diff --git a/README.md b/README.md index 53cef89..c807b6a 100644 --- a/README.md +++ b/README.md @@ -1 +1,8 @@ -# Active_Inference_Ontology \ No newline at end of file +# Active_Inference_Ontology +Live version at +https://coda.io/@active-inference-institute/active-inference-ontology-website + +Work in progress + +More information at +https://www.activeinference.org/home diff --git a/v4 (12-12-2022 snapshot)/Ontology _ Definitions, Examples, Connections view.csv b/v4 (12-12-2022 snapshot)/Ontology _ Definitions, Examples, Connections view.csv new file mode 100644 index 0000000..cd35d4d --- /dev/null +++ b/v4 (12-12-2022 snapshot)/Ontology _ Definitions, Examples, Connections view.csv @@ -0,0 +1,461 @@ +List,Tag,Term,Proposed Definition 1,Proposed Definition 2,Correct Examples,Incorrect Examples,Connections +Core,Bayesian Statistics,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 theRecognition density ","A lower-resolution camera or fMRI has lower Accuracy and thus lesser capacity to map fine scale features of stimuli. ","The true distribution was 10 +/- 1 and the person guessed 10 +/- 5, thus they had higher Accuracy. I was sure the green car was really blue; this inaccuracy was caused by the dim light. ","Accuracy is the inverse of Uncertainty " +Core,Bayesian Statistics,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 . ",The noisy readings from the thermometer resulted in high Ambiguity given the Sensory input to the Agent .,"I am curious about what is inside the box, there is Ambiguity about what it might be. ","Uncertainty on Hidden State givenSensory input " +Core,Bayesian Statistics,Bayesian Inference,"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.",The frequentist t-test is a classical example of Bayesian Inference .,"As a matter of determining the ratio of populations to sample, the following is instructive > https://aeon.co/videos/what-is-it-to-be-bayesian-the-pretty-simple-math-modelling-behind-a-big-data-buzzword" +Core,Bayesian Statistics,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. + +",The Prior or Empirical prior in Bayesian Inference can be a Belief on Hidden State .,"A Belief in Bayesian Inference requires Subjective feeling states or conscious awareness. ","A Belief is the most likely Hypothesis that provides an Explanation for the cause of Sense State, where a cause here is a Hidden State of the Generative Process." +Core,Bayesian Statistics,Belief updating,"Belief updating is changes in a Bayesian Inference Belief through time. ",,"The incoming Sensory Data resulted in Belief updating ","I promise that if you vote for me, even if I change my mind due to new Information coming in, I will never undergo Belief updating — so you know exactly what you get with me! ",In belief updating the Prior is altered to reflect incoporation of new Evidence presented to the model from the Generative Process. +Core,Bayesian Statistics,Expectation,"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. ","Although the scientist made a Prediction about the future using a Model , it was not an Expectation since they were not waiting for it to be realized. ","Anticipation, Prediction, Prediction error . + +" +Core,Bayesian Statistics,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 parameters to determine the state of an unknown, Latent cause (unobserved variable).","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 ). ","The ball rolled downhill using Inference on Policy selection, sometimes veering more to the left and other times more to the right. ","Action (more generally) and perceptual inference and Bayesian Inference (more specifically). " +Core,Bayesian Statistics,Learning,"Broad sense: Process of an Agent engaged in Updates to Cognition (and possibly) Behavior. + +","Narrow sense: Process of Bayesian Inference where Generative Model parameters undergo Belief updating ","The software agent engaged in Belief updating on internal parameters, this is technically Learning . ","Every day we change, but from a Bayesian Inference perspective it is only Learning if the Belief updating is adaptive. ","Learning model parameters is called parameter estimation and is obtained by using Sensory observations and previously inferred Hidden State. In Bayesian Inference parameters are treated as Random variables. " +Core,Bayesian Statistics,Outcome,"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 Data produced by sampling a Generative Process (a joint distribution on states and outcomes).,The Generative Process produces outcomes when we sample from it.,I was worried about the Outcome of my decision.,"Generative modelling involves using outcomes to approximate a statistical model of the Generative Process. " +Core,Bayesian Statistics,Posterior,"The Update to the Prior after Observation has occurred ","In Bayes’ theorem, the Posterior is equal to the product of the Likelihood and Prior divided by the model evidence.","The Posterior distribution reflects our degrees of Belief about Latent causes after we see Sensory Data. ",The Posterior distribution can always be trivially obtained by solving Bayes’ theorem.,"Solving an Inverse problem to go from Outcomes given a State determining a State given an Outcome. In representation learning the Posterior is called the Recognition Model since it “recognizes” the Latent cause that underlie the generation of the Sensory outcomes. " +Core,Bayesian Statistics,Prediction,"An Estimator about a State in a Model at a future time. ",The process of using a learned Generative Model to forecast what value a future Hidden State will be.,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.,A magic 8-ball can make a Prediction that will reliably be true.,The top-down priors aim at Prediction the incoming Sensory Data based on what the Agent has learned about the environment. An incorrect Prediction results in a Prediction error which the Agent attempts to minimize in the long run in the process of Prediction error minimization. +Core,Bayesian Statistics,Prior,"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 .,We arrested someone with no Priors.,"In the Active Inference (Continuous state space formulation), the Prior is represented by a Stochastic State space equation which represents the Agent‘s belief about how the Hidden State in the environment change over time." +Core,Bayesian Statistics,State,"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 ","California is the State with the best honey on the West Coast. ","111. Situation, Condition, State (from here) + +The words, situation and condition, imply, something accidental, and transitory; with this difference, that situation, respects outward circumstances; condition, those, within the matter referred to; whereas, that of state,implies, something, more habitual, and lasting. + +We, generally, use the word, situation, as relative, to affairs, rank or fortune; condition, with respect, to the nature, quality or property of a thing; and that, of state, applying it to health, or, our well or ill-being. + +We say, our situation is bad; when we are surrounded with difficulties: that a building is in bad condition; when out of repair; and, that some persons enjoy but an indifferent state of health. + +An ill-state of health, added to a bad situation of affairs, is a condition, into which, every man must, naturally, dread the falling. + +Such is the condition, and vicissitude of human life, that, the most prudent men, have, often, found themselves in perplex|ing situations; and from a state of happiness, have, as it were, through the perverseness of fortune, fallen into one most wretched and deplorable." +Core,Bayesian Statistics,Stationarity,"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.,I am exhibiting Stationarity when I stop walking.,"A common assumption in many time series techniques is that the data are at Stationarity . + +A stationary process has the property that the mean, variance and autocorrelation structure do not change over time. Stationarity can be defined in precise mathematical terms, but for our purpose we mean a