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Word-Sensibility –
Different ways to think about quadranyms. Making sense of sense refers to making human sense relatable to machine’s commonsense-system-programs.
Theory Articles
- Overview
- Introduction
- Theory & Approach
- The Principle of the Orientation of Interactivity
- Word Sensibility
- Psyche & Eros
- Driven
More Work In Progress
“The main difference between the Q Model and Q Anon is that the Q Model can explain Q Anon and Q Anon can’t explain shit.” – Dane Scalise
Scrap Yard
“The main difference between the Q Model and Q Anon is that the Q Model can explain Q Anon and Q Anon can’t explain shit.” – Dane Scalise
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.
Quadranyms Represent Normative Responses: (Proposal)
Normative responses aren’t about the order of things or the properties of things. They’re about response itself, as if responses where the only things.
- Quadranyms represent basic relational attributes of the normative response.
- The attributes hide in the societal mechanisms used to find order in things.
A normative response value requires the dynamical context and the situational context to couple; truth conditions engage and bring relevance.
GOAL: Improve commonsense prediction with normative responses.
- Normative responses are the kind of responses that people normally share.
Model Concept: (Theoretical Proposal: normative responses become standard.)
- Normative responses relate dynamic sense (e.g., spatial) as analogs for viewpoints.
Features of the Model:
The Normative response isn’t about the location or properties of things. It’s about response itself, as if responses where the only things. For example, you work to make money – the amount of money you accumulate is proportional to the amount of work you do. Is that statement true? It doesn’t matter to the normative response. The communication is a response to process – environmental and social order emergent in process.
- Quadranyms represent basic relational attributes of the normative response.
- The attributes hide in the societal mechanisms used to find order in things.
Communication holds value when the dynamical context and situational context couple; truth conditions engage and the process becomes relevant.
Our current goal is to gather research. Word-sensibility is more about responses than predictions. Commonsense prediction comes later. That is, you can have a response without prediction but a prediction requires responsiveness. We offer a distinction as not to conflate the two. In the model, only after responses become predictable does a structure for sharing intentions and viewpoints become available. This, of course, would be an outgrowth of social behavior and intersubjectivity. Our idea offers a kind of meditation on how the responsive machine might be more human like. Particularly, normative responses for more human like communication.
- The normative response is a compression of topic potential.
Calibration analysis for example topic space (frame above):
- More separate region is -x. This could refer to solid or distance.
- In this context, it is solid (door). If x = 1, then y is potential for x = 1.
- Left of x is a single region. Right of x is multiple regions (adjacent).
- This is important because an x region can close in all the way to self.
- The question is… does whole region y increase in or out of region x = 1?
- The self has affordance of space based on the potential void in region.
- If y variance is inside x = 1 then y is potential space only for x = 1.
- If self x = 1 moves to the right to x = 2 then a new region is inputted.
- This means that the y potential now involves two actual regions.
- The new region is a new input and new viewpoint for a new cycle.
- Cycles are quadranym frames that stitch together to make scripts.
Affordance Parameters: topic space (above)
- x refers to multiple, separate, actual regions.
- y refers to a single, whole, potential region.
- Separation is solid not spatial in this context (door).
- The origin is the central point to both views of region.
- Left of x input refers to the separate region of the origin.
- All of y is the afforded amount of spatial potential for origin.
- If region y increases then it is either inside or outside x region.
- If x represents origin region then y potential in only for that region.
- If open door adds new region input then y potential spans both regions.
- This means that the y potential now includes two actual regions.
- 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 stitch together to make scripts.
Script Example (basic type): Without going into detail.
- input1: [Infinite(void{…}) ⊇ Finite(between{…})]<find>
- input2: [Infinite(void{…}) ⊇ Finite(between{…})]
A similar sort of analysis applies to all frames.
Note: During this illustration, keep in mind that the situational context (text) is always true. It is not the job of the dynamical context to decide what is true or false in the text. Its job is to simply respond to it. The dynamical context is like a clock and the situational context is like another clock. If well synchronized then the dynamical context is on a true path, if not then it’s on a false path. We are not going to explore this line of thinking here but it is useful to keep in mind. Also we chose the sentence below because it allows us to easily touch on many features.)
- Example: “I will know as soon as I walk through the door.”
“We should begin thinking of events as the primary realities and of time as an abstraction from them—a concept derived mainly from regular repeating events, such as the ticking of clocks. Events are perceived, but time is not (Gibson, 1975).”
― The Ecological Approach to Visual Perception
Because sensibility has no formality like reason has with logic, the jumping off point on the subject of word-sensibility is a particular kind of problem. The general scope of word-sensibility would seem to apply to logic, semantics and really, all human methods of assessment. In linguistics, there are a wide variety of semantic approaches that exist for dealing with aspects of human sensibility, such as, how languages might encode relations between aspects of the world to convey, process, and assign meaning. Our premise is that situational context influences involving truth conditions about the world are initiated by a fundamental unitizing system, a resonance free of conditional judgment but essential to the process.
- In this approach, an orientation is required for a truth condition to be given.
As a public interface, quadranyms are best represented on a mode y and state x axes. They also configure for dependent & independent variables.
In the representation above, infinite void is dependent on finite to configure a situation for space. Between is the target variable. Void is the zero-point. Quadranym notation begins generally and adjusts to specific conditions.
