Word-Sensibility

The Full Model Overview

I know nothing in the world that has as much power as a word. Sometimes I write one, and I look at it, until it begins to shine.

– Emily Dickinson

Visit the About page for the project’s motivation, scope and goals. We are in the exploratory stage and look forward to any feedback. Please Contact.

General Area of Discussion: Model Based and Commonsense Reasoning

Interests, influences & Inspirations: Intersubjectivity, Anthroposemiotics, Enactivism, Ecological Psychology, Embodied Cognition, Phenomenology.

An Ecological Systems Perspective: Artificial Intelligence, Database Schema, Contextual Responsiveness, Commonsense Awareness, Intentions & Viewpoints.

General Artificial Intelligence & Reference Frames

To exist as an individual means not simply to be numerically distinct from other things but to be a self-pole in a dynamic relationship with alterity, with what is other, with the world.

— Evan Thompson

Introducing The Quadranym Model of Word-Sensibility:

From: In a Nutshell

Word-Sensibility is proposed as a method of textual analysis. Data training involves a database with its features tagged to identify reference frames for word sense. Word-sensibility is about the dynamic sense of word sense.

Dynamic sense refers to having a sense of things through interaction.
Interactions accumulate and unitize into systems of responsiveness.
Responsiveness is the ability to act quickly and positively to situations.
Responsiveness is about normal and successful engagement with the world.

GOAL: Improve commonsense prediction with units of responsiveness.

General Introduction: Visit About Page

About the Full Overview

On this page we offer an in-depth overview of the model. We introduce the terms Word-Sensibility, Dynamical Context, Word-Topic and Quadranym.

Word-Sensibility is about points of view, commonsense, metaphoric relations and simulating the human ability to sense dynamic relations between signs.
The word-sensibility approach aims to model words as units of homeostasis.

Sections of the Article

The Specificity of a Word’s Dynamicity
The Organism: Responding & Predicting
The Machine: An Ecological Systems Perspective
Orientation: Affordance, Invitation & Metaphor
Reference Frames: Viewpoints & Disambiguation
Responsiveness: The Motivated Dynamical Context
Intersubjectivity: Orientation & Conative Exchanges
The Word-Topic Database: Wiki & Acquisition
Word-Sensibility in a Nutshell
Final Thoughts & Summary
The General Database: Open Source Language Project

These are compiled notes that address the basic approach and philosophy.

1. The Specificity of a Word’s Dynamicity

We categorize as we do because we have the brains and bodies we have and because we interact in the world as we do.

― George Lakoff

Systems & Relations:

An agent’s performance in the world is routine given the right conditions. Word-sensibility reference frames aim to provide models for these conditions.

Word-Sensibility encompasses personal, social & environmental conditions.

Features of the Model:

Word-Topic (a word sense realm) organizes, characterizes and summarizes lexical information. A word-topic is a pre-textual rendering of context used to anchor the responses to the words and contextual developments of a text.

Every Word-Topic has at least one quadranym providing one word sense.

Quadranym: Four dimensions used to frame a word-topic word sense.

Example: Time(x)

x = q: {future, past, present, event}
modes: {future, past}
states: {present, event}

A Quadranym is a construct from the Q-database. They are rendered by template and are used in reference frames for word-topic processing.

(Note: Quadranyms are basic skeletal frameworks for word-topics. Word-topics may also include, clusters of related terms, knowledge graphs or word vectors.)

Reference frame: Word-topic variables and zeropoint (virtual viewpoint).

Example:

zeropoint:{present}
coordinates: {events}
y axis: {future}
x axis: {past}

Nowness remains the invariable event to any sense of change, procedure or modification.

Reference frames are a framework used to graph responsive dynamics.

Word-topics are clusters of related words or word senses used to assemble quadranyms. Quadranyms form word-topic realms. Realms nest together.

2. The Organism: Responding & Predicting

Once we have the full dynamical story, we can predict the behavior of the robot in its environment completely, and we can do so without making reference to the representational content of any states of its control system.

— Anthony Chemero, p 77 Radical Embodied Cognition, MIT Press 2009

Humans are highly creative with context. That is, contextual tricks to imagine possibilities. It’s a key factor to how we deal with uncertainty. Consider the ‘survey question’ below for what is blank? Like in the game of Family Feud, the most common answer to the question wins the round.

“I will know blank as soon as I walk through the door.”

Give a person a situation like this and countless scenarios are imagined. It’s virtually impossible to know what the speaker is referring to. Still, the job of commonsense prediction is about the knowledge of what typically happens; “what the weather is like,”“what’s for dinner,” “If the package arrived.” We refer to this knowledge as belonging to the Situational Context.

Likely scenarios are imagined through basic understandings of the world.

The situational context is the ability to understand circumstances such that a prediction can be made about an event i.e., one responds with knowledge.

In our approach, a prediction is one thing, a response is another.

Response in our terms doesn’t refer to understanding. Response refers to accumulated interactions that are instinctively used in different manners of responsiveness: the ability to act quickly and positively to situations. In our approach, responsiveness is innate and part of an organismic level of context.

From response to prediction. A change of plans?

We propose the idea of the Dynamical Context.

Dynamical Context: A basic Description.

Unpacking words with word-sensibility analysis begins with a distinction in the contextualizing of word sense.

A Situational Context is the communicative ability to present or understand the objective circumstances in which an event occurs and will sometimes include the appropriate behaviors associated with it.

We introduce the idea of a Dynamical Context which is something different and can be summarized as follows:

Dynamical Context: a situation resonates with a preexisting psychology, a predetermined expectation for behavior within that situation, and produces a synergy response, reshaped for the moment.
Dynamical Contextual Systems: characterized by the potential for multiple dynamic areas and interactions between them.

The dynamical context can be thought of as an organism’s dynamic sense experiences characterized by the potential interactions between them. This suggests relation to sensory motor capacities. The model is about the relation between the situational context and an organism’s dynamic sense.

It represents responsive adaptations to the environment used to process communication. The process concerns orientation to engage an interaction.

Here are a few more ideas:

Dynamical context value is realized through the situational context.
Its job is to integrate its spatial and temporal dynamics into situations.
By integrating with situations analogs and predictions can take form.

The dynamical context is represented in multi-quadranym systems.

A quadranym represents a unit of responsiveness.

Before we illustrate the model, let’s first consider the relevant topic in our example. It’s basically about an agent’s mental state. Imagine that mental state as being a unit of homeostasis between what’s known and unknown.

“I will know as soon as I walk through the door.”

Door is the relevant spatial context. The barrier of door is between the knower (self) and what is knowable (event). The objective of what is knowable is only satisfied when the knower goes through the door. Only then is what is knowable satisfied and the unit’s mental stasis is achieved.

A script like this can be repurposed in different ways and serve as an analog.