flat looking series, without trend, constant variance over time, a constant autocorrelation structure over time and no periodic fluctuations (seasonality). + +For practical purposes, stationarity can usually be determined from a run sequence plot. + +https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc442.htm" +Core,Bayesian Statistics,Surprise,"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.","Surprise (also known as Surprisal or self-Information), is a quantity that, according to the Free Energy Principle, must be minimized in order for an Agent to survive; Variational Free Energy provides an upper bound on Surprisal and is minimized instead of minimizing Surprisal directly. + +",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.,"When they walk into the room, yell “Surprise“! ",An organism that minimizes Surprisal can maintain Non-Equilibrium Steady State and will consistently revisit an Attracting set of states. Minimizing Surprisal is equivalent to maximizing model evidence. +Core,Bayesian Statistics,Temporal Depth,"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)","The longer the queue, the greater the Temporal Depth.","From Breaking down unitization: Is the whole greater than the sum of its parts? “Depth” as a measured difference applied to change (time passing) as Memory and Cognition . “Graf and Schacter (1989) first described unitization as the process whereby multiple, separate, items become represented as a single unit, either through perceiving or conceiving of a structure (e.g., semantic meaning) that would connect the disparate units.” Which leaves us with at least two ways of “structuring” time units (episodes and cycles (diachronic))." +Core,Bayesian Statistics,Uncertainty,"In Bayesian Inference , a measure of the Expectation of Surprise (Entropy) of a Random variable (associated with its variance or inverse Precision )",Random fluctuations around the true value of a quantity we are trying to measure that are a result of uncontrolled variables in the environment or measurement apparatus.,"A measure of unpredictability or expected Surprise (cf, Entropy ). The Uncertainty about a Random variable is often quantified with its Variance (inverse Precision ).",I felt a lot of Uncertainty after that job interview.,"From Basics of Estimating Measurement Uncertainty https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2556585/There is a table of terminology of the science of measurement (metrology) that includes common metrological terms and their definitions, all of which recognize that making measurement comparisons leads to some degree of measurement uncertainty. " +Core,Action,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 ","Ants are continually involved in Action in real life (Realism) and/or in a Model (Instrumentalism) ","I love this band’s first song, can’t wait till the Action really begins! ","Action of the Agent is selected by Action Planning . As the consequences of Action are not fully known, the Expected Free Energy is calculated over Affordance and used for Variational Free Energy Action and Planning as Divergence Minimization " +Core,Action,Action Planning,"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.","The Agent used Action Planning algorithms for Variational Free Energy Inference on Sensory input ","Action Planning draws from set of Affordances and uses Expected Free Energy Inference, also known as Action Prediction ." +Core,Action,Action Prediction,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 .","The Agent inferred what Affordances it had, this process is known as Action Planning or Action Prediction .","Action Prediction is an enabling factor for Action Planning and Policy selection " +Core,Action,Agency,"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.,I got a new picture taken at the passport Agency.,Agent has Agency +Core,Action,Behavior,The sequence of Action that an Agent is observed to enact.,,The Active Inference Agent uses its selected Policy to take Action that we call its Behavior.,I expect you to be on your best Behavior!, +Core,Action,Policy,"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. ",What insurance Policy do you recommend for my house?,"Policy selection in Active Inference is about Prediction error minimization or minimization of Expected Free Energy " +Core,Action,Policy selection,"The process of an Agent engaging in Action Planning from set of Affordance, in Active Inference based upon minimization of Expected Free Energy ",,,,"Similar to Decision-making " +Core,Action,Preference,"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. ","All Action results in Agents that realize their Preference in terms of Sensory outcome . ","In the Discrete state space formulation of Active Inference, the Preferences or habits of an Agent can be encapsulated in a Prior Preference Distribution, a target distribution of States the agent wishes to end up due to its phenotype and other habits it has picked up throughout its existence." +Core,Free Energy,Active Inference,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 . ",,The conclusion of Active Inference is a Hypothesis (conjecture) about the feasibility of adopting certain Policy (strategy profiles) that the Agent makes a Generative Model with Temporal Depth. +Core,Free Energy,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 Preferences, and the penalty for failing to minimize Expectation of Surprisal.",The deep affective Inference Agent used Expected Free Energy calculation as a basis of Policy selection .,"The amount of calories that the Living system has at Non-Equilibrium Steady State is the Expected Free Energy . ","Expected Free Energy can be seen as.... " +Core,Free Energy,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 + +* ....",,, +Core,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 toPrediction error . ",A set of statistical principles that describe how Agents can maintain their self-organization in the face of random fluctuations from the environment.,,,"An Agent that is consistent with the Free Energy Principle will be able to minimize Variational Free Energy by taking Actions or using Perception in a way that maximizes the Evidence for its Generative Model of the environment. + +The Free Energy Principle is a more general statement about specific kinds of self-organization in random Non-linear dynamical systems assuming that these systems have certain properties like Ergodicity (weakly mixing) and a Markov Blanket. According to the Free Energy Principle, systems that minimize Variational Free Energy will maintain their low (thermodynamic) Entropy structure through Autopoiesis in the face of random fluctuations in the environment. To do so, they must take Actions that maintain the integrity of their Markov Blanket and develop a Generative Model that can successfully predict future states of the environment based on the Sensory Data they receive through the Markov Blanket." +Core,Free Energy,Generalized 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.”",, +Core,Free Energy,Pragmatic Value,"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 ",Pragmatic value describes the extent to which a given action is aligned with rewarding preferences over sensory outcomes.,,, +Core,Free Energy,Process Theory,"Dynamic or mechanistic Model or framework that is consistent with a broader or non-mechanistic/non-operational Principle, such as 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”",, +Core,Free Energy,Variational,"Biologically: Said of the behavior of aNeuronal Ensemble that minimizes the error of quantities selected by that ensemble, by implementing an algorithm that approximates calculus of variations.",Mathematically: Using calculus of variationsto find functions that optimize the values of quantities that depend on those functions.,,, +Core,Free Energy,Variational 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",,,, +Core,Markov Partitioning,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 )",,"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. ","Canada