- Modes: E = potential ⊇ R = actual (a.k.a. predicates, functions & action y)
- States: S = actual ⊇ O = potential (a.k.a. subjects, arguments & being x)
There are various ways to unpack contextual functions from a quadranym.
- active-sense f(x) (function & argument) = Open(passage)
- passive-sense f(x) (function & argument) = Close(barrier)
Active sense initiates a topics function and argument. Passive sense is when the function and argument is finally configured for the situation.
The notation allows a user to intuit a topics function configurations. States are relevant to the input variables while modes regard the actions needed.
Consider for instance…
Open(passage): the action of open depends on the input of passage_x. If input is exit then the function is Out. If input is enter then the funtion is in. If the input is climate then the funtion is dependent on the target variable.
- Contextual Expectation: Machines can’t understand therefore can’t really generalize. So the question is, can machines approximate generalizations?
During the analysis of a text, all of the text begin as the superset and then gets filtered into subsets. The process separates constants from variables
Cluster: {…}
- Open{…)(passage{…}) ⊇ Close{…}(Barrier{…})
- The process does not define the situation, it defines its dynamics.
Consider the utility of door as described in the following situation…
Situational Context
Text: “Close the door because it’s cold outside.”
Dynamical Context
Topic Factors: {time, space, door, valence, temperature}
Meta-Dimensional Roles: {now, space ,open, door, close, temp, affect}.
Content_Roles: {<Close, the_door, because, it’s_cold, outside>}
There are a variety ways to parse text into units below. It depends on how scripts are set up.
space: Infinite{invariant)(void{outside}) ⊇ Finite{variant}(between{temp})
affect: Positive{warm)(affect{because}) ⊇ Negative{cold}(situation{change})
temp: Cold{out}(optimize{Close}) ⊇ Warm{in}(condition{cold})
open: Open{cold)(passage{the_door}) ⊇ Close{warm}(barrier{event})
now: Future{close}(present{it’s_cold}) ⊇ Past{open}(event{door})
The example shows how quadranyms can configure statistical models. In this realm, door is configured to assess optimization in a climate context.
(NOTE: 100% closed will get the desired temperature. Open includes all of the environment. Closed is a region. Temporal and spatial factors configure together to optimize desired conditions; procedure = less open & less time open. If door remains open desired temperature will be reduced. Because door is a subset of the spatial domain it is nested in spatial dimensions. This form is illustrated in any matrix table of nested topics. The point being, the variability in climate is dependent on the division of spatial modes, all_out & some_in. All space is out temperature non-discrete. In the door realm, any variance is dependent on barrier, i.e., close_in is the independent variable that influences open_out temperature. All Quadranyms can conform in this way in some manner.)
(Note, quadranyms are configured to nest dimensional relations. This helps provide the source and target anchor and tendency for the entire set of nested units. However, at times the situation is better served when the independent variable x and dependent variable y can switch, this is called a switched polarity.)
Site Summary: There are innumerable situations that humans would know and machines would not. The basic idea is for machines to have the ability to learn to abstract, at word level, the kind of contextualizing responses that human experience in the world. In this way a machine can make better sense of the things that people typically know. A conceptual model and propositions are proposed that could form the basis for further research.
Site Summary: There are innumerable situations that humans would know and machines would not. The basic idea is for machines to have the ability to abstract, at word level, micro-topics that effectually characterize or summarize human responsiveness. In this way a machine can make better sense of the things that people typically know. A conceptual model and propositions are proposed that could form the basis for further research.
Deep Analysis of Script:
A script is a procedure to think through a general sphere or cycle of understanding as it might apply dynamically to a situational context.
- [Far(position) _ Near(relation)]
The actual-subject is central to any unit or script. The speaker takes the actual-subject i.e., speaker defaults to the actual-position (deixis). The speaker is the anchor. The predicates are, far = variant, near = invariant. Central, is the actual-subject predicated on potential e.g., Far(position).
- [Far(position) ⊇ Near(relation_object_place… )]
The potential-subject (relation) refers to the prime variable predicated on actual e.g., Near(Relation). More terms may cue as the script progresses.
Scripts can be calibrated in various ways. Notice, in the first unit, relation is predicated on near. Since the text is far friend, Near(relation) progresses.
- next unit [Far(relation) _ Near(object)]
The target-subject of the last unit becomes the anchor-subject of the next unit. The predicates oscillate back and forth through each unit of script.
- [Far(relation) _ Near(object)] progresses [Far(object) _ Near(place)]
The program aims to justify the actual predicate of relation, Near(friend). Justification is for the dynamical context and not the situational context.
The application generates predictions for the dynamical context and, anchors orientation for prediction models of the situational context.
First Unit:[Far(self_position_my) ⊇ Near(relation_other_here)]
- The question becomes, how are the predicates affecting the subjects?
Second Unit:[Far(relation_there_from) ⊇ Near(object_person_to)]
- Far is continuous and variant as Near remains discrete and invariant.
Third Unit:[Far(object_there_out) ⊇ Near(place_friend_in]
- Far(x) is a potential dynamic that predicates continuing out i.e., globally.
- Near(x) is an actual situation that always predicates local and discrete.
Forth Unit:[Far(place_there_lives) ⊇ Near(object_friend_stays)]
- Discrete variants: Far(relation) + Far(object) + Far(place) = Near(friend).
(Synopsis; Dynamical Context is anchored on position. The Situational Context Near(x) is its independent variable. Far(x) is now its discrete dependent variable.)