This is basically what we aim to model; motivated homeostatic responses. The research includes anthroposemiotics; sharing intentions and viewpoints.

Motivated homeostatic responses provide topical orientations.

A script like this used as an analog is what we call a Topical Trace. In other words, unattended topics can be used as a reference for the attended topic.

The current example will serve to help illustrate the model’s basic features.

Example: I will know as soon as I walk through the door.

The example allows us to address many basic features quickly and easily.

3. The Machine: An Ecological System Perspective

“The organism’s environment is the sense it makes of the world. This environment is a place of significance and valence, as a result of the global action of the organism.”

— Evan Thomson

The Quadranym Model of Word-Sensibility:

Our aim is to generalize the dynamic sense of environmental interaction. The responsiveness of quadranyms to conditions are virtual adaptations to the environment. Consider different responses for the responsiveness of x.

x = {<I, will, know, as_soon, as_I, walk, through, the_door>}

Textual elements are parsed into responsive units (see chart below).

Multiple responsive layers are on the vertical (hierarchy).
Features of each layer are on the horizontal (quadranyms).

(Note: Units connect together to form scripts and nest together to form layers.)

Quadranyms nest in a hierarchical order with sorted portions of the text i.e., virtual responsive layers are motivated by environmental interaction.

The chart illustrates global relations and a matrix for weighted values.

Quadranym Examples: (Virtual Responsive Units)

Word-Topic (global)	Expand (y axis)	Reduce (x axis)	Object (coordinates)	Subject (zeropoint)
1-Space {through}	infinite	finite	between	void
2-Time {as_I, will, as_soon}	future	past	event	present
3-Locomotion {walk}	move	stay	place	position
4-Agent {I,}	positive	negative	goal	self
5-Mental {know}	unknown	known	knowable	knower
6-Door {the_door}	open	close	barrier	passage

Additional units can nest in the structure above such as ground or distance.

Examples of Additional Quadranyms for Additional (spatial) Layers:

Topic=Ground	E=up	R=down	O=surface	S=gravity
Topic=Distance	E=far	R=near	O=relation	S=position
Topic=Through	E=out	R=in	O=exit	S=enter

Above are word-topics from the database as additional respondents.

Sorting Textual Elements to Features: (Details are in the data preparation.)

Textual elements sort to quadranyms tagged for their word-topic feature such as, spatial, temporal, agent, mental, locomotion (i.e., realms or domains).

The general description of the process is as follows: The textual elements proceed to correspond with the features of the word-topics that proceed to correspond to one another. Furthermore, hierarchical structure form on these relationships such that the relevant circumstances of the situational context are embedded in the spatial and temporal paradigms of the system.

The text’s situational context sorts into the system’s dynamical context.

Pre-Aligned: Columns are pre-tuned in the database (i.e., orientation).

The database provides nested systems used to form realms or domains.

Spatial States: void is the constant state and between is the variable state.

The spatial source-condition is all space, its target-condition is some space.
In this hierarchy, space is the acting domain-system over all occurrent layers.
Door is a realm-system that normally layers within the space domain system.

(Note: Realms nest in domains. Any word can be a domain or a realm.)

Weighted Target Value e.g., [Spatial target: door ⊆ Source: Space: Between]

E = expand: open		O = object: barrier
	N = Topic Name: door
S = subject: passage		R = reduce: close

Above, Quadranym Entry Field for Door (A Database Word-Topic}

A responsive body requires an environment as its primary affordance.

Word-Topic Prime = Space: (quadranym superset FOR string)
Hierarchical Terms: (multiple quadranym subsets FOR string)

Responsiveness responds to being in a space. A space is the condition where responses enact over a time period. A layer is a spatial-temporal respondent.

Layers nest virtual spatial-temporal units of reference (i.e., quadranyms).

Quadranym Template: (Prime Dimensions)

Mode: expand (expansive sense FOR string)*
Mode: reduce (reductive sense FOR string)*
State: object (objective sense FOR string)*
State: subject (subjective sense FOR string)*

*(Word-Sensibility’s Four Prime Adjectives for Describing Sense)

(Note: There is no real subjective and objective distinction in the hierarchical structure. Mental attributes and environmental attributes are nested layers in a single global system. The distinction is in quadranyms and is temporal and local.)

Modes: Homeostatic measure for responses.
States: Source and target conditions.

Together, modes and states form spatial-temporal units (i.e., responsive unit).

Units represent attention to prior experiences of interaction with the world and do not represent direct current experience. Units are virtual experiences.

Each quadranym is a response from a particular self perspective. It adds up to a general responsiveness. Quadranyms are like neural reference frames.

At its core, word-sensibility is that which prescribes internal and external distinctions to responsive units. That is, it aims to model an agent’s internal responses to external occurrences at different nested levels in the system.

When sorting is complete, we say that the dynamical context is coupled to the situational context. Next, textual elements find orientation in each unit.

4. Orientation: Affordance, Invitation & Metaphor

“But, actually, an affordance is neither an objective property nor a subjective property; or it is both if you like. An affordance cuts across the dichotomy of subjective-objective and helps us to understand its inadequacy. It is equally a fact of the environment and a fact of behavior. It is both physical and psychical, yet neither. An affordance points both ways, to the environment and to the observer.”

― James J. Gibson

As stated, quadranyms dimensions are divided into modes and states. Here, we will first discuss states. State is the generic term for a unit’s conditions.

States are addressed in this section. Modes are addressed in the next.

States: Source and target conditions.

The Orientation of Source and Target Conditions:

Orientation has a specific role in each quadranym. It identifies the source condition that is necessary before any target condition can be engaged.

Source and target conditions are internal features of quadranym units.
Textual elements map to the condition features situated in each unit.

Fine tuning a text involves mapping it to source and target conditions. The term affordance is borrowed to refer to a quadranym’s responsive conditions.

In the schema, the source is like an affordance and its utility is the target.

(Note: Affordances show agents the actions they can take. Agents perceive affordances without having to consider how to use them. e.g., a teacup holds fluids or may be utilized as a cookie cutter. (The source affords target variables.))

The source affordance is something different from perceptual affordances. It doesn’t attend the world. It attends the experience of attending the world.

Source affordances represent reliving and sharing orientations with others.

In the schema, a source condition is a factor intrinsically occurrent in the context. A target condition is a factor changing or modifying in the context.

(Intrinsically Occurrent: a temporal relation to the self from which adaptations occur. A virtual present moment to orient any changes or modifications in a unit.)

Example: {<I, will, know, as_soon, as_I, walk, through, the door>}
Source: Space {through}, Time {as soon, as_I}, Agent {I}.
Target: Space {door}, Time {will), Mental {know}, Locomotion {walk}.

Sorted String Sets: Target Condition ⊆ Source Condition

(Note: The source condition can be understood in different ways, as subjective sense, anchor or an unattended state. The target is always the attended state.)

