis
Core
15
Bayesian Inference
Core
16
Behavior
Core
17
Belief
Core
18
Belief updating
Core
19
Blanket States
Core
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?
20
Cognition
Core
21
Complexity
Core
22
Cue
Core
23
Culture
Core
24
Data
Core
25
Decision-making
Core
26
Ensemble
Core
27
Epistemic value
Core
(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.
28
Ergodicity
Core
29
Evidence
Core
30
Expected Free Energy
Core
31
External States
Core
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.
32
Free Energy
Core
33
Free Energy Principle
Core
34
Friston Blanket
Core
35
Generalized Free Energy
Core
36
Generative model
Core
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.
37
Generative Process
Core
38
Hidden state
Core
39
Hierarchical Model
Core
40
Inference
Core
41
Information
Core
42
Information Geometry
Core
43
Internal States
Core
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.
44
Latent cause
Core
45
Living system
Core
46
Markov Blanket
Core
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]
47
Markov Decision Process
Core
48
Markovian Monism
Core
49
Model Inversion
Core
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?
50
Multi-scale system
Core
51
Narrative (model)
Core
52
Niche
Core
53
Non-Equilibrium Steady State
Core
54
Novelty
Core
55
Observation
Core
56
Particle
Core
57
Perception
Core
58
Policy
Core
59
Policy selection
Core
60
Posterior
Core
61
Pragmatic value
Core
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.
62
Prediction
Core
63
Principle
Core
64
Process Theory
Core
65
Recognition Models
Core
66
Regime of Attention
Core
67
Representation
Core
68
Risk
Core
69
Salience
Core
70
Sense States
Core
What do edges represent? E.g. labeling the edges
Have incoming statistical dependencies with external states
Have outgoing statistical dependencies towards internal states
71
State
Core
72
State space
Core
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.
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.
73
Surprise
Core
74
System
Core
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
75
Temporal Depth
Core
76
Uncertainty
Core
77
Variational
Core
78
Variational free-energy
Core
79
Abstract Action
Supplement
80
Abstract action prediction
Supplement
81
Abstract Bayesian Inference
Supplement
82
Abstract epistemic value
Supplement
83
Abstract External State
Supplement
84
Abstract Generative Model
Supplement
85
Abstract Hidden State
Supplement
86
Abstract Internal State
Supplement
87
Abstract Sensory State
Supplement
88
Abstract System
Supplement
89
AbstractAccuracy
Supplement
90
Action
Supplement
91
Action and Planning as Divergence Minimization
Supplement
92
Action at a distance
Supplement
93
Action Integral
Supplement
94
Active Inference
Supplement
95
Agency based model
Supplement
96
Agency free model
Supplement
97
Alignment (of internal states)
Supplement
98
Appraisal theories of emotion
Supplement
99
Attenuation of response
Supplement
100
Augmented reality
Supplement
101
Bayes-optimal control
Supplement
102
Bayesian
Supplement
103
Bayesian Brain
Supplement
104
Bayesian surprise
Supplement
105
Belief updating
Supplement
106
Bottom-up attentional control
Supplement
107
Cognitive Science
Supplement
108
Cognitive System
Supplement
109
Cognitivism
Supplement
110
Collective behavior
Supplement
111
Conditional density
Supplement
112
Conditional Probability
Supplement
113
Congruence
Supplement
114
Connectionism
Supplement
115
Control (states)
Supplement
116
Control theory
Supplement
117
Counterfactual
Supplement
118
Cybernetics
Supplement
119
Density
Supplement
120
Deontic Action
Supplement
121
Development
Supplement
122
Dissisipation
Supplement
123
Divergence (Kullback–Leibler)
Supplement
124
Domain
Supplement
125
Domain-generality
Supplement
126
Domain-specificity
Supplement
127
Dynamic causal modelling
Supplement
128
Dynamic expectation maximization
Supplement
129
Dynamicism
Supplement
130
Ecology
Supplement
131
Ecology, Evolution, Development
Supplement
132
Embedded Embodied Encultured Enactive Inference
Supplement
133
Embodied Cybernetic Complexity
Supplement
134
EmbodiedBelief
Supplement
135
Emotion
Supplement
136
Empirical prior
Supplement
137
Enactivism
Supplement
138
Entropy
Supplement
139
Estimator
Supplement
140
Event-related potential (ERP)
Supplement
141
Evolution
Supplement
142
Expectation maximization
Supplement
143
Expected Utility Theory
Supplement
144
Experience of body ownership (EBO)
Supplement
145
Explaining Away
Supplement
146
Explanation
Supplement
147
Extended Cognition
Supplement
148
Falsification
Supplement
149
Far-from-equilibrium
Supplement
150