The point of production is where “continuous” Far(x) becomes discrete units. Each anchor is an actual argument for the independent variable Near(x).
Each unit is a discrete new argument of the dynamical context. Again, the aim is an optimal path from a dynamical context to a situational context.
Scripts represent a general model of sequential learning. Every cycle closes the loop on data as dimensions iterate and terms filter into their roles.
Script Interpretation:
Given the text:
- “My friend lives far from here.”
BECAUSE:
- Far = potential
- Near = actual
THEREFOR:
- IF:
Far_relation = Far_object_friend
THEN
Far_place = Near_object_friend
The dynamic of a far friend?
- Self = dependent far_place + independent near_friend
Gross Units:
[Far(position) _ Near(relation)]<find>[Far(relation) _ Near(object)]<find>[Far(object) _ Near(place)]<find>[Far(place) _ Near(object)]<stop>
(Note: <find> refers to find ordered pairs.)
Net Units:
Far(position) + [Far(relation) + Far(object) + Far(place) = Near(object)
Knowing:
The self knows; position, person, relation, object, place, other and so on.
Gloss Utility: How affected subjects of each unit are settled by copulas.
1, Position: IF position IS position THEN relation IS here_self OR there_other.
- For First Unit: [Far(position) ⊇ Near(relation)] → here OR there.
2, Relation: IF position IS relation THEN object IS here_self AND there_other.
- For Second Unit: [Far(relation) ⊇ Near(object)] → this AND that.
3, Object: IF position IS object THEN place IS here_self OR there_other.
- For Third Unit: [Far(object) ⊇ Near(place)] → here OR there.
4, Place: IF position IS place THEN object IS there_other NOT here_self.
- For Forth Unit: [Far(place) ⊇ Near(object)] → there NOT here.
Q–Unit:
Example: Navigating.
- Quadranyms populate Q-units.
- 2 Modes + 2 States = 1 Q-unit
- [Mode_Selector(State_self) → Mode_Critic(State_world)]
Above are examples of four dimensions for a Q-unit. Navigation in this instance is comprised of four components: Selector Mode, Critic Mode, Self State, World State. These components act as primary roles for this dynamical context example of navigation. We refer to these roles as, Meta-Dimensional Roles (M-Roles). Roles of the dynamical context that might be attracted to these roles are called, Content Roles (C-roles).
Quick Review of Q-unit Positional Dynamics:
The Q-Unit represents the upper right quadrant of the C graph. All words of a dynamical context begin in the negative quadrant as the superset cluster. Words in the cluster that generally need attended to are deemed positive terms, or passive potentials. Words that generally do not need attended or can remain non-declarative stay negative, or active actuals. Relationships between negative terms (active-actuals) and positive terms (passive-potentials) create subset trajectories of a dynamical context.
- active-actuals ⊇ passive-potentials
Consider space as a layer constraining the dynamical context, navigation:
- [Mode_Selector(State_void) ⊇ Mode_Critic(State_between)]
Space cluster example:
Space{void, between, infinite, finite, object, fit, path, locomotion, obstruct…}
Consider the terms above, some terms of a cluster play the dimensional roles. Each term of a cluster is a possible vector point depending on the dynamical context. Each quadrant represents a dimensional role. Dimensions change per topic (e.g., space). The zero point of reference represents the subjective quadrant (void). The zero point is about those things not seen by others or are unattended to the subjective sense but power it. For instance, one does not attend the emptiness of space if not necessary. In day to day living, it is those things that are in the emptiness of space that generally concern us. The objective sense is always about positive things (between), even if only theoretically. The earth we walk on and the obstacles we avoid will usually belong to the positive quadrant of space. The point being, what is afforded is not necessarily attended to.
General Framework of Space Sense:
- (∀x) space(x) ⟹ [infinite(void) ⊇ finite(between)(x)]
Q-unit: [-+(- -) ⊇ +-(++)] = x:
- – – = active-actual (00)
- -+ = active-potential (01)
- +- = passive-actual (10)
- ++ = passive-potential (11)
Active-actual refers to more experience necessary. It is about a subjective sense that is being driven by the environment and always deals with a negative dynamic of a topic. Passive-potential refers to no more experience necessary. It is about the attended objective sense most responsive to the unattended subjective sense. For instance, between represents a positive sense to the subjective sense of void in the dynamical context of space. Locomotion and spatial relations between objects are the kind of resourceful tendencies and motivated behaviors used to form any sense unit of space.
More Notes on Q-unit Vector Dynamics Theory:
It might seem a bit counter intuitive at first. One might assume positive terms are active-actuals and negative terms are passive-potentials. However, passive-potentials represent what’s known where active-actuals represent basic responses driving what’s known. It is really the environment driving where flux and unit relationships are virtually embedded in the agent. Active-actuals are anchors that couple passive-potential concepts to environmental conditions at each layer. Passive-potentials are just that, passive until driven. Theoretically, once driven, a virtual oscillation between active-actuals and passive-potentials begin. This forms scripts. Ontologically, where word sense is about how a lexical unit is linked to a concept, word-sensibility is about the dynamic frameworks used to anchor those concepts. Word-sensibility is not about truth conditions, rather, it is about motivations, tendencies and habits. In short, passive-potentials are about cued up subsets of words and active-actuals are about dynamic instance of sensibility powered up to relate those words.