A Global System Model of the Occurrent Context Layers:

Orientation has a specific role in global layers, it sets the hierarchical order.

Contextual Layers: General to relevant spatial-temporal invitation system.

Example: I will know as soon as I walk through the door.

Contextual Ecosystem (Onion)

Layers of Target Potentials (environmental adaptors):

Space: the_door
Time/Locomotion: will
Time/Locomotion: walk
Agent/Mental: know

Layers of Source Actuals (responsive anchors):

Space: through (anchors the_door)
Time/Locomotion: as_soon (anchors will)
Time/Locomotion: as_I (anchors walk)
Agent/Mental: I (anchors know)

Sorted States: [Space S, Time T, Locomotion L, Agent A, Mental M]

S: Source state: void {through} ⊇ target state: between {the_door}.
T/L: Source state: present/position {as_soon} ⊇ target state: event/place {will}.
T/L: Source state: present/position {as_I} ⊇ target state: event/place {walk}.
A/M: Source state: self/knower {I} ⊇ target state: goal/knowable {know}.

(Note: The source affordances above refer to anchors more than perceptions in the world. However, the anchors help select, organize and interpret the targets.)

Spatial-Temporal Affordance & Invitation Layers:

Keep in mind that through of the first layer is intrinsic to the agents nature. It doesn’t describe what the agent is doing, rather, it describes what the agent is invited to do i.e., Invitation systems form the hierarchies that nest affordances.

The same basic sequential dynamic in each unit (cycle) occur in the layers.
Last Unit: Knower is the source condition. Knowable is the target condition.
Layers: Void begins the general realm. Knowable ends the relevant realm.

(Note: The distinction between agent and environment is local and temporal.)

Invitation Layers & Contextual Artifacts:

(Note: A contextual artifact is dynamically actual and situationally potential.)

Spatial orientation space_void {through}, is the most general layer in this Invitation System. It is the primary invitation to the environmental space. It is the invitation that anchors for the targets on each more relevant layer.

Each layer is a sequence (cycle), FROM source condition TO target condition.

Each quadranym has a source i.e., an idiosyncratic method of orientation. For instance, layer 2 orientation sources {as_soon} as the actual position of the present state i.e., present position orients to event {will} as the temporal target.

Layer 2: subjective source {as_soon} orients the objective variable {will}.

(Note: as_soon is during the time of being before event. Will is that event. In the normal understanding of the term here, will expresses the future inevitability of the event. In this system the expression itself is the event i.e., as_soon → will.)

Each layer is an invitation for the next layer to proceed.

Layer 3: subjective source{as_I} orients the objective variable {walk}.

(Note: as_I is during the time of being for event. Walk is that event.)

Layers 1,2,3 provide invitation for layer 4. These layers can serve as a system for other relevancies such as, door → leave instead of door → know.

Original 4: Source state: self/position {I} ⊇ target state: goal/place {know}.
Alternative 4: Source state: self/position {I} ⊇ target state: goal/place {leave}.

In the example, the invitation layers amounts to …

Spatial orientation invites door to invite locomotion to invite knowing.

System Layers & Metaphorical Mapping:

(Note: Metaphor requires a dedicated article. Overview below)

On a metaphorical level, a door can be a transition to nearly anything.

The doors of our mind.
The doors to other worlds.
The doors of opportunity.
The doors of knowledge.

Consider our current sentnence example:

I will know as soon as I walk through the door.

As anyone might imagine, will_know can be replaced by seemingly endless other relevancies such as, will_have_opportunity, will_change, will_start, will_go etc…

Metaphorical Target Realm Examples:

Addressing obstacles.
New opportunities.
Ending or starting.
Transitioning.
Learning.

In our approach, a metaphor is the relationship between the dynamical context and the situational context. Two different systems of context.

Cognitive metaphor is the difference between source and target domains.

A domain is a specified sphere of knowledge. A condition is the state of something. Where conditions change, domains basically stay the same.

Consider the sentence example below:

The news of the merger hit them all like a brick.

Conceptual metaphors are normally understood as a connection between two domains. The dynamics of source and target conditions also apply here.

In the model, source condition is dynamical. Target condition is situational.

Consider the script below. Situational brick becomes dynamical brick.

[Situation(brick)]<find>[Dynamic(brick) ⊇ Situation(news)]<find>[Dynamic(news)⊇ Situation(hit)]<find>[Dynamic(hit)]<cycle>.

The source is for the dynamical context and the target is for the situational context. The iteration cycle above is a script for a dynamical context layer.

Scripts distribute condition factors between source and target domains.

Above is a reference frame. Modes are the axes. More detail in the next section.

The reference frame modes adjust to source or target. News is a potential condition of brick. Other layers such as feel and impact add more grounding.

The source affordance is brick and its target variable is news.

Contextual Layers: General to relevant spatial-temporal invitation system.

A virtual space where all the members are located (in the void of this space).

Layered State Orientations: (Blank Example)

Space: Source state: void{…} ⊇ target state: between{…}.
News/Feel: Source state: info/aware{…} ⊇ target state: receive/contact{…}.
News/Feel: Source state: info/aware{…} ⊇ target state: receive/contact{…}.
Agent/Impact: Source state: self/force{…} ⊇ target state: goal/contact{…}.

Sorted State Elements: (Content Example)

Source state: void {all} ⊇ target state: between {the_news}.
Source state: info/aware {of_the} ⊇ target state: receive/contact {merger}.
Source state: info/aware {Like_a} ⊇ target state: receive/contact {brick}.
Source state: self/force {them} ⊇ target state: goal/contact {hit}.

The situational context is about the merger and how they all felt about it. Humans will know brick is a simile expressing the emotional impact. Brick is not about the situation. It is about the dynamical context in response to the situation i.e, a distinction between a dynamic sense and the actual situation.

How does an agent know the difference?

The situational context represents objective sense (participate with others).
The dynamical context represents subjective sense (a self distinction).

(Note: Some inspiration for this distinction is with the efferent copy (neurology). Situational contexts represent actual situations. Dynamical contexts represent responses that can occur in those situations. Stability requires their distinction.)

The dynamical context presents interactive spaces. That is, contextual responses. The situational context allows a response within its constraints.

Situational contexts allow temporal-spatial units to be figurative or literal.

A reciprocal relationship between a dynamical context and a situational context renders apt virtual orientation for the responsiveness to a meaning.

Situational Context: Representational: What hit them? {< news, hit, them>}.
Source state: feel {them_all} ⊇ target state: react {the_news}<find> topic

The word (or sense) brick is being repurposed from the situational context position. From the dynamical context position it is simply a viewpoint layer.

Dynamical Context: Procedural: How did it hit them? {like_a_brick}.
Source state: motion {hit} ⊇ target state: matter {brick}<find> topic

There is no need to go into all the properties of a brick.