and Kenya have many people involved in dance, really you could say these two are Active States ","Active States and Sense State make Blanket State, which partition Internal State and External State in the Markov Blanket partitioning formalism. " +Core,Markov Partitioning,Blanket State,"Set of states in the Markov Blanket Partition that make Internal State and External State have Conditional Probability that are independent. ",,,, +Core,Markov Partitioning,External State,"States with Conditional density independent from Internal State, conditioned on Blanket State. ",,,, +Core,Markov Partitioning,Friston Blanket,"Markov Blanket with partitioned Active States and Sense State . ",,,, +Core,Markov Partitioning,Hidden State,"Unobserved variable in Bayesian Inference , can reflect a Latent cause . ",,,, +Core,Markov Partitioning,Internal State,"States with Conditional density independent from External State , conditioned on Blanket State. ",,,, +Core,Markov Partitioning,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. ",,,, +Core,Markov Partitioning,Markov Decision Process,"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 . ",,,,"Is Markov Blanket one way, and thus the Friston Blanket has x2 @markovboundaries linked to the decision making process. The probability and Preference Information Geometry between the x2 markov boundaries at each nested levels being updated gives the @temporality at the heart of @activeinference" +Core,Markov Partitioning,Sense State,"In the Friston Blanket formalism, the Blanket State are the Sense State (incoming Sensory input) and Active States (outgoing influence of Policy selection )",,,, +Core,Agents in the Niche,Affordance,"Options or capacities for Action by an Agent ",,"“In this T-maze model, there are 3 Affordance for movement at the junction (Left, Right, Down).” ","“He didn’t have the Affordance to do that, but he did it anyway”. ", +Core,Agents in the Niche,Agent," 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” ",“Calcium carbonate is the anti-caking Agent in this cookie”,Agent has Agency +Core,Agents in the Niche,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 .) ",,BacillisAxelis345 engaged in Cognition to decide whether to eat or to escape.,, +Core,Agents in the Niche,Culture,"Culture is the Niche for social Agent, that structures their Regime of Attention ",,,, +Core,Agents in the Niche,Ensemble,Group of more than one Agent.,,"The ant colony from a Behavior / Collective behavior perspective is an Ensemble of nestmates. ",,"Can have Group Renormalization Theory aspect or Stigmergy or Swarm " +Core,Agents in the Niche,Generative Model,"A formalism that describes the mapping between Hidden State, and Expectations 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.",,, +Core,Agents in the Niche,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.,The Generative Process generates Sensory Data.,The Agent uses its Generative Process to determine future Hidden State of the environment.,"The Generative Process exists in certain States that are Hidden State from the perspective of the Agent. When in a state, from the statistical perspective, it can generate a Sensory outcome which is sensed by the Agent. The agent will use this Sensory Data to form a Generative Model which may or may not match the true Generative Process but will nonetheless allow it to predict future outcomes drawn from the Generative Process if it has a good Generative Model." +Core,Agents in the Niche,Narrative,"Information used by Agent in context of entity- and event-oriented Cognition, specifically Hierarchical Model (Hierarchically Mechanistic Mind ). ",,,I wrote a Narrative about my time in graduate school., +Core,Agents in the Niche,Niche,"Ecology System constituting the Generative Process (as Partitioned from the Agent who instantiates a Generative Model ). ",,,, +Core,Agents in the Niche,Non-Equilibrium Steady State,"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).","Generally, a Non-Equilibrium Steady State refers to a System with dynamics that are unchanging, or at Stationarity in some State. ",,,"Similar to Stationarity " +Core,Agents in the Niche,Particle,"An Agent consisting of Blanket State and Internal State , partitioned off from Niche .",,,, +Core,Agents in the Niche,Recognition Model,"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.","In Dayan and Abbot (2001), the probability of a Hidden State (causes) given Sensory Data (effects) under some parameter. ","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.",, +Core,Agents in the Niche,Representation,"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",,,, +Core,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 ",,,"Attention is structured in complex sociocultural Systems to realize a Regime of Attention " +Core,Perception,Evidence,"Data as recognized and interpreted by Generative Model of Agent ",,,, +Core,Perception,"Novelty ","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 .)",,,, +Core,Perception,Observation,"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).)","Any Sensory input, either discrete-valued or continuous-valued. (This is a broad sense.)",,, +Core,Perception,Perception,Posterior State Inference after each new Observation.,,,, +Core,Perception,Regime of Attention,"feedback mechanisms between 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. ",,,, +Core,Perception,Salience,"The extent to which a Cue commands the Attention of an Agent given their Regime of Attention ",,,,"Related to Attention, for example of Affordance " +Core,"Information ",Complexity,"The extent to which an Agent must revise a Belief to explain incoming Sensory observations. ",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 observations.,,, +Core,"Information ",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. (dictionary.apa.org)",,,,"Approach to discussing Symbol in the Niche and social Culture patterns. " +Core,"Information ",Data,Data are a set of values of qualitative or quantitative variables about one or more Agent or object .,,,,"Evidence is Data that is evaluated by Agent in the context of their Generative Model " +Core,"Information ",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 ",,"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. ",,"It is variously known as Bayesian surprise, epistemic affordance, the value of information, intrinsic motivation and so on." +Core,"Information ",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 ). ",,"There is more maximum Information in 1 terabyte than in 1 gigabyte. ","I listened to that podcast and the file checksum was OK but the Information was modified relative to the reference version. ", +Core,"Information ",Information Geometry,A Statistical manifold each of whose points corresponds to a Probability distribution (e.g. the expectation and variance of a normal density).,,,, +Core,Systems,Hierarchical Model,"A hierarchy of Estimators, which operate at different spatiotemporal timescales (so they track features at different scales); all carrying out Predictive Processing ",,,, +Core,Systems,Living system,"Agent engaged in Autopoiesis ",,,, +Core,Systems,Multi-scale system,"Realism framing of Hierarchical Model ",,,, +Core,Systems,State space,Set of variables/parameters that describe a System .,A state space is the set of all possible configurations of a system,,, +Core,Systems,System,"Set of relations described by State space of a Model . ",Differentiable and Integratable in terms of Variables and functions.,,, +Supplement,,Abstract Action,,,,, +Supplement,,Abstract action prediction,,,,, +Supplement,,Abstract Bayesian Inference,,,,, +Supplement,,Abstract epistemic value,,,,, +Supplement,,Abstract External State,,,,, +Supplement,,Abstract Generative Model,,,,, +Supplement,,Abstract Hidden State,,,,, +Supplement,,Abstract Internal State,,,,, +Supplement,,Abstract Sensory State,,,,, +Supplement,,Abstract System,,,,, +Supplement,,AbstractAccuracy,,,,, +Supplement,,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 Abstract GraphNode could be stated to