The Q aims to reflect the subjective nature of sensibility. When sensibility is active most of that activity is unattended (off to awareness). Most of our thoughts are unattended according to cognitive scientists. The Q is about the trace between pre-reflective and reflective thoughts. When a thought is passive that means no interactive processing is necessary to understand it, it is already positive, it is there or it is that. That is, passive means no more experience necessary and refers to those senses able to be positively identified and used because they are coupled to active-actual anchors being driven to drive them. Active-actuals are virtually motivated to find passive potentials and urge the more experience necessary sense. This dynamic forms all scripts. Scripts are units of trajectories linked together to identify new trajectories. Scripts then layer to form hierarchical structures. Hierarchies ground strategies for dynamic contextual understanding.
Basically, subjective and objective states are part of the same monism or oneness and are only dualistic to fit a dynamical context or trajectory.
See: Theory & Approach
pc1. Pseudo Code Example
Word-Sensibility & Ecological Reasoning
Making & Tweaking Scripts
Analyzing relations of locations in sentences. This idea is for database operators who review scripts and tweak them as necessary. Prompts allow for improvements. It then generates analysis reports on scripts.
* “My friend lives far from here.”
IF far THEN FUNCTION f(x) = Distance(x)
RETURN ARGUMENT INPUT Distance(x)
PRINT:
[qFunctions (modes)
{Far, Near}
qArguments (states)
{position, relation}]
PROMPT: RETURN ADDITIONAL CONTEXT ELEMENTS:
PRINT:
Object(x)
Place(x)
(note, the above is from a situational context polynym. see analysis report. it deals with subjects, objects, goals and deictic elements)
PROMPT: RETURN INPUTS AS ARGUMENTS:
PRINT:
[Far(position) _ Near(relation)]::[Far(relation) _ Near(object)]::[Far(object) _ Near(place)]::[Far(place) _ Near(object)]<stop>
(note: each input changes their function at least once except for the input position, as it is the prime argument that all others are based on. and also object, that changed three times as it is the final argument.
INTERPRETATION OF PROPOSITION:
PRINT:
IF:
Far_relation = Far_object
THEN
Far_place = Near_object
(note, above are all elements of the ontological realm of distance. these elements are pulled from a cluster and are virtually attended in the script (position is primary but unattended). as a composition, it would be interpreted something like, “the object is at a place far away from here.” (interpretations like this could involve phrasal templates.))
PROMPT: RETURN ADDITIONAL CONTEXT ELEMENTS:
PRINT:
Person(x)
(note, this can be chosen from situational context elements)
RUN PARSE SCRIPT W/CONTENT INPUTS:
(note, this is pulled automatically from sentence and unit default)
PARSE UNIT Person(x): (note, single quadranym)
FROM: [Far(position) TO Near(x)] = relation_qFunction_person
RETURN FUNCTION f(x):
PRINT:
Friend(x)
PARSE FLUX Person(x): (note, between quadranyms)
FROM: [Near(FRIEND)] TO [Far(x)] = position_qFuntion_person
RETURN FUNCTION f(x):
PRINT:
Self(x)
CONDITIONAL FUNCTIONS W/ARGUMENTS:
PRINT:
Self(position) → Friend(relation)
RETURN:
IF RELATION INFERS ACTION RUN IN RELATION POSITION
ELSE RUN RELATION IN OBJECT POSITION (i.e., noun)
PRINT SCRIPT
[Far(SELF) _ Near(relation)] | [Far(relation) _ Near(FRIEND)] | [Far(FRIEND) _ Near(place)] | [Far(place) _ Near(FRIEND)]<stop>
(note, like a rock can become a tool of potential, it only has power when acted on. once the rock is acted on in a way that serves a goal, it becomes active_actual and is part of the agency itself. in this situation, the term relation is like that rock, relation becomes part of the agency and begins its own becoming. this is how a script works. it is an oscillation between actual and potential states. once the potential is actualized it is then a new active state with a new potential. however, its previous state remains as a passive_potential_state to be reused. notice that relation, friend, place all change between two different states. those arguments all act in the mode/function of near and in the mode/function of far. the spatial scope is about how the function far, as the active potential mode, increases the area of objects and events. near on the other hand, decreases the area of objects and events and narrows the scope.)
PROMPT: IF POSITIVE ASSESSMENT SAVE SCRIPT
PROMPT: RUN ANALYSIS REPORT THEN PRINT:
PROPOSITION CONDITIONAL:
IF:
Far_relation = Far_friend
THEN
Far_place = Near_friend
OUTPUT QUADRANYM:
q4:[E_Far(S_place) _ R_Near(O_friend)]
OUTPUT FRAME RANGE = (1 of 4, unit 4)
INPUT QUADRANYM:
q1[E_Far(S_position_self) ⊇ R_Near(O_relation_x)]
INPUT FRAME RANGE = (1 of 4, unit 1)
POLYNYM SITUATIONAL CONTEXT FUNCTIONS = {person, object, place}
OF:
Person(x)
Time(x)
Discourse(x)
Place(x)
Purpose(x)
Subject(x)
Object(x)
Inference(x)
Goal(x)
Q ATTRIBUTES:
SUPERSET/SUBSET CLUSTERS:
E(s)_Superset {position, self, far, near, friend, object, place, relations…}
R(o)_Subset {relation, object, place, friend}
THE QUESTION HEMISPHERE = E(s):
Far = E, active_potential_spatial_mode
Position = S, active_actual_temporal_state
(note, active refers to more experience necessary to obtain a sense of a situation. in other words a unit is started but not complete. actual refers to the temporal center of the process. it reflects the metaphysical notion of being presently active with input power from the environment.)