Below are orientation topics (dynamic entailments of the sentence):

hit: Source state: force {from} ⊇ target state: contact {to}
Brick: Source state: block {heavy} ⊇ target state: build {solid}
Energy: Source state: motion {hit} ⊇ target state: matter {brick}
Emotion: Source state: feel {them_all} ⊇ target state: react {the_news).

Brick invites energy (size/weight modes of measure).

(Note: Source-target conditions pull inferences from textual elements and default states. Impact felt: hit/brick filtered: physically, psychologically or emotionally.)

The dynamical context copies the situational context within its own system paradigms. The result are various kinds of viewpoints. Some viewpoints apply and some don’t as they are inferred, filtered, measured and balanced.

The situational context works to preserve the objective perception.
The dynamical context works to apply responses to the perception.

(Note: With word-sensibility analysis there are no initial concrete ontological foundations except for basic responses and motivations. Instead, dynamical contexts and situational contexts develop on the same continuum where they become more distinct over time and continuously form and reform each other.)

Dynamic sense is about systems of source and target layers and mapping those systems and layers to each other to aptly target objective conditions.

The situational context analysis is about the truth conditions.

The situational context seeks an objective ontological foundation.

The dynamical context analysis is about the dynamic sense.

The dynamical context is a subjective ontology driven by orientation.

Dynamical contexts are by default virtual rationalizing systems when acting without objective conditions of the situational context. When coupled to objective conditions, its function is to simultaneously preserve reliable objective perspectives and its dynamic sense. It is generally healthy when the coupling is highly predictive to the potential conditions of its environment.

Dynamical context references prior successful couplings to justify a response.

Dynamical context units virtually seek to interface with real conditions to assimilate a vast and pervading objective field (i.e., the environment) into its system. The reward and benefit is that once attached to viable conditions, the system naturally functions to increase the tractability of its objective field.

Dynamical Context: source conditions anchor for objective condition targets.

Although beyond the scope of this article, there is filtering such as inhibiting and debugging processes that control relational dynamics between systems.

The meaning is in the text. The dynamical context responds to the meaning.

In this approach, metaphor is an example of dynamic sense responding to the situational context and we suggest that disambiguation processes utilize a very similar structure. We’ll touch on disambiguation in the next section.

Dynamical context units are about finding ways to adapt to environments.

Orientational layers can be added, subtracted or rearranged. Next, spatial orientation adjusts its spatial region coordinates to the situational context.

Quadranym reference frames operate like sensing units of homeostasis.

(Note: The idea of orientation as illustrated above is one example or way to think about layers of virtual orientation. Sentential methods of orientational analysis may at times require grammatical assistance. Grammatical analysis primarily pertains to the situational context. The goal is to model the relations between the situational context and the dynamical context systems. Also, orientation may summarize chunks of text e.g., an entire sentence may be a source or a target. The distinction spans between the word-topics and the theme topics of a text. Still, orientation as we are presenting it is chiefly about the ability to abstract the human experience of contextual expectation by building up from the word level.)

5. Reference Frames: Viewpoints & Disambiguation

The heart of the problem is not so much how we see objects in depth, as how we see the constant layout of the world around us. Space, as such, empty space, is not visible, but surfaces are.

― James J. Gibson

As stated, quadranyms dimensions are divided into states and modes. Here, we will now discuss modes. Mode is the generic term for a unit’s measures.

Modes provide measure to balance relations between state conditions.

Modes: Homeostatic measure for virtual responses.

Calibrate Word-Topics to Situational Context:

Every word-topic takes aim at its condition variables based on what’s given. For example, void is what’s given for the topic space and takes aim at the variables belonging to between such as, objects, regions, solid separation, spatial separation. Emptiness is the source affordance for spatial context.* Door is the relevant object of space (i.e., target variable). A reference frame performance is about calibrating coordinates to integrate textual elements.

*(Note: Probable layer: T=ground: [ E=up | R=down | O=surface | S=gravity])

Reference Frames: (Various data methods of analysis may apply.)

Dynamical Context Responds to Situational Context:

The situation provides the objectivity and the reason for the input-output configurations. Each reference frame illustrates a response to the situation.

Calibrate: Space Topic: whole region responds to separate regions FOR between.

Viewpoint: Less whole region and more separate region illustrates the spatial context.

I will know as soon as I walk through the door.

The output y increases when the door is open. A new region is a new input. This informs the y axis to adjust its value i.e., whole region raises its potential.

Inputs are specified, such as, region X₁ and region X₂.
Inputs are actual and the outputs are their potential.

(Note: Peri-personal space if defined can also specify some value. Also, inputs can represent objects, separation and type of separation when the utility is specified.)

Utility: Whole region depends on separate region to be between.

Any potential output depends on some actual input to position between.

(Note: Whole region is infinite potential. Separate region is finite and actual.)

Notes for Space Topic Graph:

If X₁ represents one region then y potential is only for that region.
If open door adds new region then y potential spans both regions.
This means that the y potential now includes actual regions, X₁, X₂.
Affordance potential connects. It is less separate because of access.
The new region X₂is a new input and new viewpoint for a new cycle.
Cycles are quadranym frames that join together to make scripts.

Script Example (basic):

Space topic blanks: (2 perspectives)

input X₁: [Infinite(void{…}) ⊇ Finite(between{…})]<find>
input X₂: [Infinite(void{…}) ⊇ Finite(between{…})]

Term clusters sort between the two spaces. Each space is a perspective.

(Note: Input X₂ is an extension of input X₁. X₂ is a new viewpoint of X₁ . X₁ is a spatial condition with low potential for knowable event. X₂ will raise potential.)

In the previous section we illustrated a system of hierarchical layers. Each layer represents a timeline. Some longer or shorter depending on the event.

Contextual Timeline Layers: ( A brief description.)

Quadranyms form units. Units are frames that when linked together form scripts. Each layer of script forms a hierarchy. Scripts run simultaneously on different contextual timelines. The upper scripts constrain lower scripts.

For example, you get up for work, take shower, brush teeth, have breakfast, commute, arrive, do tasks etc… work constrains lower contextual timelines.

Reference Frames Shape Contextual Viewpoints:

The coordinates of the target (to the origin) is relevant to the given context. We can use mental coupling as an analogy and say that the source is to be coupled to the target in a particular way. The space topic reference frame above illustrates three frames converging on one point (extended on z axis).

The source and target relations of space, door and agent align.
All of these reference frames share their homeostatic measures.
General Targets: Spatial orientation targets door to target goal.

What Follows are Multiple Quadranym Subsets:

A similar description of calibration applies to the other reference frames.

The following frames will either align to region X₁ or region X₂ .
A frame such as topic time spans both regions (FROM X₁ TO X₂ ).
X₁ is the actual region and X₂is the potential region in the context.