Fokker-Planck Equation
Supplement
151
Foraging
Supplement
152
Friston's Law
Supplement
153
functional magnetic resonance imaging (fMRI)
Supplement
154
Gaussian distribution
Supplement
155
Generalized coordinates
Supplement
156
Generalized Synchrony
Supplement
157
Generative density
Supplement
158
Generative modelling
Supplement
159
Gestalt
Supplement
160
Goal-driven selection
Supplement
161
Gradient Descent
Supplement
162
Group Renormalization Theory
Supplement
163
Guidance signal
Supplement
164
Habit learning/formation
Supplement
165
Hamilton's Principle of Least Action
Supplement
166
Helmholtz (inference) machine
Supplement
167
Hierarchically Mechanistic Mind
Supplement
168
Homeostasis
Supplement
169
Homeostatic system
Supplement
170
Homeostatic system
Supplement
171
Hyperprior
Supplement
172
Hypothesis
Supplement
173
Information bottleneck (IB)
Supplement
174
Interoception
Supplement
175
Interoceptive sensitivity
Supplement
176
Inverse problem
Supplement
177
Lateral geniculate nucleus
Supplement
178
Likelihood
Supplement
179
Marr's Levels of Description
Supplement
180
Material science
Supplement
181
Memory
Supplement
182
Message Passing
Supplement
183
Mismatch negativity
Supplement
184
Model
Supplement
185
Model accuracy
Supplement
186
Morphogenesis
Supplement
187
Multisensory integration
Supplement
188
Neuronal Ensemble
Supplement
189
Niche construction
Supplement
190
Noisy signal
Supplement
191
Non-linear dynamical systems
Supplement
192
Optimal control
Supplement
193
Precision
Supplement
194
Prediction error
Supplement
195
Prediction error minimization
Supplement
196
Predictive Coding
Supplement
197
Predictive coding (PC)
Supplement
198
Predictive Processing
Supplement
199
Prior
Supplement
200
Random variable
Supplement
201
Receptive field
Supplement
202
Recognition density
Supplement
203
Representationalism
Supplement
204
Reservoir Computing
Supplement
205
Reward
Supplement
206
Salience
Supplement
207
Sample space
Supplement
208
Selection bias
Supplement
209
Selection history
Supplement
210
Self-organization
Supplement
211
Selfhood
Supplement
212
Semi-Markovian
Supplement
213
Sense of agency
Supplement
214
Sensory attenuation
Supplement
215
Sensory Data
Supplement
216
Sensory input
Supplement
217
Sensory outcome
Supplement
218
Shared Generative Model ('Shared Narrative')
Supplement
219
Signal
Supplement
220
Simulation
Supplement
221
Sophisticated Inference
Supplement
222
spike-timing dependent plasticity
Supplement
223
Stigmergy
Supplement
224
Stochastic
Supplement
225
Subjective feeling states
Supplement
226
Surprisal
Supplement
227
Synergetics
Supplement
228
Teams
Supplement
229
Theory
Supplement
230
Thermodynamic system
Supplement
231
Thermostatistics
Supplement
232
Thinking Through Other Minds
Supplement
233
Top-down attentional control
Supplement
234
Umwelt
Supplement
235
Unidirectionality or "mere" active inference
Supplement
236
Variational Niche Construction
Supplement
237
Von Economo neurons (VENs)
Supplement
238
Weak mixing
Supplement
239
Working memory
Supplement
240
World States (World Systems)
Supplement
241
Interface
Supplement
242
active
Entailed
243
area
Entailed
244
attitude
Entailed
245
backbone
Entailed
246
causality
Entailed
247
computer
Entailed
248
concentration
Entailed
249
concept
Entailed
250
consensus
Entailed
251
conversation
Entailed
252
current
Entailed
253
default-mode
Entailed
254
dynamics
Entailed
255
ego
Entailed
256
energy
Entailed
257
environment
Entailed
258
error
Entailed
259
feedback
Entailed
260
field
Entailed
261
framework
Entailed
262
free
Entailed
263
genetic
Entailed
264
hierarchical
Entailed
265
idea
Entailed
266
increase
Entailed
267
influence
Entailed
268
interpretation
Entailed
269
inverse
Entailed
270
language
Entailed
271
machine
Entailed
272
metaphor
Entailed
273
neuronal
Entailed
274
object
Entailed
275
objective
Entailed
276
observer
Entailed
277
parameter
Entailed
278
part
Entailed
279
perceptual inference
Entailed
280
perspective
Entailed
281
phase
Entailed
282
physics
Entailed
283
play
Entailed
284
probability
Entailed
285
Probably Approximately Correct (PAC)
Entailed
286
problem
Entailed
287
propositional
Entailed
288
purpose
Entailed
289
question
Entailed
290
random
Entailed
291
recognition
Entailed
292
role
Entailed
293
science
Entailed
294
selection
Entailed
295
self-organization
Entailed
296
social
Entailed
297
states
Entailed
298
technology
Entailed
299
understanding
Entailed
300
resource
Entailed
301
tree
Entailed
302
abstractCounterpart
Entailed
303
represents
Entailed
There are no rows in this table
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