be the counterpart of an actual Computer in a ComputerNetwork.,,, +Supplement,,Action and Planning as Divergence Minimization,,,,, +Supplement,,Action at a distance,,,,, +Supplement,,Action Integral,,,,, +Supplement,,Active Blockference,,,,, +Supplement,,Active learning,,,,, +Supplement,,active processes,,,,, +Supplement,,active processes,,,,, +Supplement,,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,"Active Vision ",,"Epistemic value -driven Learning or Foraging " +Supplement,,Agency based model,,,,, +Supplement,,Agency free model,,,,, +Supplement,,Algorithm,,,,, +Supplement,,Alignment,,,,, +Supplement,,analogy,,,,, +Supplement,,Analytical Philosophy,,,,, +Supplement,,Appraisal theories of emotion,,,,, +Supplement,,Attenuation of response,,,,, +Supplement,,Attracting set,,,,, +Supplement,,Augmented reality,,,,, +Supplement,,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.","The pile of sand quickly dissipated in the wind, however I still think it is my favorite example of long-range Autopoiesis . ", +Supplement,,Bayes-optimal control,,,,, +Supplement,,Bayesian,,,,, +Supplement,,Bayesian belief updating,,,,, +Supplement,,Bayesian Brain,,,,, +Supplement,,Bayesian mechanics,,,,, +Supplement,,Bayesian Model Selection,,,,, +Supplement,,Bayesian surprise,,,,, +Supplement,,Bethe approximation,,,,, +Supplement,,Blanket index,,,,, +Supplement,,Bottom-up attentional control,,,,, +Supplement,,categorical,,,,, +Supplement,,Category Theory,,,,, +Supplement,,changing mind (cognition),,,,, +Supplement,,changing the mind,,,,, +Supplement,,changing the world,,,,, +Supplement,,changing world (action),,,,, +Supplement,,chaos,,,,, +Supplement,,Circular causality,,,,, +Supplement,,Coarse graining,,,,, +Supplement,,Cognitive Science,,,,, +Supplement,,Cognitive System,,,,, +Supplement,,Cognitivism,,,,, +Supplement,,Collective behavior,,,,, +Supplement,,Conceptual metaphor,,,,, +Supplement,,Conditional density,,,,, +Supplement,,Conditional Probability,,,,, +Supplement,,Confidence,,,,, +Supplement,,Congruence,,,,, +Supplement,,Connectionism,,,,, +Supplement,,constraint,,,,, +Supplement,,Continental Philosophy,,,,, +Supplement,,Continuous state space,,,,, +Supplement,,Control (states),,,,, +Supplement,,Control theory,,,,, +Supplement,,Counterfactual,,,,, +Supplement,,Cybernetics,,,,, +Supplement,,Decision-making,"Within Active Inference , this is the same as Policy selection ",,,, +Supplement,,Density,,,,, +Supplement,,Deontic Action,,,,, +Supplement,,DeSci,,,,, +Supplement,,Development,,,,, +Supplement,,Discrete state space,,,,, +Supplement,,Dissisipation,,,,, +Supplement,,Distribution,,,,, +Supplement,,divergence,,,,, +Supplement,,Domain,,,,, +Supplement,,Domain-generality,,,,, +Supplement,,Domain-specificity,,,,, +Supplement,,Dynamic Causal Modelling,,,,, +Supplement,,Dynamic expectation maximization,,,,, +Supplement,,Dynamicism,,,,, +Supplement,,EcoEvoDevo,"Ecology, Evolution, Development",,,, +Supplement,,Ecology,,,,, +Supplement,,Embedded Embodied Encultured Enactive Inference,,,,, +Supplement,,Embodied Belief,,,,, +Supplement,,Embodied Cybernetic Complexity,,,,, +Supplement,,Emotion,,,,, +Supplement,,Empirical prior,,,,, +Supplement,,Enactivism,,,,, +Supplement,,Entropy,,,,, +Supplement,,Epistemic foraging,,,,, +Supplement,,Ergodicity,,,,, +Supplement,,Estimator,,,,, +Supplement,,Event-related potential,,,,, +Supplement,,Evolution,,,,, +Supplement,,Expectation maximization,,,,, +Supplement,,Expected Utility Theory,,,,, +Supplement,,Experience of body ownership,,,,, +Supplement,,Explaining Away,,,,, +Supplement,,Explanation,,,,, +Supplement,,Extended Cognition,,,,, +Supplement,,Exteroception,,,,, +Supplement,,Factor graph,,,,, +Supplement,,Falsification,,,,, +Supplement,,Far-from-equilibrium,,,,, +Supplement,,Filter,,,,, +Supplement,,fitness,,,,, +Supplement,,flow,,,,, +Supplement,,Fokker-Planck Equation,,,,, +Supplement,,"Foraging ",,,,, +Supplement,,Forney,,,,, +Supplement,,Friction,,,,, +Supplement,,Friston's Law,,,,, +Supplement,,"functional magnetic resonance imaging ",,,,, +Supplement,,Gauge theory,,,,, +Supplement,,Gaussian distribution,,,,, +Supplement,,Generalized coordinates,,,,, +Supplement,,Generalized Synchrony,,,,, +Supplement,,Generative density,,,,, +Supplement,,Generative modelling,,,,, +Supplement,,Gestalt,,,,, +Supplement,,Goal-driven selection,,,,, +Supplement,,Gradient Descent,,,,, +Supplement,,Graphical,,,,, +Supplement,,Group Renormalization Theory,,,,, +Supplement,,Guidance signal,,,,, +Supplement,,Habit learning/formation,,,,, +Supplement,,Hamilton's Principle of Least Action,,,,, +Supplement,,Helmholtz Decomposition,,,,, +Supplement,,Helmholtz Free Energy,,,,, +Supplement,,Helmholtz machine,,,,, +Supplement,,Hermeneutics,,,,, +Supplement,,Hierarchically Mechanistic Mind,,,,, +Supplement,,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.,You take the High road and I’ll take the low road.,"The High road connects a number of different fields together, drawing from concepts of Autopoiesis and self-organization within random Non-linear dynamical systems who are proposed to be in Non-Equilibrium Steady State with respect to the environment. Under certain Ergodicity assumptions about such systems it is possible to show how the Free Energy Principle arises." +Supplement,,Homeostasis,,,,, +Supplement,,Homeostatic system,,,,, +Supplement,,Hyperprior,,,,, +Supplement,,Hypothesis,,,,, +Supplement,,Information bottleneck,,,,, +Supplement,,Instrumentalism,,,,, +Supplement,,Interface,,,,, +Supplement,,Interoception,,,,, +Supplement,,Interoceptive sensitivity,,,,, +Supplement,,Interpretation,,,,, +Supplement,,Inverse problem,,,,, +Supplement,,Kullback-Leibler Divergence,,,,, +Supplement,,Lagrangian,,,,, +Supplement,,Latent cause,,,,, +Supplement,,Lateral geniculate nucleus,,,,, +Supplement,,Least action,,,,, +Supplement,,Likelihood,,,,, +Supplement,,link,,,,, +Supplement,,"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). ","Be careful, the Low road can be dangerous.", +Supplement,,Marginal approximation,,,,, +Supplement,,Markovian Monism," Markovian Monism ",,,, +Supplement,,Marr's Levels of Description,,,,, +Supplement,,Material science,,,,, +Supplement,,maximum caliber,,,,, +Supplement,,Mean,,,,, +Supplement,,Mean field approximation,,,,, +Supplement,,Memory,,,,, +Supplement,,Message Passing,,,,, +Supplement,,Mismatch negativity,,,,, +Supplement,,Mode,,,,, +Supplement,,Model,,,,, +Supplement,,Model accuracy,,,,, +Supplement,,model evidence,,,,, +Supplement,,Model Inversion,"Model Inversion ",,,, +Supplement,,Morphogenesis,,,,, +Supplement,,Multisensory integration,,,,, +Supplement,,Network,,,,, +Supplement,,Neuronal Ensemble,,,,, +Supplement,,Niche construction,,,,, +Supplement,,Noisy signal,,,,, +Supplement,,Non-linear dynamical systems,,,,, +Supplement,,normative,,,,, +Supplement,,Optimal control,,,,, +Supplement,,overfitting,,,,, +Supplement,,Partially Observed Markov Decision Process,,,,, +Supplement,,Partition,,,,, +Supplement,,path,,,,, +Supplement,,Path integral,,,,, +Supplement,,phenotype,,,,, +Supplement,,Policy posterior,,,,, +Supplement,,Policy prior,,,,, +Supplement,,population,,,,, +Supplement,,Precision,,,,, +Supplement,,Prediction error,,,,, +Supplement,,Prediction error minimization,,,,, +Supplement,,Predictive Coding,,,,, +Supplement,,predictive machine,,,,, +Supplement,,Predictive Processing,,,,, +Supplement,,Principle,"Principle ",,,, +Supplement,,Probability distribution,,,,, +Supplement,,Proprioception,,,,, +Supplement,,Quantum,,,,, +Supplement,,Quantum mechanics,,,,, +Supplement,,Quantum-like,,,,, +Supplement,,Qubit,,,,, +Supplement,,Random variable,,,,, +Supplement,,Realism,"Realism ",,,, +Supplement,,Receptive field,,,,, +Supplement,,Recognition density,,,,, +Supplement,,Renormalization,,,,, +Supplement,,Representationalism,,,,, +Supplement,,Reservoir Computing,,,,, +Supplement,,Reward,,,,, +Supplement,,"Risk ","Risk ",,,, +Supplement,,Sample space,,,,, +Supplement,,Selection bias,,,,, +Supplement,,Selection history,,,,, +Supplement,,"Self-organization ",,,,, +Supplement,,Selfhood,,,,, +Supplement,,Semi-Markovian,,,,, +Supplement,,Sense