Calibrate: Time Topic: potential event responds to actual event FOR the_event.

Viewpoint: More potential and more actual illustrates the temporal event context.

Potential time depends on actual time to be temporal event.

Notice that knowable has a high potential in the time topic.

(Note: Time topic includes both regions in its procedural scheme, X₁ → X₂ )

Notes for Time Topic Graph:

The input begins the occurrent procedure of the context.
The output is the potential of the actual input for the objective.

(Note: Nowness remains the invariable event to all of the changes.)

Calibrate: Mental Topic: observation responds to information FOR knowable.

Observation depends on information to be knowable event.

Mental is constrained by the X₁spatial and temporal dynamics.

Mental is the reference frame that tells the relevant story in the context. Less information is the input. The response to the input is less observation.

Increased observation to information is represented in time topic X_2.

(Note: Knowable is a real potential in time topic. Above, mental topic only aligns to the spatial region of X₁. Real potential is in X₂. Time topic spans both regions.)

Notes for Mental Topic Graph:

Information can be of the agent or of the environment.
If space adds new region then mental receives new input.

(Note: New region is more information that raises observation. Observation is what one perceives and that is the potential of knowable in this Reference frame. Information is the actual input that observation depends on. Observation is a response to the mental goal. Observation and information measure knowability.)

Distance – Additional Layer Example:

Calibrate: Distance Topic: remote responds to proximity FOR relation.

Viewpoint: More remote and more proximity illustrates the spatial distance context.

Remote depends on proximity to be spatial relation separation.

(Note: If statement, “Rome is far from here.” then proximity is less.)

Notes for Distance Topic Graph:

The output is more potential and means relation is remote.
Remoteness has to do with the agent’s position to the relation.
Changes are required in the X₁ region for relation to be less remote.

(Note: Distance topic aligns with time topic. Knowable is a potential of distance )

The Script’s System Summary:

The task is to weigh variables, provide temporal sequence and recognize goal. Scripts develop in the process. Scripts define procedural motivations.

Conative Script: between dynamics between knower and knowable.

FROM: Void affords through (state) that motivates changes to door (state).
TO: Knower affords know (state) that motivates changes to place (state)

(Note: Notice how in 2, the mental topic’s subject layer-5 targets locomotion’s object layer-4. And also, notice how in 1, layer 1 targets all of layer-6 (see top chart). During data training weighted values develop between the nested layers.)

A word-topic is about a space. A word-topic adds its homeostasis to a space of a context i.e., motivational dynamics between source and target conditions.

Disambiguation: Reference Frames & Viewpoints

(Note: Disambiguation requires a dedicated article. Overview below.)

Example:

“Put the umbrella in the tub because it’s wet.”

What is the word wet referring to, umbrella or tub?

A human would know that the umbrella is wet and goes in the tub.

The truth may be that high level commonsense awareness like this requires human like experiences. Although machines are mostly incapable of that, they may be able to someday better address our intentions and viewpoints.

Above are basic renderings of quadranyms (reference frames) for umbrella and tub. To further assist with any disambiguation problem requires nested systems (as was illustrated in the previous section). Not an easy problem because of the high amount of potential systems found in the analysis. Another issue is finding the right hierarchical order for the layering of these systems. However, in this particular example there are clues. Umbrella has to do with climate and to vary climate requires certain space states, targets and modes of measure.

Spatial Climate Variances (examples): Shelter, Cover, Barrier, Distance, etc…

(Note: An agent has maintenance goals with umbrella (e.g., protection).)

Modes of Measure: out is dependent on in, far is dependent on near, open is dependent on close and so on. In the quadranym database (schema), climate is a domain/realm that works interdependently with other domains/realms.

(Note: The dynamical context system is essentially a domain general framework.)

For an agent to vary its climate condition it targets various spaces.

(Note: Umbrella has different quadranym senses in the database with different target potentials. In this case, target includes the (element) rain (sometimes sun).)

Conative Script: exposure dynamics between cover and rain.

Umbrella Skeletal System:

General layer: T=Shelter: [E=out | R=in | O=exposure | S=protect]
Relevant layer: T=Umbrella: [ E=above | R=under | O=rain | S=cover]
protect affords cover (state) that motivates changes to rain (state)

(Note: Umbrella has maintenance goals (e.g., shade, dry) + object or agent.)

T= Maintenance: [ E=preserve | R=neglect | O=object | S=keep]

Bathtub Skeletal System:

Conative Script: full dynamics between contain and water.

General layer: T=Container: [E=out | R=in | O=full | S=empty]
Relevant layer: T= Bathtub: [ E=out | R=in | O=water | S=contain]
Empty affords contain (state) that motivates changes to water (state):

(Note: Bathtub has maintenance goals (e.g., clean, rinse) + object or agent.)

T= Maintenance: [ E=preserve | R=neglect | O=object | S=keep]

Homeostatic Measures for Source & Target Conditions:

Umbrella/Shelter: Cover/Safe: dry is actual = under | wet is potential = top.
Tub/Container: Contain/Empty: wet is actual = in | dry is potential = out.
General Modes of Measure: actual: risk {wet} | potential: use {wet} | .

The problem is not only about the amount of layers necessary but the invitations between them. Eventually, the system will respond to water as, adaptions for control (uses & risks). This is basically embedded in the system.

Applied to each layer: Coping: [Use(controlling) ⊇ Risk(adapting)]

System layers (Onion):

Each layer calibrates to the context in relation to other system layers.

The conative scripts do not have all the layers to respond to the situation (effective invitation relations between layers). However, they are orientations that anchor easily to most apt system layers (onions). It remains an active condition. This means that the adaptation for ‘It’ is not yet found. When the adaptation is found it becomes a passive condition. Find Unit: wet=source: [actual motivation is for controlling wet | potential adaptation is for target.]

Term frequencies of previous situational clusters (e.g., rain, sun, umbrella).

The source-actual and target-potential are identified for both targets.

Target A:

“Put the umbrella in the tub because it’s wet.”
System Goal: source = control {wet} ⊇ target = adaptation {tub}

(Note: Umbrella is wet? How Likely? How does tub adapt?)

Target B:

“Put the umbrella in the tub because it’s wet.”
System Goal: source = control {wet} ⊇ target = adaptation {umbrella}

(Note: Tub is wet? How Likely? How does umbrella adapt?)

A likely scenario is derived between the two targets above.

It is not a choice between A and B but how their factors combine.

In both target scenarios above, umbrella goes in the tub. It is likely that umbrella is contained in tub or likely that the umbrella is washed in tub.

It is unlikely that umbrella covers tub or that it protects tub.

The question is, what is the most typical relevant adaptation?

