Word-Sensibility +

Model Introduction

Visit the About page for the project’s motivations, scope and goals.

Theoretical and Computer Model: Research includes, Anthroposemiotics, Ecological Psychology, Radical Embodied Cognition, Semiotics and Umwelt.

General Area of Discussion: Model Based Reasoning and Commonsense.

Introducing The Quadranym Model of Word-Sensibility:

Word-Sensibility is proposed as a method for 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.

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

The article is about schema concepts and not actual data preparation.
We are in the exploratory stage. We look forward to any feedback.

The Specificity of a Word’s Dynamicity

What are Affordances?

I also assume that they are not simply the physical properties of things as now conceived by physical science. Instead, they are ecological, in the sense that they are properties of the environment relative to an animal.

― James J. Gibson,

GOAL: Improve commonsense prediction with units of responsiveness.

Responsiveness is the ability to act quickly and positively to situations.
Units illustrate responding with dynamic sense. Units may serve as analogs.

Features of the Model:

Word-Topics (realms of word sense) organize, characterize and summarize lexical information. They’re pre-textual renderings of context used to anchor focus given to the words and the contextual developments in a text.

Every Word-Topic has a four dimension set called a Quadranym.

Quadranym 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.

Word-Topic Reference frame:

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

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

Reference frames are a framework used to graph responsive dynamics.

Quadranyms are realms of clustered information. Realms nest together.

Response & Prediction

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 x? Like in the game of Family Feud, the most common answer to the question wins the round.

“I will know x 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.

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

We propose the idea of, The Dynamical Context.

Essentially, it’s responsive adaptations to the environment used for communication. It’s about dynamic sense. Here are few more ideas:

Its job is to integrate its spatial and temporal dynamics with situations.
Distinct from the situational context in the contextualizing of word sense.
Dynamical context value is realized through the situational context.
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 reused in different ways and serve as an analog.

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

We’ll use the example 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.

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

An Ecological Perspective

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: Responsive Units.

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

A responsive body requires an environment as its primary affordance. Spatial affordance is a general quadranym frame for most hierarchical sets.

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

(Note: Hierarchical structure can be thought of as contextual timelines. In this context, space topic is primary in the timeline. It is the constant in time. The time topic is nested in the space topic. The agent topic is nested in the time topic. Mental topic is nested in the agent topic. Locomotion topic is nested in the mental topic. Door topic is nested in the locomotion topic. Each layer is a sequence in time. The general layers provide affordances for the relevant layers.)

All layers have virtual temporal setting cycles. The cycle for the word-topic space is unchanged for the entire cycle. It provides through as the spatial affordance. In the context, it is essentially already there as an invitation in the setting, and requires no change. The change is made by its target door. This is another topic. The word-topic door will likely require modification to its barrier. The invitation by through of the word-topic space sets that up.

Task: Define temporal setting cycles for each layer.

(Note: Temporal cycles are abstracted in quadranyms primarily between subjective and objective developments. Theoretically, these developments become regular oscillations. Oscillations are regulated differently on each layer. Spatial measures are abstracted primarily between expansive and reductive developments. These developments are illustrated in spatial reference frames.)

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.)

Each quadranym is a response from a particular self perspective. It adds up to a general responsiveness. Quadranyms are like neuronal 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 agents internal responses to external occurrences at different nested levels in the system.

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.

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

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

(Note: When sorting is complete, we say that the dynamical context is coupled to the situational context. Then the dynamical context fine tunes units to context.)

Source and Target Conditions:

The orientating concept has a specific role in quadranyms. It is the condition that is necessary or primary before any potential condition can be engaged.

(Note: The quadranym subject is the zeropoint of a word-topic reference frame and represents orientation. What follows is the quality, property or object that defines how it is to responsively align to a situation or context. The situation makes it clear how it should align. The affordance is the entire orienting process.)

In the schema, affordance is the source and its utility is the target.
Fine tuning a text involves mapping it to source and target conditions.

The source condition is also called the subjective state. We can think of it as the unattended state and anchor. The target is always the attended state.

Source and target conditions are internal features of quadranym units.
Textual elements map to the condition features situated in each unit.
It is a sequence: FROM the source condition TO the target condition.

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.

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

(Note: The entirety of the text begins as a superset of constants. The schema separates textual elements that have constant value from textual elements that have variable value. Any variable value feature is called the target condition. The constants of the superset represent the source conditions while the variable values of the subsets represent the target conditions. The source condition is what affords any adaptation potential in a given context. Potential is the target.)

Responsive adaptors (target potentials): will, know, walk, the_door.

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

Contextual Timelines: General to relevant temporal hierarchy.

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}.