of agency,,,,, +Supplement,,Sensorimotor,,,,, +Supplement,,Sensory attenuation,,,,, +Supplement,,Sensory Data,,,,, +Supplement,,Sensory input,,,,, +Supplement,,Sensory observation,,,,, +Supplement,,Sensory outcome,,,,, +Supplement,,Sensory State,,,,, +Supplement,,Sensory states,,,,, +Supplement,,Sentience,,,,, +Supplement,,"Shared Generative Model ",,,,, +Supplement,,Signal,,,,, +Supplement,,Simulation,,,,, +Supplement,,solenoidal,,,,, +Supplement,,Sophisticated Inference,,,,, +Supplement,,spike-timing dependent plasticity,,,,, +Supplement,,Statistical manifold,,,,, +Supplement,,Statistical Parametric Mapping,,,,, +Supplement,,Stigmergy,,,,, +Supplement,,Stochastic,,,,, +Supplement,,Subjective feeling states,,,,, +Supplement,,sufficient statistic,,,,, +Supplement,,Surprisal,,,,, +Supplement,,Swarm,,,,, +Supplement,,Symbol,,,,, +Supplement,,Synergetics,,,,, +Supplement,,T-Maze,,,,, +Supplement,,Teams,,,,, +Supplement,,Theory,,,,, +Supplement,,Thermodynamic system,,,,, +Supplement,,Thermostatistics,,,,, +Supplement,,Thinking Through Other Minds,,,,, +Supplement,,time,,,,, +Supplement,,Top-down attentional control,,,,, +Supplement,,Umwelt,,,,, +Supplement,,Unidirectionality,,,,, +Supplement,,Update,,,,, +Supplement,,Variance,,,,, +Supplement,,Variational message passing,,,,, +Supplement,,Variational Niche Construction,,,,, +Supplement,,Variational principle,,,,, +Supplement,,"Von Economo neurons ",,,,, +Supplement,,Weak mixing,,,,, +Supplement,,Working memory,,,,, +Supplement,,World States,,,,, +Supplement,,,,,,, +Supplement,Bayesian Statistics,Approximate Posterior,"A probability distribution (often denoted by ""q"") used in Variational Bayesian Inference to approximate the true (but unknown) Posterior distribution. + +",,"The parameters of the approximate posterior distribution are adjusted until the shape of the distribution matches the true Posterior distribution, as measured by Kullback-Leibler Divergence.",In Bayesian Inference we can solve Bayes’ theorem directly to obtain the Approximate Posterior.,"We cannot usually solve Bayes’ theorem because we must evaluate the model evidence term needed to normalize the product of the Likelihood and Prior. We can optimize the Kullback-Leibler Divergence between the Approximate Posterior and Posterior through a proxy functional, Variational Free Energy, which is the upper bound on Surprisal (negative log model evidence)." +Entailed,,active,,,,, +Entailed,,area,,,,, +Entailed,,backbone,,,,, +Entailed,,brain,,,,, +Entailed,,causality,,,,, +Entailed,,classical physics,,,,, +Entailed,,computer,,,,, +Entailed,,concentration,,,,, +Entailed,,concept,,,,, +Entailed,,condition,,,,, +Entailed,,consensus,,,,, +Entailed,,conversation,,,,, +Entailed,,current,,,,, +Entailed,,default-mode,,,,, +Entailed,,dynamics,,,,, +Entailed,,ecosystem,,,,, +Entailed,,ego,,,,, +Entailed,,energy,,,,, +Entailed,,environment,,,,, +Entailed,,error,,,,, +Entailed,,feedback,,,,, +Entailed,,field,,,,, +Entailed,,framework,,,,, +Entailed,,free,For no cost.,"Able to be liberated. ",,, +Entailed,,genetic,,,,, +Entailed,,hierarchical,,,,, +Entailed,,idea,,,,, +Entailed,,increase,,,,, +Entailed,,influence,,,,, +Entailed,,interpretation,,,,, +Entailed,,inverse,,,,, +Entailed,,language,,,,, +Entailed,,Logic,,,,, +Entailed,,machine,,,,, +Entailed,,matrix,,,,, +Entailed,,neuronal,,,,, +Entailed,,object,,,,, +Entailed,,objective,,,,, +Entailed,,observer,,,,, +Entailed,,Ontology-to-Model Link,,,,, +Entailed,,opportunity,,,,, +Entailed,,parameter,,,,, +Entailed,,part,,,,, +Entailed,,perceptual inference,,,,, +Entailed,,perspective,,,,, +Entailed,,phase,,,,, +Entailed,,physics,,,,, +Entailed,,play,,,,, +Entailed,,probability,,,,, +Entailed,,Probably Approximately Correct,,,,, +Entailed,,problem,,,,, +Entailed,,propositional,,,,, +Entailed,,Propositional attitude,,,,, +Entailed,,Psychological attitude,,,,, +Entailed,,purpose,,,,, +Entailed,,question,,,,, +Entailed,,random,,,,, +Entailed,,recognition,,,,, +Entailed,,relative entropy,"Synonym for Kullback-Leibler Divergence ","relative entropy ",,, +Entailed,,"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.",,,, +Entailed,,resource,,,,, +Entailed,,role,,,,, +Entailed,,science,,,,, +Entailed,,selection,,,,, +Entailed,,self-organization,,,,, +Entailed,,situation,,,,, +Entailed,,social,,,,, +Entailed,,states,,,,, +Entailed,,Stimulus,,,,, +Entailed,,technology,,,,, +Entailed,,Thing,,,,, +Entailed,,transition,,,,, +Entailed,,tree,,,,, +Entailed,,understanding,,,,, \ No newline at end of file diff --git a/v4 (12-12-2022 snapshot)/Ontology _ Terms & Translations View.csv b/v4 (12-12-2022 snapshot)/Ontology _ Terms & Translations View.csv new file mode 100644 index 0000000..94d949c --- /dev/null +++ b/v4 (12-12-2022 snapshot)/Ontology _ Terms & Translations View.csv @@ -0,0 +1,413 @@ +List,Term,German,Russian,Portuguese,Spanish,French,Italian,Hebrew,Czech,Persian,Let us know what to add! +Core,Accuracy,Genauigkeit,Точность,Acurácia,Precisión,l'éxactitude,Accuratezza,דיוק,přesnost,درستی, +Core,Action,,Действие,Ação,Acción,une action / une mesure,Azione,פעולה,akce,کُنش, +Core,Action Planning,,Планирование действий,Planejamento de Ação,Planeamiento de Acción,plannification des mesures/actions,Piano d'azione,תכנון פעולה,plánování akce,برنامه‌ریزی کُنش, +Core,Action Prediction,,Прогнозирование действий,Predição de Ação,Predicción de Acción,prédiction (f) d'action; prédiction (f) d'actions,Predire l'azione,חיזוי פעולה,předpověď akce,پیش‌بینی کُنش, +Core,Active Inference,,Активное обновление,Inferência Ativa,Inferencia Activa,inférence (f) active,Inferenza attiva,הסקה פעילה,aktivní inference,استنباط کُنش‌گر, +Core,Active States,,Активные состояния,Estados Ativos,Estados activos,etats actifs,stati attivi,מצבים פעילים,aktivní stavy,حالت‌های فعال, +Core,Affordance,,Возможность,Affordance,Affordance,affordance,convenienza,אפשרויות זמינות,prostředek,بضاعت (داراک), +Core,Agency,,Агентность,Agência,Agencia,une Agence,rappresentanza,סוכנות,agentura,کُنش‌گری, +Core,Agent,,Агент,Agente,Agente,un agent,Agente,סוכן,agent,کُنش‌گر, +Core,Ambiguity,,Неопределённость,Ambiguidade,Ambiguedad,l'ambiguïté,Ambiguità,אי ודאות,nejednoznačnost,ابهام, +Core,Attention,Aufmerksamkeit,Внимание,Atenção,Atención,attention/intéret,Attenzione,תשומת לב,pozornost,توجه, +Core,Bayesian Inference,bayessches Schlussfolgern,Байесовское обновление,Inferência Bayesiana,Inferencia Bayesiana,Inférence bayésienne,Inferenza Bayesiana,הסקה בייסיאנית,Bayesovská inference,استنباط بیزی, +Core,Behavior,,Поведение,Comportamento,Comportamiento,comportement,Comportamento,התנהגות,chování,رفتار, +Core,Belief,Glaube,Убеждение,Crença,Creencia,une conviction/croyance,Credenza,אמונה,přesvědčení,باور, +Core,Belief updating,,Корректирование предположений,Atualização de Crença,Actualización de creencia,mise à jour des convictions,aggiornamento della credenza,עדכון אמונות,aktualizace přesvědčení,به‌روزرسانی باور, +Core,Blanket State,,Состояния ограждения,Estados encobertos,Estados Encubiertos,états (n) couvertes; états (n) secrets ???,,מצבים כסויים,stav obalu,حالت‌های پوششی, +Core,Cognition,,Познание,Cognição,Cognición,La cognition,cognizione,קוגניציה,poznávání,شناخت, +Core,Complexity,Komplexität,Сложность,Complexidade,Complejidad,la complexité,Complessità,מורכבות,složitost,پیچیدگی, +Core,Cue,,Указатель,Pista,Señal,Repère,spunto,רמז,nápověda,اشارتگر (نشانه), +Core,Culture,Kultur,Культура,Cultura,Cultura,La culture,Cultura,תרבות,kultura,فرهنگ, +Core,Data,Daten,Данные,Data/Dados,Data (Datos),Data,Dato,נתנים,data,داده, +Core,Ensemble,,Ансамбль,"Conjunto ",Conjunto,ensemble (m),tutti insieme,אוסף,soubor,گروه, +Core,Epistemic value,der epistemische Wert,Эпистемическая ценность,o valor epistêmico,el valor epistémico," la valeur épistémique",valore epistemico,ערך הידע,epistemická hodnota,ارزش شناختی, +Core,Evidence,Beweis,Свидетельство,Evidência,Evidencia,Preuve,prova,ראייה,důkaz,گواه, +Core,Expectation,,Ожидание,Expectativa,Expectativas,attente (m),Aspettativa,,očekávání,چشم‌داشت, +Core,Expected Free Energy,,Ожидаемая свободная энергия,Energia Livre Esperada,Energía