In this approach, the answer lies with the motivation for adaptation.
There could be several controls, such as, wash, contain, protect, cover.
The goal is to find the most typical relevant-adaptation-system layers.
Both items could be wet. Which is more typical? Create invitation layers.
Invitation-system-layers nest typical relevant-adaptation-system layers.

Ideal Disambiguation Scenario: (The aim is for typical layers to align.)

All space is the general invitation layer nesting more relevant layers.
General Sense Unit: T= Space: [ E=out | R=in | O=between | S=void]
Relevant Sense Unit: T= Cope: [ E=use | R=risk | O=adapt | S=control]

Bathtubs are usually indoors and in homes. Umbrella: rain is more typical then sun. Moving from a rain space to indoor space provides shelter from rain. Umbrella is wet but not needed. To control wet indoors umbrella goes in the tub. The system orientations span from keeping–dry to keeping–house.

A word-sensibility system is where motivations play out for a given context.

From General to Relevant Layers:

T=Space: …………[ E=infinite | R=finite | O=between | S=void]
T=Climate: ….. [E=temporate | R=severe | O=area | S=condition]
T=Shelter:………………..[ E=out | R=in | O=exposure | S=protect]
Umbrella: ……………….[ E=out | R=in | O=rain | S=cover]
Tub:……………………..[ E=out | R=in | O=water | S=contain]
T=Agent:…………….[ E=active | R=passive | O=goal | S=self]
T= Maintenance: [ E=preserve | R=neglect | O=object | S=keep]

A possible contextual onion (ecosystem). Each layer provides being-becoming cycles.

The process of becoming aligned: Generally, the active-source is about the effort, motivation or intention and it represents the affordances occurrent in the apt-being. The passive-target is about the system alignment completed.

The ideal scenario may not always be met in this or in other disambiguation attempts. However, the problems in a knowledgebase may be more easily found based on topical orientation i,e., users can track orientational issues.

Word-sensibility viewpoints are about efforts to adapt. Next, we illustrate the process and representation for active-passive conditions (units & cycles).

Coping: [Active{controlling) ⊇ Passive(adapted)]

(Note: Theoretically, the general process of word-sensibility requires a reciprocal relationship between ontological and compositional senses. Compositional sense proves the logic. That is, quadranyms are justified by situations (training data). Target variables found in prior texts are anchored by various source conditions. Word-sensibility disambiguation is only half the process. This pertains to the distinction between the dynamical context and the situational context systems.)

6. Responsiveness: The Motivated Dynamical Context

“To understand is to experience harmony between what we aim at and what is given, between the intention and the performance – and the body is our anchorage in the world. ”

― Maurice Merleau-Ponty

Truth conditions of text are about situational contexts. Dynamical contexts couple to the text. That is, textual elements receive a virtual dynamic sense.

Quadranyms represent responsive units for communicating dynamics.
A quadranym unit is not itself a meaning. It is a response to a meaning.

For a value to be realized in a quadranym requires a dynamical context and a situational context to couple i.e., truth conditions engage making unit relevant.

Dynamical context is a system to integrate with the situational context.
Integration provides a responsiveness for present and future predictions.

Dynamical Context Tools & Representation:

Responsive Unit Blank: [Potential{…}(actual{…}) ⊇ Actual{…}(potential{…})]

The quadranym is a template representing synergistic components.

Unit of Responsiveness (Template Square)

The square illustrates the dynamic unitization process of coupling to a situation.

Top: potential level
Bottom: actual level
Left: active hemisphere
Right: passive hemisphere
State Axis: bottom left to top right is a becoming
Mode Axis: top left to bottom right is a measuring
Word-Topic: The head word of the grammatical layer (not shown)

A unit of responsiveness is like a spontaneous activity in the beginning. If it somehow aligns with its environmental system it becomes a responsiveness.

The template illustrates the left hemisphere as active organismic power.
The right hemisphere illustrates the passive alignment to the environment.
The passive state is oriented by its active state and is calibrated by modes.
Responsiveness is rendered once an interaction with the world is integrated.

(Note: Units assimilate various modes of measure for homeostatic responses.)

Active and Passive Power:

Units are virtual power cycles occurring on multiple layers and timelines.

The active condition receives power from its environment.
The passive condition requires power from the active condition.
Passive power is equal to the organism’s environmental alignment.

Motivation is Modular in Quadranyms:

The source condition is the active-actual condition. The target condition is the passive-potential condition. The passive-potential aligns to the world FOR the active-actual. This represents the motivated dynamical context.

(Note: FOR represents closing the loop on target variables.)

In the schema, Condition is the state of things. Change is the mode of things.

Active energy represents a unit using power e.g., organismic condition.
Passive energy represents a power to be used e.g., environmental condition.
Active sense represents more experience necessary i.e., find relation.
Passive sense represents no more experience necessary i.e., relation found.

(Note: A quadranym is about attention to the world through one’s experience.)

To be clear, the environment is not itself passive energy. On the contrary, the environment powers the agent. Passive energy refers to aspects of the environment that have become part of an agent’s normative responses. For instance, tool use such as grasping a mug is an example of a passive condition.

Active condition transfers energy to the passive condition.
That is, it starts from the environment back to the environment.
This transfer of energy may then become a normative response cycle.
Passive condition is the environmental invitation for an organism to align.
It may take multiple quadranym cycles to reach a passive condition objective.

(Note: Passive power refers to an organism’s skill or ability to align itself to the environment based on its active power orientation. For instance, consider human locomotion, it is passive power on the ground where a gecko has the passive power to walk on the ground but also include walls and ceilings. Active power is about causal relations to the environment (i.e., setae hair covers gecko toe pads).)

Temporal Layers & Constraining Systems:

Consider for instance a chimpanzee who smashes small stones with a big rock. Maybe the act continues just for the dynamic sense of it, the impact, stones fractured into fragments. Now consider the act repurposed to crack nuts. A new motivation — a new system of responsiveness is organized.

Active Power: chimpanzee (i.e., the skill to use)
Passive Power: rock (i.e., a certain utility to use)

Active power (power to use) and passive power (power being used) refers to an organisms responsive ability. That is, the resources it has in its responsiveness.

Representing environmental energy is a flux.
Representing organismic energy is a unit.

Flux is double bracketed: [b] → [a]
Unit is single bracketed: [a → b]

A flux point is found between units.

flux:[Structure(world)]→[Function(self)]<find>

A unit is a response to a flux point i.e., stimulus → response.

unit:[Function(self) → Structure(world)]<flux>

Hierarchical Structure: Upper scripts constrain lower scripts.

General Layer:[Function(survive) → Structure(world)]
General Layer:[Function(nutrition) → Structure(goal)]
Relevant Layer:[Function(hunger)] → Structure(food)]
Relevant Layer: [Function(food) → Structure(nuts)]
Relevant Layer: [Function(nuts) → Structure(rock)]
Relevant Layer: [Function(rock) → Structure(smash)]

The primary advantage of layers is the ability to create various hierarchical orders. In this way, an act can be constrained by different motivations.