THE ANSWER HEMISPHERE = R(s):
- Near = R, passive_actual_spatial_mode
- Relation = O, passive_potential_temporal_state
(note, passive refers to no more experience necessary. a sense is complete and now can be used in a variety of ways. it reflects the notion of a potential power to be utilized in relation to a situation)
Q CYCLE FUNCTIONS:
- E(s) RUN CYCLE FUNCTION: x is questioned = Far(x)
- R(o) END CYCLE FUNCTION: x is answered = Near(x)
(note: an active_actual _temporal state is the metaphysical notion of Q agency. it represents active_actual_power only in the present of time. passive_potential_temporal_state refers to a mental object of the agency becoming in time. it represents passive_potential_power. again, a rock can become a potential tool, but it only has power when acted on. once the tool is acted on, it is then active_actual and part of the agency itself)
FIRST ORDER CONCLUSIONS:
qArgument: ∀x(Self(x) → Relation(x, friend)
qFunction: ∃x(Friend(x) → Far(x, place)
PHRASAL TEMPLATE: NO RETURN
HIERARCHICAL STRUCTURE: NO RETURN
NESTED INFERENCES: NO RETURN
(note, a Q can be unpacked much further with deeper attribute analyses.)
Save: OI
Humans have a clear advantage over machines when it comes to understanding words because humans experience the world and machines don’t. Humans apply intentional variability to their experiences, a likely result of highly evolved social behaviors. Word-sensibility initiates before a word-sense so to allow general responsive dynamics of one’s experience to manifest. The word-sensibility model illustrates an equivocation process to provide orientation for our abstractions. We suggest, that through conative and affective exchanges, humans have acquired a skill to capture in themselves a sense of actual activity (i.e., dynamical context) used to constrain a sense of potential conditions (i.e., situational context) of an experience or behavior shared between people. The idea is that the ‘coherent sense‘, refers to the orientation of interactivity (OI) occurring between people. One orients with another. The orientational process describes a kind of empathy where individuals learn to orient abstract information with others and consequently also for themselves (e.g., myths).
Humans have a clear advantage over machines when it comes to understanding words because humans experience the world and machines don’t. Humans apply intentional variability to their real world experiences, a likely result of highly evolved social behavior. The word-sensibility model illustrates an equivocation process to provide orientation for our abstractions. The orientation of interactivity (OI) describes a kind of empathy where individuals learn to orient abstract information with others and consequently also for themselves (e.g., myths). Word-sensibility initiates before a word-sense so to allow general responsive dynamics of one’s experience to manifest. We suggest, that through conative and affective exchanges, humans have acquired a skill to capture in themselves a sense of actual activity (i.e., dynamical context) used to constrain a sense of potential conditions (i.e., situational context) to which another person is drawn in to educe the same basic constraining behavior. For individuals, one can experience a dynamical context to which certain situations can be thought about using explicit thoughts or essentially, talking to one’s self. Here, the idea is that ‘coherent sense‘ refers to OI attributed to only one. When the OI experience or behavior is shared this refers to one orienting with another and ‘coherent sense’ is attributed between people.
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The twelve systems are not scientific models but insights into degrees of organisation. In his first major essay in this field, The Dramatic Universe (Vol.
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The First Eight Systems
(1) Monad = Wholeness
(2) Dyad = Complementarity
(3) Triad = Action & relatedness
(4) Tetrad = Activity
(5) Pentad = Potential
(6) Hexad = Event
(7) Heptad = Transformation
(8) Octad = Completedness
1. State: the length of a temporal cycle i.e. active_begin → passive_end
2. Mode: a realm of spatial factors i.e. active_out → passive_in
3. Active: refers to more experience necessary i.e. active_ power
4. Passive: refers to no more experience necessary i.e. passive_power
5. Subjective: being-in-time i.e. active-actual state_temporal center
6. Expansive: acting-in-space i.e. active-potential mode_spatial factors
7. Objective: temporal-becoming i.e. passive-potential state_Apt Events
8. Reductive: spatial-measure i.e. passive-actual mode_location relation
Also…
9. Being: harmony in time i.e. actual is directed
10. Actual: substance in space I.e. actual is shared
11. Becoming: substance in time i.e. potential is given
12. Potential: harmony in space I.e. potential is excepted
Here we illustrate Q matrix concepts. This page will be as self contained as possible. This page offers a direct and basic breakdown of the model’s basic components. It is helpful to also read the About Page and the Model Page.
Below is a Q table. It represents a Polynym. Each topic name represents one of its dimensions (ranks). Polynym dimensions can be of any number.
This one is fashioned in five dimensions (i.e., pentanym).
5 parts × Relations of Locations = [space, door, distance, direction, container]
| space | infinite | finite | between | void |
| door | open | close | barrier | passage |
| distance | far | near | relation | position |
| direction | there | here | to | from |
| container | out | in | full | empty |
| time | future | past | event | present |
Unlike polynyms that have any number of dimensions, quadranyms align and nest (local) topics using four (global) dimensions. Prime dimensions form all quadranym units. The terms in each of the EROS columns tend to follow and fill in a pervading sense. Meta-Dimensional Roles are the general terms (M-roles). Specific roles are called content roles (C-roles). C-roles are attracted to M-roles. M-roles work to make C-role relations tractable.