Libre esperada,énergie (f) libre attendue; énergie (f) libre espérée,,תוחלת האנרגיה החופשית,očekávaná volná energie,انرژی آزاد چشم‌داشته, +Core,External State,,Внешние состояния,Estados Externos,estados externos,états (m) externes; états (m) extérieurs; états (m) externes,stati esterni,מצבים חיצוניים,vnější stavy,حالت بیرونی, +Core,Free Energy,freie Energie,Свободная энергия,Energia Livre,"Energía libre ",énergie libre,Energia Libera,אנרגיה חופשית,volná energie,انرژی آزاد, +Core,Free Energy Principle,,Принцип свободной энергии,Princípio da Energia Livre,Principio de la Energía libre,Pricipe de l'énergie libre,"Principio della Energia Libera +",עקרון האנרגיה החופשית,princip volné energie,اصل انرژی آزاد, +Core,Friston Blanket,,Фристоновское ограждение,Cobertor de Friston,La Manta de Friston,couverture (f) Friston,,שמיכת פריסון,Fristonův obal,پوشش فریستون, +Core,Generalized Free Energy,,Обобщенная свободная энергия,Energia Livre Generalizada,Energía libre generalizada,énergie (f) libre généralisée,,אנרגיה כללית מוכללת,obecná volná energie,انرژی آزاد تعمیم‌یافته, +Core,Generative Model,das generatives Modell,Порождающая модель,o Modelo Generativo,el modelo generativo,le modèle génératif,il modello generativo,מצב גנרטיבי,generativní model,مدل زایا, +Core,Generative Process,,Порождающий процесс,Processo Generativo,Proceso generativo,processus (m) génératif,processo generativo,תהליך גנרטיבי,generativní proces,فرآیند زایا, +Core,Hidden State,,Скрытое состояние,Estados Ocultos,Estados ocultos,états (m) cachés; étés (m) cachés,stati nascosti,מצב חבוי,skryté stavy,حالت‌های پنهان, +Core,Hierarchical Model,,Многоуровневая модель,Modelo Hierárquico,Modelo jerárquico,modèle (m) hiérarchique,Modello gerarchico,מודל היררכי,hierarchický model,مدل پایگانی, +Core,Inference,Schlussfolgern,Вывод,Inferência,Inferencia,inférence (f); déduction (f),Inferenza,הסקה,inference,استنباط, +Core,Information,Information,Информация,Informação,Información,Information,informazione,מידע,informace,اطلاعات, +Core,Information Geometry,,Информационная геометрия,Geometria de Informação,Geometría (f) de la información,géométrie (f) de l'information,geometria dell'informazione,גיאומטריית המידע,informační geometrie,هندسهٔ اطلاعات, +Core,Internal State,,Внутренние состояния,Estados Internos,Estados internos,état intérieur (m); état (m) interne,stati interni,מצבים פנימיים,vnitřní stav,حالت درونی, +Core,Learning,Lernen,Активное обучение,Aprendizagem Ativa,Aprendizaje Activo,apprentissage (m) actif,Apprendimento attivo,למידה פעילה,aktivní učení,یادگیری, +Core,Living system,lebendes System,Живая система/биологическая система,Sistema Vivo,Sistema vivo,Système Vivant,Sistema vivente,מרכת חייה,živý systém,سامانهٔ زنده, +Core,Markov Blanket,die Markov-Decke,Марковское ограждение,o Cobertor de Markov; manta de Markov; o envoltório de Markov,la manta markoviana,la couverture de Markov,la coperta di Markov,שמיכת מרקוב,Markovský obal,پوشش مارکوف, +Core,Markov Decision Process,,Марковский процесс принятия решений,Processo de decisão Markoviano,Proceso de decisión markoviana,processus (m) décisionnel de Markov,Processo decisional di tipo Marcoviano,תהליכי החלטה מרקוביאנים,Markovské rozhodovací procesy,فرآیند تصمیم‌گیری مارکوف, +Core,Multi-scale system,,Многомасштабная система,Sistema multiescala,Sistema multiescalar,système multi-échelle,sistema di tipo multi-scala,מודל מרובה קני מידה,vícestupňový systém,سامانهٔ چندمقیاسی, +Core,Narrative,,Нарратив,Narrativa,Narrativa,récit (m); narration (f),Narrazione,נרטיב,vyprávění,روایت (روایی), +Core,Niche,,Ниша,Nicho,Nicho,niche (f); créneau (m),Nicchia,כוך/נישה,výklenek,آشیان, +Core,Non-Equilibrium Steady State,der Nicht-Gleichgewichtszustand,Неравновесное устойчивое состояние,Estado de Equilíbrio não Estacionário,el estado de equilibrio no estacionario; el estado estacionario no equilibrado,(m) l'état d'équilibre non équilibré,il stato di equilibrio non-stazionario,מצב יציב שלא בשווי משקל,nerovnovážný ustálený stav,حالت پایای نامتعادل, +Core,"Novelty ",,Новизна,Novidade,Novedad,nouveauté (f),Novità,חידוש,novota,تازگی (نوظهوری), +Core,Observation,Beobachtung,Наблюдение,Observação,Observación,une observation,Osservazione,תצפית,postřeh,مشاهده, +Core,Outcome,Ergebnis,,,,,,,,برآمد, +Core,Particle,Teilchen,Частица,Partícula,Partícula,une particule,Particella,חלקיק,částice,ذره, +Core,Perception,Wahrnehmung,Восприятие/Воспринимание,Percepção,Percepción,la perception,Percezione,תפיסה,vjem,ادراک, +Core,Policy,,Правила,Prática Normativa,regla (f); práctica (f) normativa; estrategia (f); política (f),pratique normative (f); règles; politique (f); stratégie (f),Strategia,מדיניות,zásada,خط مشی, +Core,Policy selection,,Выбор правила,Seleção de prática normativa,selección (f) de la práctica normativa; selección (f) de políticas; selección (f) de política; selección (f) de reglas,sélection (f) de la pratique normative; sélection (f) de règle; sélection (f) de la stratégie; sélection (f) politique,Selezione della strategia,בחירת מדיניות,výběr zásady,انتخاب خط مشی, +Core,Posterior,hinten gelegen,Апостериорный,Posterior,Posterior,Postérieur(e/es/s),Precedente,פוסטריור,zadní/předcházející,پس‌آیند, +Core,Pragmatic Value,der pragmatische Wert,Прагматическая ценность,Valor Pragmático,el valor pragmático,valeur (f) pragmatique,valore pragmatico,ערך פרגמטי,pragmatická hodnota,ارزش کاربردی, +Core,Prediction,,Предсказание,Predição,Predicción,Une prédiction,Predizione,עקרון,předpověď,پیش‌بینی, +Core,Preference,,Предпочтение,Preferência,Preferencias,préférence (f); préféré (m),Preferenza,,přednost,ترجیح (برتری دادن، به‌شماری), +Core,Prior,,Приор,Anterior,Antecendete,,Precedente,,předchozí,پیش‌آیند, +Core,Process Theory,,Теория процессов,Teoria de Processos,Teoría de procesos,théorie (f) des processus,Teoria del processo,תאוריית תהליכים,teorie procesu,نظریهٔ فرآیند, +Core,Recognition Model,,Модели распознавания,Modelos de Reconhecimento,Modelo de reconocimiento,modèle (m) de reconnaissance,modelli di ricognizione,מודלי זיהוי,model rozpoznávání,مدل بازشناسی, +Core,Regime of Attention,,Режим внимания,Regime de Atenção,Régimen de Atención,régime (m) de soins; régime (m) d'attention; régime (m) attentionnel,regime di attenzione,משטר תשומת לב,režim pozornosti,نظام توجه, +Core,Representation,,Репрезентация,Representação,Representación,une représentation,Rappresentazione,ייצוג,reprezentace,بازنمود, +Core,Salience,,Значимость,Saliência,Saliencia,saillance (f); saillant (m),Saliente,בולטות,salience,برجستگی (اهمیت), +Core,Sense State,,Сенсорные состояния,Estados Sensoriais,Estados Sensoriales,états (m) sensoriels; états (m) de sens,saliente,מצבי חישה,smyslové stavy,حالت‌های حسی, +Core,State,,Состояние,Estado,Estado,un état,Stato,מצב,stav,حالت, +Core,State space,der Zustandsraum,Пространство состояний,o espaço de estados,el espacio de estado,(m) l’espace d'état; l'espace des phases,il condizone dello spazio; il spazio di stato,מרחב מצבים,stavový prostor,فضای حالت, +Core,Stationarity,,Стационарность,estacionário,Fijo or estatico,stationnarité (f); fixe (m); statique (m),Fisso o statico,,stacionarita,پایداری, +Core,Surprise,Überraschung,Сюрприз,Surpresa,Sorpresa,La surprise,Sorpresa,הפתעה,překvapení,شگفتی, +Core,System,das System,Система,o sistema,el sistema,le système,Sistema,מערכת,systém,سامانه, +Core,Temporal Depth,,Темпоральная глубина,Profundidade Temporal,Profundidad temporal,profondeur (f) temporelle ??,Spessore temporale,עומק טמפורלי,časová hloubka,عمق زمانی, +Core,Uncertainty,,Неопределенность,Incerteza,Incertidumbre,l'incertitude,Incertezza,אי ודאות,nejistota,عدم قطعیت, +Core,Variational,,Вариационный,Variacional,Variacional,variationnel (m); variationnelle (f),Variazionale,משתנה,variační,فراگردان, +Core,Variational Free Energy,,Вариационная свободная энергия,Energia livre variacional,Energía libre variacional,énergie (f) libre variationnelle,"Energia libera variazionale +",אנרגיה חופשית משתנה,variační volná energie,انرژی آزاد فراگردان, +Supplement,Abstract Action,,,Ação,,,,,,, +Supplement,Abstract action prediction,,,Predição de Ação,,,,,,, +Supplement,Abstract Bayesian Inference,,,Inferência