Scripts are about source conditions allowing for new target variables.

Flux Point Illustration: ¹(i.e., Causal Flux = Environmental Power)

7. Intersubjectivity: Orientation & Conative Exchanges

We human beings constitute and reconstitute ourselves through cultural traditions, which we experience as our own development in a historical time that spans the generations. To investigate the life-world as horizon and ground of all experience therefore requires investigating none other than generativity – the processes of becoming, of making and remaking, that occur over the generations and within which any individual genesis is always already situated. … Individual subjectivity is intersubjectively and culturally embodied, embedded, and emergent.

― Evan Thomson

A script changes the orientation. Each frame is an orientation constrained by its layers. Basic theoretical perspectives of orientation are given below.

Above represents the Prototype Theory of categorization.

A source that anchors can change its orientation.

Orientational Illustrations:

Consider the image above, orientation represents the response cycles of an individual. The actual node represents remembered interactions and the potential node represent the passive potential conditions. Potential conditions are passive senses that easily couple to actual senses. Actual sense does not refer to the actual world, but to that which is driven by the actual world.

Intersubjectivity is represented as sharing or aligning orientation.

Actual node represents the anchor of orientation.

Below illustrates intersubjective orientation.

Potential node represents shared anticipation.

The image above illustrates two individuals sharing common experiences to share focus. The transformation is from two discrete interacting units to one interacting unit. The behavioral benefit is about increasing capacity to relate better potential conditions to the occurrent area of anticipation.

Situatedness: Intersubjective units are nested in layers. We call shared orientation a coherent bias. Conditional sense provides the target variables.

Units span from intrasubjective to intersubjective cycles

(Note: Coherent bias represents orientation for an individual of a community.)

The sum of nested layers provide context to emend condition potentials.

Nested layers provide constraints to produce likely menu options.

Cycles are layers in each individual and also between them as social cycles.

Unit cycles on a social scale can resemble agent–patient relations.

Quadranym representation example for above:

What are having to eat?
Eat(x) ⟹ [Sate(hungry) ⊇ Starve(food)x]

Superset (i.e., active): Hungry is the subject predicated on Sate.
“What are we having to eat?” (Experience anchors the anticipation.)

Hungry represents the active-actual condition. Hungry alerts the body that it is time to eat. It begins the process of eating. This is why hungry is predicated on the active-potential sate. Where the active-actual represents the condition, the active-potential (sate) begins the homeostatic measure.

I prepared a nice Bird.
Eat(x) ⟹ [Sate(hungry) ⊇ Starve(food)x]

Subset (i.e., passive): Food is the subject predicated on Starve.
“I prepared a nice bird!” (A condition variable potential is presented.)

Food represents the passive-potential condition. Food is the target variable predicated on the passive-actual starve. How the organism aligns to its environment is about the passive-potentials. Starve is the organism burning more energy then it takes in. Starve balances the homeostatic measure.

A System of Contextual Unitization:

The source condition is what’s given, the target condition is its potentials.

8. The Word-Topic Database: Wiki & Acquisition

Human self awareness is a form dependent on the intentional forms we share with others.

— Michael Tomasello

The Q database is a wiki where people can create and edit quadranyms.

Quadranyms have four dimensions:

There are two default states or conditions: Subjective, Objective
There are two default modes or measures. Expansive, Reductive

Each dimension can collect or cluster terms for a particular topic.

Every quadranym is given a topic name (or head word)

Making a quadranym is nothing too tricky and actually fun to do. Choose a tittle for your topic and the terms for its default categories: Expansive, Reductive, Objective, Subjective. We call these categories the quadranym prime dimensions or facets. The next step is to plan your Q data scheme.

One can enter general Q units or create specific realms or domains.

Data: Like a head-word in a dictionary has different word senses, a word-topic in the database has different quadranyms. A word-topic is a Q realm.

Consider the word-topic eat. [sate y, starve x, food vector, hungry zeropoint]

Quadranyms can be used in reference frames. Other utility terms in a frame can be added.

Below, different (word sense) quadranyms for the word-topic eat.

The quadranym wiki is like a thesaurus.

Quadranym Unit Representation Examples:

Word-Topic: Space(x)

For all x, If x is space, Then x is:

Mode Sets: Expand = infinite ⊇ Reduce = finite
State Sets: Subject = void ⊇ Object = between

Shorthand:

1) Word-Topic: Space(x)

(∀x) space(x) ⟹ [Infinite(void) ⊇ Finite(between)(x)]

2) Word-Topic: Time(x)

(∀x) time(x) ⟹ [Future(present) ⊇ Past(event)(x)]

3) Word-Topic: Agent(x)

(∀x) agent(x) ⟹ [Active(self) ⊇ Passive(goal)(x)]

4) Word-Topic: Mental(x)

(∀x) mental(x) ⟹ [Unknown(knower) ⊇ Known(knowable)(x)]

5) Word-Topic: Locomotion(x)

(∀x) locomotion(x) ⟹ [Move(position) ⊇ Stay(place)(x)]

6) Word-Topic: Door(x)

(∀x) door(x) ⟹ [Open(passage) ⊇ Close(barrier)(x)]

Database Examples: (Spatial set with random quadranyms.)

Topic	Expansive	Reductive	Objective	Subjective
space	infinite	finite	between	void
time	future	past	event	present
distance	far	near	relation	position
direction	there	here	to	from
door	open	close	barrier	passage
container	out	in	full	empty
energy	active	passive	matter	motion
friend	affection	genial	companion	self
reside	move	stay	visit	live
perception	stimuli	select	organize	interpret
logic	proposition	conclusion	evidence	argument
scientific	hypothesis	fact	law	theory
science	prediction	test	analysis	hypothesis

Data Holds the Key: A word-topic is more than just a quadranym unit. The quadranym unit addresses the basic dimensions involved. Still, additional contextual inferences are required by all word-topic realms. A realm contains a knowledge graph that allows word-topics to adapt to different contexts.

A wiki allows users to collaborate and modify data.
Domains and realms can be specialized data projects.
The goal is interoperability between data projects.

Discrete systems with distinct topical orientations can be developed. Discrete systems work together to improve the word-topic data. Topical interoperability is the ability to adopt or reject the topical orientations of other systems. Competitive performance comparisons advance schemes.

The Quadranym Wiki: Users upload and edit quadranyms.

There can be a list of quadranyms for any one Topic.

9. Word-Sensibility in a Nutshell

Cognition generally described are coupled dynamical systems nested between, nervous system and body – body and brain – brain and environment.