In the matrix above, notice how temporal & spatial realms align or nest together. Although the nestings suggest certain relationships, these are primary positions that can be altered through scripts as we will see later.
Q analysis is about how dynamic roles are equivocated to satisfy the task of describing a condition. Roles of a column can be connected using relations.
- ConceptNet types e.g., between isA barrier, open motivatedBygoal out.
- Also, relations can connect rank terms e.g., open obstructedBy barrier
A quadranym is a simple organizing construct that is reasonably explained in minutes. Below is an introductory outline of the quadranym dimensions.
Q Prime Dimensions & M-Roles:
- E: Expansive (potential mode) term examples: potential, active, unity, group, over, all, new, implicit, big, learning, playing (or a kind of child like view of life where the world is constantly unfolding), mystery, sustain, spatial openness.
- R: Reductive (actual mode) term examples: actual, passive, plurality, it, that, you , me, fact, in, down, measure, explicit, focus, small, (or a kind of adult view of life where the world needs controlling), familiar, deny, spatial closeness.
- O: Objective (potential state) term examples: potential, becoming, condition, variant, practice, decide between, interpersonal, social, temporal endings.
- S: Subjective (actual state) term examples: actual, being, coherent, core, constant, perspective, beliefs, desires, intrapersonal, temporal beginnings.
Various metaphysical notions apply to the Quadranym Prime Dimensions such as, part/whole, plurality/unity, being/becoming, time and space.
Q Prime Dimensions from: Model Page
A Q word is written, nTopic = Topic Name for Word. For example…
- nSpace
- nDoor
- nDistance
- nDirection
- nContainer
Quadranyms act like anchors of word-sensibility for word sense.
Consider space and its word sense entries in Wordnet.
- S: (n) space, infinite (the unlimited expanse in which everything is located) “they tested his ability to locate objects in space”; “the boundless regions of the infinite”
- S: (n) space (an empty area (usually bounded in some way between things)) “the architect left space in front of the building”; “they stopped at an open space in the jungle”; “the space between his teeth”
- S: (n) space (an area reserved for some particular purpose) “the laboratory’s floor space”
- S: (n) outer space, space (any location outside the Earth’s atmosphere) “the astronauts walked in outer space without a tether”; “the first major milestone in space exploration was in 1957, when the USSR’s Sputnik 1 orbited the Earth”
- S: (n) space, blank (a blank character used to separate successive words in writing or printing) “he said the space is the most important character in the alphabet”
- S: (n) distance, space (the interval between two times) “the distance from birth to death”; “it all happened in the space of 10 minutes”
- S: (n) space, blank space, place (a blank area) “write your name in the space provided”
- S: (n) space (one of the areas between or below or above the lines of a musical staff) “the spaces are the notes F-A-C-E”
- S: (n) quad, space ((printing) a block of type without a raised letter; used for spacing between words or sentences)
A lexicon like wordnet defines a word (with gloss and synsets) for as many distinct senses in the entry. A word’s general message or pervading sense is, in some manner, often perceived in each of the different senses. What kind of framework can be used for this kind of analysis? Researchers are discovering that the mind responds to written text using many of the same resources (i.e., motor and sensory perception) used to interact in the world. Regarding words only as atomic sentential elements of grammar does not suit this kind of analysis. The grounded sense of words seem to exist in the interactions between us and those things we resonate with in the world; each word, remembered dynamics, educing a sense of, dynamical orientation.
- Orientational Template: nTopic[E_mode(s_state) ⊇ R_mode(o_state)]
- A topical orientation of space; nSpace[Infinite(void) ⊇ Finite(between)].
- Mode sets: potential ⊇ actual (a.k.a., predicates, functions & action)
- State sets: actual ⊇ potential (a.k.a., subjects, arguments & being)
Quadranyms contain two sets: the superset virtually holds all the elements that the system has on nTopic. This is the source set (a.k.a., the unattended set). Any subset of the source is the target set (a.k.a., the attended set).
- unattended_source ⊇ attended_target
Quantification all or some default: Infinite = All ⊇ Finite = Some. The situational context quantifies conditions e.g., room, yard, arena, that area.
- x = Infinite(void_room) ⊇ Finite(between_walls)
Below is a list of synonyms (C-roles) for space from Thesaurus.com.
Space {area, arena, capacity, distance, field, location, slot, spot, territory, zone, amplitude, blank, breadth, compass, expanse, expansion, extension, extent, gap, headroom, headway, infinity, interval, lacuna, leeway, margin, omission, play, range, reach, spaciousness, sphere, spread, stretch, tract, turf, volume, elbowroom}.
- Create trajectories for: source set ⊇ target set.
For the space template; void anchors the dynamics of the entire set. It functions like a heuristic bias and referred to as the, Coherent Bias. All C-roles are anchored on void and become attended under the term between.
- M-roles: {<void, between>}
- C-roles: {area, arena, capacity, distance, field, location, slot}
Q Gloss Utility: Phrasal template for C-Roles Intending SPACE:
- IF void IS area THEN IT IS between here AND there.
- IF void IS arena THEN IT IS between this AND that.
- IF void IS capacity THEN IT IS between empty AND full.
- IF void IS distance THEN IT IS between here AND there.
- IF void IS field THEN IT IS between this AND that.