States in layers are FOR particular textual elements. Consider topic time in example layer 3, present state is FOR as_I while its event state is FOR walk.

As a default, subjective {as_I} is what orients the objective variable {walk}.

Layers 1,2,3 provides affordance for layer 4 that can serve as analog for other contexts. For instance, instead of agent knowing agent can be leaving.

Dynamic Sense

Starting Aligned: Affordance conditions are pre-tuned in nested systems.

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.

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

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

Inputs are specified, such as, region x1 and region x2.
Inputs are actual and the outputs are their potential.
Peri-personal space can specify inputs from 0 – x0.9

Notes for Space Topic Graph: (key: potential y, actual x)

If x 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 is a new input and new viewpoint for a new cycle.
Cycles are quadranym frames that join together to make scripts.

Script Example (basic): (Without going into detail.)

Space topic source and target sets: (2 perspectives)

input1: [Infinite(void{…}) ⊇ Finite(between{…})]<find>
input2: [Infinite(void{…}) ⊇ Finite(between{…})]

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

A similar sort of calibration description applies to all frames.

Calibrate: Topic Time: FOR Event; Potential event responds to actual events.

Active-potential for future event. Actual procedure has already been enacted in time.

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

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

Additional unit inferences can nest, such as topic distance into topic space.

(Note: Notice that x and y variables have (+ -) polarities. They can be switched.)

The task is to weigh relations, provide temporal sequence and recognize goal. Scripts develop in the process. Scripts bring new levels of procedure.

(Note: Analogous, in some ways, are oscillatory and scale free brain activity.)

Conative Processes: Obstruction scenario between knower and knowable.

knower affords know (state) that motivates changes to place (state)
Void affords through (state) that motivates changes to door (state).

(Note: Notice how in 1, the mental topic’s subject layer 4 targets locomotion’s object layer 5. And also, notice how in 2, layer 1 targets all of layer 6 (see chart).)

Quadranym Entry Representation (Spatial Target – used on Layer 6):

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

Motivated Sense

Theoretical Concepts:

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

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

The truth of the situation is always in the text. The dynamical context couples to the text thus textual elements receive a virtual dynamic sense.

Dynamical context is a system to integrate with the situational context.
The integration provides scripts of responsiveness for future predictions.

Unit of Responsiveness Template

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

The template illustrates the left hemisphere as actual subjective power.
The right hemisphere illustrates the potential alignment to the environment.
A potential state is afforded by its actual state and is calibrated by modes.
The process starts with modes providing measure for the manner of response.
Once an environmental aspect is integrated, a responsiveness is rendered.

(Note: Conceptually, state values are about the alignment of a unit to its environmental condition. Mode value is how well that alignment is measured. (The potential mode must measure based on its opposition. Its opposition is the actual mode i.e., if up is the potential mode, then down is the actual mode. (For self orientation: actual is down based on pre-aligned spatial nests.) The model specifies the respondent as the potential and what it responds to as the actual.))

Motivation is Modular in Quadranyms:

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

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

Active energy represents the units using power e.g., organismic energy.
Passive energy represents the power being used e.g., environmental energy.
Active Sense represents more experience necessary i.e., find relation.
Passive Sense represents no more experience necessary i.e., relation found.

Above represents unit cycles occurring on multiple layers and timelines.

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.

Passive power is the environmental invitation for the organism to align.

Temporal Systems of Constraint:

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
Passive Power: rock

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 resource is a flux.
Representing organismic resource is a unit.

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

A flux point lives 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)]<flux>
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)

Word-Topics & Database Systems

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.

Different Word Sense Quadranyms for Word-Topic Eat

And, like a head-word in a dictionary has different word senses, a word-topic in the database has different quadranyms. A wiki populates database.

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.

In a Nutshell

The problem 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 i.e., dynamical context.

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

Word-sensibility is about words operating 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.

As we know, barrier is a changing dynamic FOR passage – of topic door.
Barrier represents potential coordinates. Passage is just the zeropoint.
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.”

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

In the example above, one can basically see how a statistical model can model a homeostasis regarding door and climate changes as well as, security, privacy and aesthetics (i.e., word-topics cluster inferential terms.)

The project is about abstracting normative responses for machines.

Theoretical Perspective

It might seem that the dynamical context provides understanding for 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 hints the “environment” that the dynamical context aims to predict.

Predicting external actions and consequences for self. See below.

Where word sense is about the intended meaning of a word describing the situational context; the word-topic is about the responses that anchor the dynamical context to the “environment” that shapes the situational context.

The system does not represent understandings, it represents responses.
The understanding is in the text. The responses anchor its influence.

We hope to run tests on the data in the near future. The current goal is to simply introduce the idea. Success depends on good test data and the wiki.