— Randell D. Beer

The model involves unpacking a multi-organizational dynamic system. We might imagine each quadranym as representing a discrete dynamical system. All systems draw upon the environment and then give back to it, thus participating in an ecology of dynamical systems (dynamical contexts).

The source (state) virtually says, I feel this way about that target (state).

Reference frames are word-models that operate like units of homeostasis.

A word is like a sensing unit of homeostasis where it responds to changes presented to it in a text. It finds stasis by adjusting its coordinates on the x-y axes to coordinates that its zeropoint can most effectively anchor for.

For example, consider door rendered as a virtual unit of responsiveness.

Barrier represents potential coordinates. This is the target potential.
Passage is the zeropoint. This is the source that anchors the potentials.
Each unit is a kind of reference frame in which the dynamic sense of a word is balanced to a context e.g., IF context a THEN more x less y.

Consider the contextual dynamics of door for the sentences below:

Context a: “Close the door because it’s cold outside.”
Context b: “Open the door because it’s hot in here.”

Quadranym: (∀x) door(x) ⟹ [Open(passage) ⊇ Close(barrier)(x)]

In the example above, consider a reference frame model used to represent a homeostasis regarding door and responses to climate. Also, security, privacy or even aesthetics (word-topics cluster inferential word vectors.)

Now imagine any word able to have several references of responsiveness.

The project is about abstracting normative responses for machines.

10. Final Thoughts & Summary

(1) perception consists in perceptually guided action and (2) cognitive structures emerge from the recurrent sensorimotor patterns that enable action to be perceptually guided.

— Francisco J. Varela, Evan Thompson, Eleanor Rosch, The Embodied Mind; Cognitive Science and Human Experience, MIT Press 1991

The Approach:

The word-sensibility approach pertains to a dynamical systems description of a society of organisms who divide between members many organismic interactions and responses of ‘the_individual’ to the environment that, in its affect, produces the seeds of communication, ritual, culture and self identity.

Human communication is about sharing general spheres of responsiveness.

Action Based Theory of Context:

The Cycle: environments form the systems for the agent to form relations. Concepts are a byproduct of the interaction between an agent and its environment. The primary product is context from which the concepts emerge. This forms the contextual ecosystems that drives one’s responsiveness.

An action based theory of context includes empathy and mind reading.
The concept of affordance is borrowed to refer to apt motivated conditions.
These lead the agent’s virtual responsive-action on nested layers of context.

The Dynamical Context:

It might seem that the dynamical context provides an understanding of the situational context. However, we suggest that this is not quite right, or even backwards. It’s better to say that the dynamical context seeks predictions for itself, not for the situational context found in the text. The situational context imparts the environment that the dynamical context aims to predict.

Predicting external actions and consequences for self. See bottom of page.

Word-sensibility is less about objective cognitive attitudes and more about the affective and conative attitudes that likely shape its relational dynamics.

A dynamical context determines how a situational context is experienced.

Where word sense is about the intended meaning of a word to describe the situational context, the word-topic is about the responses that anchor the dynamical context to the “environment” that the situational context reveals.

The Database:

An objection to a method like this is the idiosyncratic nature of developing orientations. However we believe this may be a strength in the long run. Orientations create biases that allows systems to generalize. Each word is well defined and — each word has a contextual responsiveness. Topical interoperability is the ability for a system to share or reject the orientations of other systems i.e., the coherent bias between systems based on performance.

Word-sensibility is about sharing dynamic general-relevant orientations.

Review Important Points:

The system does not represent understandings — it represents responses.
Dynamical contexts have no truth conditions without situational contexts.
Dynamical context anchors the elements found in the structures of the text.
Inhibiting and debugging methods are necessary in the system’s processes.
The aim is to represent the human ability to create and share viewpoints.

System Summary: Environments are a place for our responses and our responses are a place for our words. The purpose of word-sensibility is to identify responsive units and model how they dynamically unitize, organize and interact in/as contextual ecosystems. The theoretical focus on context is more about word-level concepts then it is about sentence-level concepts and generally inspired by the views of enactivism. The aim is to intervene with a notion of words as systems of dynamical context. The word-sensibility model is proposed as a way to illustrate an ecological systems perspective on contextual responsiveness. Theoretically, it models a way to facilitate an exchange of orientation so to share normative type information with others.

We suggest, that a reciprocal relationship between a dynamical context and a situational context renders a responsiveness a word-sensibility orientation.

We aim to begin tests on the data system in the near future. The goal here is to introduce the basic ideas. Continued focus is on; wiki/acquisition and API.

11. Quadranym & Polynym Wiki/Aquisition

The human mind adapted to a symbolic culture, thus, extending biological memory out to function within a culture.

— Merlin Donald, 1991.

Check out the prototype database at…

polynyms.com

Open Source Language Project

We introduce a method to acquire sets of conceptual relations in a format of dimensions. We call it nymology, a practice of collecting and organizing idea sets: each a set of terms representing a certain number of dimensions used to strategically unfold, frame or simplify a concept. Each set functions as either part, step or type. A dimension number is called a polynym i.e., mononym, duonym, trionym, tetranym, pentanym and so on. Entries include, area, source, URL. A collection resembles a thesaurus and can be used to help populate knowledgebase systems and enhance queries.

What is a Polynym?
We use Polynym to describe idea sets (as part, step or type), such as:

3 parts × Freud’s psyche = [ Id, Ego, Superego ]
7 types × Deadly sins = [ Wrath, Greed, Sloth, Pride, Lust, Envy, Gluttony ]
5 steps × Grief = [ denial, anger, bargaining, depression, acceptance ]

There is a difference between artifactual polynyms based on long term study (accepted or rejected) and system processes representing polynym constructs assembled for the moment, to contextualize a situation.

Polynym idea sets are strategic ways to think about a topic. Strategies pertain to the Situational Context. That is, what is true or false. It is a deliberative process in that a topic is about what the condition is.

Polynym idea sets exist in virtually every discipline and are abundant.

What is a Quadranym?
We define Quadranym as a four-part conceptual construct using a dual-axis Mode-State model. It operates as a virtual unit of orientation & constraint.

Expansive-Reductive (ER-mode) and Objective-Subjective (OS-state).

Quadranym idea sets are about framing a response to a topic. Responses pertain to the Dynamical Context. That is, how one copes. It is on the heuristic level in that it is about a quick and easy response to a situation.

Inspirational Ideas For the Word-Sensibility Project

See Control Theory for inspiration (Achieving levels of control and stability).

Wikipedia (Using a feedback loop to control the process variable.)

See Efference Copy for Inspiration (The Q unit is like Sensory Feedback).

Wikipedia *(Predicting external actions and consequences for self.)*

Prototype Theory for Inspiration (Wikipedia)

Adding new information to existing Knowledge

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A Theoretical Look at the Role of Words for AI: Commonsense Ontology & Reference Frames