Bayesiana,,,,,,, +Supplement,Abstract epistemic value,,,Valor epistêmico,,,,,,, +Supplement,Abstract External State,,,Estado externo,,,,,,, +Supplement,Abstract Generative Model,,,Modelo Generativo,,,,,,, +Supplement,Abstract Hidden State,,,Estado oculto,,,,,,, +Supplement,Abstract Internal State,,,Estado interno,,,,,,, +Supplement,Abstract Sensory State,,,Estado sensorial,,,,,,, +Supplement,Abstract System,,,Sistema,,,,,,, +Supplement,AbstractAccuracy,,,Acurácia,,,,,,, +Supplement,abstractCounterpart,,,Contraparte,,,,,,, +Supplement,Action and Planning as Divergence Minimization,,,Ação e planejamento como minimização de divergência,,,,,,, +Supplement,Action at a distance,,,Ação à distância,,,,,,, +Supplement,Action Integral,,,Ação integral,,,,,,, +Supplement,Active Blockference,,,,,,,,,, +Supplement,Active learning,,,,,,,,,, +Supplement,active processes,,,,,,,,,, +Supplement,active processes,,,,,,,,,, +Supplement,Active Vision,,Активное зрение,Visão Ativa,Visión activa,,visione attiva,ראיה פעילה,aktivní vidění,, +Supplement,Agency based model,,,Modelo baseado na agência,,,,,,, +Supplement,Agency free model,,,Modelo livre de agência,,,,,,, +Supplement,Algorithm,,,Algoritmo,,,,,,, +Supplement,Alignment,,,Alinhamento,,,,,,, +Supplement,analogy,,,Analogia,,,,,,, +Supplement,Analytical Philosophy,,,,,,,,,, +Supplement,Appraisal theories of emotion,,,,,,,,,, +Supplement,Approximate Posterior,,,,,,,,,پس‌آیند تقریبی, +Supplement,Attenuation of response,,,,,,,,,, +Supplement,Attracting set,,,,,,,,,, +Supplement,Augmented reality,,,Realidade aumentada,,,,,,, +Supplement,Autopoiesis,,Аутопоэзис,Autopoiese,Autopoiesis,Autopoïèse,autopoiesi,אוטופואזה,,, +Supplement,Bayes-optimal control,,,,,,,,,, +Supplement,Bayesian,,,Bayesiano (a),,,,,,, +Supplement,Bayesian belief updating,,,,,,,,,, +Supplement,Bayesian Brain,,,Cérebro Bayesiano,,,,,,, +Supplement,Bayesian mechanics,,,,,,,,,, +Supplement,Bayesian Model Selection,,,,,,,,,, +Supplement,Bayesian surprise,,,Surpresa Bayesiana,,,,,,, +Supplement,Bethe approximation,,,,,,,,,, +Supplement,Blanket index,,,,,,,,,, +Supplement,Bottom-up attentional control,,,Controle atencional ascendente,,,,,,, +Supplement,categorical,,,,,,,,,, +Supplement,Category Theory,,,,,,,,,, +Supplement,changing mind (cognition),,,,,,,,,, +Supplement,changing the mind,,,,,,,,,, +Supplement,changing the world,,,,,,,,,, +Supplement,changing world (action),,,,,,,,,, +Supplement,chaos,,,Caos,,,,,,, +Supplement,Circular causality,,,Causalidade circular,,,,,,, +Supplement,Coarse graining,,,,,,,,,, +Supplement,Cognitive Science,,,Ciências Cognitivas,,,,,,, +Supplement,Cognitive System,,,Sistemas Cognitivos,,,,,,, +Supplement,Cognitivism,,,Cognitivismo,,,,,,, +Supplement,Collective behavior,,,Comportamento Coletivo,,,,,,, +Supplement,Conceptual metaphor,,,Metáfora Conceitual,,,,,,, +Supplement,Conditional density,,,Densidade Condicional,,,,,,, +Supplement,Conditional Probability,,,Probabilidade Condicional,,,,,,, +Supplement,Confidence,,,Intervalo de Confiança,,,,,,, +Supplement,Congruence,,,Congruência,,,,,,, +Supplement,Connectionism,,,Conexionismo,,,,,,, +Supplement,constraint,,,,,,,,,, +Supplement,Continental Philosophy,,,,,,,,,, +Supplement,Continuous state space,,,,,,,,,, +Supplement,Control (states),,,Controle (estados),,,,,,, +Supplement,Control theory,,,Teoria do Controle,,,,,,, +Supplement,Counterfactual,,,Contrafatual; Contrafactual,,,,,,, +Supplement,Cybernetics,,,Cibernética,,,,,,, +Supplement,Decision-making,,Принятие решений,Tomada de Decisão,Toma de decisiones,La prise de décisions,prendere decisioni,ביצוע החלטות,,, +Supplement,Density,,,Densidade,,,,,,, +Supplement,Deontic Action,,,Ação Deôntica,,,,,,, +Supplement,DeSci,,,,,,,,,, +Supplement,Development,,,Desenvolvimento,,,,,,, +Supplement,Discrete state space,,,,,,,,,, +Supplement,Dissisipation,,,Dissipação,,,,,,, +Supplement,Distribution,,,Distribuição,,,,,,, +Supplement,divergence,,,Divergência,,,,,,, +Supplement,Domain,,,Domínio,,,,,,, +Supplement,Domain-generality,,,,,,,,,, +Supplement,Domain-specificity,,,,,,,,,, +Supplement,Dynamic Causal Modelling,,,Modelagem causal dinâmica,,,,,,, +Supplement,Dynamic expectation maximization,,,Maximização de expectativa dinâmica,,,,,,, +Supplement,Dynamicism,,,Dinamicismo,,,,,,, +Supplement,EcoEvoDevo,,,"Ecologia, Evolução, Desenvolvimento",,,,,,, +Supplement,Ecology,,,Ecologia,,,,,,, +Supplement,Embedded Embodied Encultured Enactive Inference,,,,,,,,,, +Supplement,Embodied Belief,,,CrençaCorporificada,,,,,,, +Supplement,Embodied Cybernetic Complexity,,,Complexidade Cibernética Corporificada,,,,,,, +Supplement,Emotion,,,Emoção,,,,,,, +Supplement,Empirical prior,,,Anterior empírico,,,,,,, +Supplement,Enactivism,,,Enativismo,,,,,,, +Supplement,Entropy,,,Entropia,,,,,,, +Supplement,Epistemic foraging,,,,,,,,,, +Supplement,Ergodicity,,Эргодичность,Ergodicidade,Ergodicidad,,ergodicità,ארגודיות,,, +Supplement,Estimator,,,Estimador,,,,,,, +Supplement,Event-related potential,,,,,,,,,, +Supplement,Evolution,,,Evolução,,,,,,, +Supplement,Expectation maximization,,,Maximização de expectativa,,,,,,, +Supplement,Expected Utility Theory,,,Teoria da Utilidade Esperada,,,,,,, +Supplement,Experience of body ownership,,,,,,,,,, +Supplement,Explaining Away,,,Dissolver,,,,,,, +Supplement,Explanation,,,Explanação,,,,,,, +Supplement,Extended Cognition,,,Cognição Estendida,,,,,,, +Supplement,Exteroception,,,,,,,,,, +Supplement,Factor graph,,,,,,,,,, +Supplement,Falsification,,,Falsificação,,,,,,, +Supplement,Far-from-equilibrium,,,,,,,,,, +Supplement,Filter,,,,,,,,,, +Supplement,fitness,,,,,,,,,, +Supplement,flow,,,,,,,,,, +Supplement,Fokker-Planck Equation,,,Equação Fokker-Planck,,,,,,, +Supplement,"Foraging ",,,,,,,,,, +Supplement,Forney,,,,,,,,,, +Supplement,Friction,,,Fricção,,,,,,, +Supplement,Friston's Law,,,Lei de Friston,,,,,,, +Supplement,"functional magnetic resonance imaging ",,,Imagem por Ressonância Magnética Funcional,,,,,,, +Supplement,Gauge theory,,,,,,,,,, +Supplement,Gaussian distribution,,,Distribuição Gaussiana,,,,,,, +Supplement,Generalized coordinates,,,Coordenadas generalizadas,,,,,,, +Supplement,Generalized Synchrony,,,Sincronia generalizada,,,,,,, +Supplement,Generative density,,,Densidade Generativa,,,,,,, +Supplement,Generative modelling,,,Modelagem Generativa,,,,,,, +Supplement,Gestalt,,,Gestalt,,,,,,, +Supplement,Goal-driven selection,,,,,,,,,, +Supplement,Gradient Descent,,,,,,,,,, +Supplement,Graphical,,,Gráfico,,,,,,, +Supplement,Group Renormalization Theory,,,"Teoria de Grupo de Renormalização ",,,,,,, +Supplement,Guidance signal,,,,,,,,,, +Supplement,Habit learning/formation,,,Aprendizado/Formação de Hábito,,,,,,, +Supplement,Hamilton's Principle of Least Action,,,"Princípio Hamiltoniano da Menor Ação; Princípio de Hamilton de Mínima Ação ",,,,,,, +Supplement,Helmholtz Decomposition,,,,,,,,,, +Supplement,Helmholtz Free Energy,,,,,,,,,, +Supplement,Helmholtz machine,,,Máquina de Helmholtz,,,,,,, +Supplement,Hermeneutics,,,,,,,,,, +Supplement,Hierarchically Mechanistic Mind,,,Mente Hierarquicamente Mecanística,,,,,,, +Supplement,High road,,,,,,,,,, +Supplement,Homeostasis,,,Homeostase,,,,,,, +Supplement,Homeostatic system,,,Sistema homeostático,,,,,,, +Supplement,Hyperprior,,,Hiperanterior,,,,,,, +Supplement,Hypothesis,,,Hipótese,,,,,,, +Supplement,Information bottleneck,,,Gargalo de informação,,,,,,, +Supplement,Instrumentalism,,,Instrumentalismo,,,,,,, +Supplement,Interface,,,Interface,,,,,,, +Supplement,Interoception,,,Interocepção,,,,,,, +Supplement,Interoceptive sensitivity,,,Sensibilidade interoceptiva,,,,,,, +Supplement,Interpretation,,,Interpretação,,,,,,, +Supplement,Inverse problem,,,Problema inverso,,,,,,, +Supplement,Kullback-Leibler Divergence,,,Divergência de Kullback-Leibler,,,,,,, +Supplement,Lagrangian,,,,,,,,,, +Supplement,Latent cause,,Неявная причина,Causa Latente,Causa latente,,causa latente,סיבות חבויות,,, +Supplement,Lateral geniculate nucleus,,,Núcleo geniculado lateral,,,,,,, +Supplement,Least action,,,,,,,,,, +Supplement,Likelihood,,,Verossimilhança,,,,,,, +Supplement,link,,,,,,,,,, +Supplement,"Low road ",,,,,,,,,, +Supplement,Marginal approximation,,,Aproximação marginal,,,,,,, +Supplement,Markovian Monism,,Марковский монизм,Monismo Markoviano,,,,מוניזם מרקוביאני,,, +Supplement,Marr's Levels of Description,,,Níveis de Descrição de Marr,,,,,,, +Supplement,Material science,,,,,,,,,, +Supplement,maximum caliber,,,,,,,,,, +Supplement,Mean,,,Média,,,,,,, +Supplement,Mean field approximation,,,,,,,,,, +Supplement,Memory,,,Memória,,,,,,, +Supplement,Message Passing,,,Troca de Mensagem,,,,,,, +Supplement,Mismatch negativity,,,,,,,,,, +Supplement,Mode,,,Modo,,,,,,, +Supplement,Model,,,Modelo,,,,,,, +Supplement,Model accuracy,,,Acurácia do modelo,,,,,,, +Supplement,model evidence,,,,,,,,,, +Supplement,Model Inversion,,Инверсия модели,Inversão de Modelo,Inersión de modelo,modèle d'inversion,,היפוך מודל,,, +Supplement,Morphogenesis,,,Morfogenesis,,,,,,, +Supplement,Multisensory integration,,,Integração Multisensorial,,,,,,, +Supplement,Network,,,Network,,,,,,, +Supplement,Neuronal Ensemble,,,,,,,,,, +Supplement,Niche construction,,,Construção de Nicho,,,,,,, +Supplement,Noisy signal,,,Sinal de ruído,,,,,,, +Supplement,Non-linear dynamical systems,,,Sistemas dinâmicos não-lineares,,,,,,, +Supplement,normative,,,,,,,,,, +Supplement,Optimal control,,,Controle óptimo,,,,,,, +Supplement,overfitting,,,,,,,,,, +Supplement,Partially Observed Markov Decision Process,,,,,,,,,, +Supplement,Partition,,,Partição,,,,,,, +Supplement,path,,,,,,,,,, +Supplement,Path integral,,,,,,,,,, +Supplement,phenotype,,,,,,,,,, +Supplement,Policy posterior,,,,,,,,,, +Supplement,Policy prior,,,,,,,,,, +Supplement,population,,,,,,,,,, +Supplement,Precision,,,Precisão,,,,,,, +Supplement,Prediction error,,,Erro de predição,,,,,,, +Supplement,Prediction error minimization,,,Minimização de erro de predição,,,,,,, +Supplement,Predictive Coding,,,Codificação Preditiva,,,,,,, +Supplement,predictive machine,,,,,,,,,, +Supplement,Predictive Processing,,,Processamento Preditivo,,,,,,, +Supplement,Principle,,Принцип,Princípio,Principio,un principe,Principio,עקרון,,, +Supplement,Probability distribution,,,Distribuição de Probabilidade,,,,,,, +Supplement,Proprioception,,,,,,,,,, +Supplement,Quantum,,,,,,,,,, +Supplement,Quantum mechanics,,,,,,,,,, +Supplement,Quantum-like,,,,,,,,,, +Supplement,Qubit,,,,,,,,,, +Supplement,Random variable,,,Variável aleatória,,,,,,, +Supplement,Realism,,,Realismo,,,,,,, +Supplement,Receptive field,,,Campo receptivo,,,,,,, +Supplement,Recognition density,,,,,,,,,, +Supplement,Renormalization,,,,,,,,,, +Supplement,Representationalism,,,Representacionalismo,,,,,,, +Supplement,Reservoir Computing,,,,,,,,,, +Supplement,Reward,,,Recompensa,,,,,,, +Supplement,"Risk ",,Риск,Risco,Riesgo,le risque,Rischio,סיכון,,, +Supplement,Sample space,,,Espaço de amostra,,,,,,, +Supplement,Selection bias,,,,,,,,,, +Supplement,Selection history,,,,,,,,,, +Supplement,"Self-organization ",,,Auto-organização,,,,,,, +Supplement,Selfhood,,,,,,,,,, +Supplement,Semi-Markovian,,,Semi-Markoviano,,,,,,, +Supplement,Sense of agency,,,Sensação de agência,,,,,,, +Supplement,Sensorimotor,,,Sensoriomotor,,,,,,, +Supplement,Sensory attenuation,,,Atenuação sensorial,,,,,,, +Supplement,Sensory Data,,,Dado sensorial,,,,,,, +Supplement,Sensory input,,,Entrada sensorial,,,,,,, +Supplement,Sensory observation,,,,,,,,,, +Supplement,Sensory outcome,,,Saída sensorial,,,,,,, +Supplement,Sensory State,,,,,,,,,, +Supplement,Sensory states,,,,,,,,,, +Supplement,Sentience,,,,,,,,,, +Supplement,"Shared Generative Model ",,,Modelo generativo compartilhado,,,,,,, +Supplement,Signal,,,Sinal,,,,,,, +Supplement,Simulation,,,Simulação,,,,,,, +Supplement,solenoidal,,,Solenoidal,,,,,,, +Supplement,Sophisticated Inference,,,Inferência Sofisticada,,,,,,, +Supplement,spike-timing dependent plasticity,,,,,,,,,, +Supplement,Statistical manifold,,,,,,,,,, +Supplement,Statistical Parametric Mapping,,,,,,,,,, +Supplement,Stigmergy,,,,,,,,,, +Supplement,Stochastic,,,Escolástico,,,,,,, +Supplement,Subjective feeling states,,,Estados de sentimentos subjetivos,,,,,,, +Supplement,sufficient statistic,,,,,,,,,, +Supplement,Surprisal,,,Surprisal/ Alarme,,,,,,, +Supplement,Swarm,,,Enxame; Multidão,,,,,,, +Supplement,Symbol,,,Símbolo,,,,,,, +Supplement,Synergetics,,,Sinergético,,,,,,, +Supplement,T-Maze,,,,,,,,,, +Supplement,Teams,,,Times,,,,,,, +Supplement,Theory,,,Teoria,,,,,,, +Supplement,Thermodynamic system,,,Sistema termodinâmico,,,,,,, +Supplement,Thermostatistics,,,Termoestatística,,,,,,, +Supplement,Thinking Through Other Minds,,,Pensando através de outras mentes,,,,,,, +Supplement,time,,,,,,,,,, +Supplement,Top-down attentional control,,,Controle atencional descendente,,,,,,, +Supplement,Umwelt,,,Umwelt,,,,,,, +Supplement,Unidirectionality,,,Unidirecionalidade,,,,,,, +Supplement,Update,,,Atualização,,,,,,, +Supplement,Variance,,,Variância,,,,,,, +Supplement,Variational message passing,,,,,,,,,, +Supplement,Variational Niche Construction,,,Construção de Nicho Variacional,,,,,,, +Supplement,Variational principle,,,,,,,,,, +Supplement,"Von Economo neurons ",,,,,,,,,, +Supplement,Weak mixing,,,,,,,,,, +Supplement,Working memory,,,Memória de trabalho,,,,,,, +Supplement,World States,,,Estados do mundo,,,,,,, +Supplement,,,,,,,,,,, +Entailed,active,,,ativo,,,,,,, +Entailed,area,,,área,,,,,,, +Entailed,backbone,,,,,,,,,, +Entailed,brain,,,cérebro,,,,,,, +Entailed,causality,,,causalidade,,,,,,, +Entailed,classical physics,,,,,,,,,, +Entailed,computer,,,computador,,,,,,, +Entailed,concentration,,,concentração,,,,,,, +Entailed,concept,,,conceito,,,,,,, +Entailed,condition,,,,,,,,,, +Entailed,consensus,,,consenso,,,,,,, +Entailed,conversation,,,conversa,,,,,,, +Entailed,current,,,atual,,,,,,, +Entailed,default-mode,,,modo padrão,,,,,,, +Entailed,dynamics,,,dinâmica,,,,,,, +Entailed,ecosystem,,,,,,,,,, +Entailed,ego,,,ego,,,,,,, +Entailed,energy,,,energia,,,,,,, +Entailed,environment,,,ambiente,,,,,,, +Entailed,error,,,erro,,,,,,, +Entailed,feedback,,,feedback,,,,,,, +Entailed,field,,,campo,,,,,,, +Entailed,framework,,,quadro teórico / framework,,,,,,, +Entailed,free,,,livre,,,,,,, +Entailed,genetic,,,genética,,,,,,, +Entailed,hierarchical,,,hierárquico,,,,,,, +Entailed,idea,,,ideia,,,,,,, +Entailed,increase,,,aumenta,,,,,,, +Entailed,influence,,,influência,,,,,,, +Entailed,interpretation,,,interpretação,,,,,,, +Entailed,inverse,,,inverso,,,,,,, +Entailed,language,,,linguagem,,,,,,, +Entailed,Logic,,,lógica,,,,,,, +Entailed,machine,,,máquina,,,,,,, +Entailed,matrix,,,matriz,,,,,,, +Entailed,neuronal,,,neuronal,,,,,,, +Entailed,object,,,objeto,,,,,,, +Entailed,objective,,,objetivo,,,,,,, +Entailed,observer,,,observador,,,,,,, +Entailed,Ontology-to-Model Link,,,,,,,,,, +Entailed,opportunity,,,oportunidade,,,,,,, +Entailed,parameter,,,parâmetro,,,,,,, +Entailed,part,,,parte,,,,,,, +Entailed,perceptual inference,,,inferência perceptual,,,,,,, +Entailed,perspective,,,perspectiva,,,,,,, +Entailed,phase,,,fase,,,,,,, +Entailed,physics,,,física,,,,,,, +Entailed,play,,,jogar/jogo,,,,,,, +Entailed,probability,,,probabilidade,,,,,,, +Entailed,Probably Approximately Correct,,,,,,,,,, +Entailed,problem,,,problema,,,,,,, +Entailed,propositional,,,"proposicional ",,,,,,, +Entailed,Propositional attitude,,,atitude proposicional,,,,,,, +Entailed,Psychological attitude,,,atitude psicológica,,,,,,, +Entailed,purpose,,,propósito,,,,,,, +Entailed,question,,,pergunta/questão,,,,,,, +Entailed,random,,,aleatório,,,,,,, +Entailed,recognition,,,reconhecimento,,,,,,, +Entailed,relative entropy,,,entropia relativa,,,,,,, +Entailed,"represents ",,,representa,,,,,,, +Entailed,resource,,,recurso,,,,,,, +Entailed,role,,,papel,,,,,,, +Entailed,science,,,ciência,,,,,,, +Entailed,selection,,,seleção,,,,,,, +Entailed,self-organization,,,auto-organização,,,,,,, +Entailed,situation,,,,,,,,,, +Entailed,social,,,social,,,,,,, +Entailed,states,,,estados,,,,,,, +Entailed,Stimulus,,,estímulo,,,,,,, +Entailed,technology,,,tecnologia,,,,,,, +Entailed,Thing,,,coisa,,,,,,, +Entailed,transition,,,transição,,,,,,, +Entailed,tree,,,árvore,,,,,,, +Entailed,understanding,,,entendimento,,,,,,, \ No newline at end of file