- IF void IS location THEN IT IS between this AND that.
- IF void IS slot THEN IT IS between this AND that.
- and so on…
Q Gloss Utility has variations. It is made up of; copulas, quantifiers, subjects, predicates & conditionals i.e., ALL of the Area(between) or, IF some area THEN it is between this AND that. Copulas and conditional terms are capitalized.
Distance is potential relation from one’s position, far and near are the actions.
Below, a list of synonyms (C-roles) for Distance from Thesaurus.com .
Distance {area, length, orbit, radius, scope, separation, size, space, span, stretch, width, absence, ambit, amplitude, bit, breadth, compass, expanse, extension, extent, farness, heavens, hinterland, horizon, lapse, objective, outpost, outskirts, provinces, purlieu, purview, reach, remoteness, remove, sky, spread, sweep, way, country mile}
All of the C-roles are anchored on position and attended under relation.
- M-roles: states:{<position, relation>}
- C-roles: targets:{area, length, orbit, radius, scope, separation, size}
Q Gloss Utility: Phrasal template for C-Roles Intending Distance:
- IF position IS area THEN relation IS here NOT there.
- IF position IS length THEN relation IS FROM there TO here
- IF position IS orbit THEN relation IS central TO here
- IF position IS radius THEN relation IS central TO here.
- IF position IS scope THEN relation IS FROM this TO that.
- IF position IS separation THEN relation IS there NOT here.
- IF position IS size THEN relation IS FROM big TO small.
- and so on…
The application is not so much about prediction although it assists in that. The application is more about anchoring orientation for prediction models. This is another distinction between dynamical and situational contexts.
Q analysis is based on the Dynamical Context and not the Situational Context. The differences between contexts will be reviewed in the next section. For now, a basic example of a Dynamical Context script is given.
Basically, a script allows a target of some condition to become the new bias. Q is about heuristic thinking or quick thinking. Slower more deliberative thinking is a hand-off to another process level (See; Thinking Fast & Slow).
Orientation for Topic Distance:[Far(position) _ Near(relation)].
The true meaning of distance is found in the situational context. That is, the situational-context–text will answer any dynamical-context questions.
- “My friend lives far from here.”
Textual Elements: {<my, friend, lives, far, from here>}
Dynamical contexts cued.
- Direction(x) → [There(from) ⊇ Here(to)](x)
- Friend(x) → [Affection(self) ⊇ Companion(other)](x)
- Reside(x) → [Move(live) ⊇ Stay(visit)](x)
- Distance(x) → [Far(position) ⊇ Near(relation)](x)
- Mine(x) → [Give(possess) ⊇ Keep(object)](x)
Not all of these units need to run as scripts. Distance is a good general constraint and orientation for all of the units to be structured in.
Distance may continue to anchor as the composition continues. Every quadranym can re-scale, FROM word topic TO theme topic.
Preloaded Typical-Target-Set for Distance
- position(x)
- relation(x)
- object(x)
- place(x)
Script actions & components:
A script is a procedure to think through a general sphere or cycle of understanding as it might apply dynamically to a situational context.
The target subject of the last unit becomes the anchor subject of the next unit. The predicates oscillate back and forth through each unit of script.
Scripts run when first unit fails. Notice that in the first unit, relation is predicated on near. Since relation is friend and friend is far then, near what?
- Central to the notion above is position = self. The self acts as a kind of deictic center, an invariable. Notice that in the paradigm far is a potential. Self is predicated on the potentials of far. The actual is about what is predicated on near. Since relation_friend is predicated on near in the unit, it conflicts with the situational context of the text. Orientation targets friend as near? The target is wrong. The friend is not near. Near’s potential subject is targeted.
The question:
- What subject is predicated on Near?
With the completion of this task, a common target is found for all units.
Script (gross units):
[Far(position) _ Near(relation)]<find>[Far(relation) _ Near(object)]<find>[Far(object) _ Near(place)]<find>[Far(place) _ Near(object)]<stop>
(Note: <find> refers to find ordered pairs.)
Solve distance for Text:
Distance(x) → Far(position_self) = Near(relation_friend)(x)
Given situational context text:
- “My friend lives far from here.”
The answer are the net units.
Answer: Far(relation) + Far(object) + Far(place) = Near(friend).
Textual Interpretation of distance:
- IF:
Far_relation = Far_object
THEN
Far_place = Near_object - IF:
Far_relation = Far_friend
THEN
Far_place = Near_friend
If a well formed response is not provided then each unit can be analyzed.
- Deictic center of quadranym: source_ self → target_other
Analyze script:
Script Terms from: quadranyms, the text, typical targets, the Polynym Matrix.
Below, notice that M-role subjects are the targets of the last unit that then become the new anchor of the new unit. C-roles go with attractive M-roles.
Term relations below are weighted i.e., C-roles stick to M-roles. Source and target relations are trajectories. Paradigmatic relations e.g., position = self.
First Unit:[Far(position_self_my) ⊇ Near(relation_friend_here)]
Second Unit:[Far(relation_there_from) ⊇ Near(object_friend_to)]
Third Unit:[Far(object_there_out) ⊇ Near(place_friend_in]
Forth Unit:[Far(place_there_lives) ⊇ Near(object_friend_stays)]
Every cycle closes the loop on more information. Reports are generated.
A blank sript can be reused based on other circumstances.
Distance Response (blank script):
First Unit:[Far(position) _ Near(relation)]
- IF position IS position THEN relation IS there OR here.
Second Unit:[Far(relation) _ Near(object)]
- IF position IS relation THEN object IS here AND there.
Third Unit:[Far(object) _ Near(place)]
- IF position IS object THEN place IS here OR there.
Forth Unit:[Far(place) _ Near(other)]
- IF position IS place THEN object IS here OR there.
The usefulness of scripts are their ability to be reused in different ways. The goal is the ability to use dynamical context units and scripts as a system of metaphorical analysis. Details are beyond the scope of this article.
The project includes a wiki, code page and knowledgebase. It aims to access word sense in dictionaries by using quadranym and polynyms to simulate the human ability to sense dynamic relations between words.
Every unit is a trajectory. The trajectory is about what is being and how that being may become. That is, each subjective state (being) has an objective state (becoming) to identify. Each is predicated by modes (e.g., distance: far & near). Each unit initiates from the actual state (superset subject) and is always predicated on the expansive mode category. This reflects a basic concept of the word-sensibility model; that what is actual is the dynamic being sensed. Logic is a form of reasoning used to reach a conclusion using the most accurate facts available. This is not how commonsense necessarily functions as it can manifest on social assumptions and no facts. The model aims to abstract how assumptions form. Commonsense reasoning does not always strictly adhere to logic as it sometimes aims to make points by generating and using rhetoric to form arguments. The Q explores how a system does this (i.e., form & share novel ideas). Topical interoperability is the ability to except or reject the orientations or conclusions of other systems.
- The signifier requires two different systems of context to be signified. Q analysis makes a distinction between the situational context (conditions in the world) and the dynamical context (the organisms responsiveness).
Important save (but a bit of a mess)
Script Description & Analysis:
Text: “My friend lives far from here.”
Premise: Distance → [Far(position) ⊇ Near(relation)].
Template & Color Key:
- Mode Sets: potential ⊇ actual (i.e., predicates of paradigm not text)
- State Sets: actual ⊇ potential (i.e., subjects of paradigm not text)
- The paradigm begins at the zero-point. This represents the actual-subject i.e., the umwelt as a deictic center. It is predicated on potential. It targets what is predicated on actual. Any target is the potential-subject of any situation.
Subject States: Find Ordered Pairs (<find>). M-Roles tract Text:
(Ordered pairs are found through frequency and Q Matrix. Notice that there are four different topics below. Pairs on left tract topic’s lists terms on right.)
Topics from table being used: {mine, direction, container, reside}. Textual terms are sorted into topic lists by pairs like, self_here → other_there.
- position <find> relation ⇒ (Being(mine): {possess_object_here_other})
- relation <find> object ⇒ (Being(direction): {from_to_far_person})
- object <find> place ⇒ (Acting(container): {out_in_there_lives})
- place <find> object ⇒ (Acting(reside): {move_stay_my_friend})
(Notice Being(x) and Acting(x), i.e., ER-Mode = Acting, OS-State = Being. You can think of being as the central self and acting as the process. They Identify the axes being used in the lists, e.g., Being(mine) = possess → object. Textual terms are root related. As we show next, nodes are related using 3 bi-directed edges.)
(Ranks & columns are two directions in which the list terms are related. Above relates ranks, below relates columns. Terms are targeted from both directions)
Q Relations: 3 Bidirectional (↔︎ ) Types:
- partOf: terms related by unity (i.e., belonging to an orientation).
- stepTo: terms related by sequence (i.e., initiate steps from orientation).
- typeFor: terms related by purpose (i.e., this is used for that).
- possess partOf from, from stepTo out, out partOf move.
- object partOf to, to stepTo in, in partOf stay.
- here partOf far, far stepTo there, there partOf my.
- other typeFor person, person stepTo lives, lives partOf friend.
(Terms stepTo (sequence) from partOf initial anchor to partOf final anchor. This represents different states of the terms. Below illustrates potential & actual Modes. The key to modes in scripts; potential is only active in the initial unit.)
Predicate Modes: Sense Assertions. Modes virtually oscillate in Script.
- Target M-role of previous unit becomes Source M-role of next unit.
First Unit:[Far(position_self_my) ⊇ Near(relation_other_object)]
- Potential_Far(x) asserts a continuous-out i.e., global.
- Actual_Near(x) asserts a discrete-situation i.e., local.
Second Unit:[Far(relation_there_from) ⊇ Near(object_friend_to)]
- Far is a continuous variant i.e., inclusive position.
- Near is a discrete invariant i.e., exclusive position.
Third Unit:[Far(object_there_out) ⊇ Near(place_here_in]
- How is textual subject “far_friend” when it is predicated on Near?
- Near & Far are paradoxically conflicted. Only near is quantifiable.
Forth Unit:[Far(place_there_lives) ⊇ Near(object_friend_stays)]
- Far(x) = Remote(x) when the preferred term* anchors the target.
- The target_friend anchored to source_place, completes script.
*(Best sense anchors unit e.g., place anchors object = discrete final unit.)
Conclusion: Far(relation) + Far(object) + Far(place) = Near(friend).
Script Interpretation:
Discrete = []
- IF:
[Far_relation] = [Far_friend]
THEN
[Far_place = Near_friend]
- Potential_Far = continuous variant i.e., unity (e.g., central).
- Actual_Near = discrete invariant i.e., individual (e.g., proximity).
- Production Units = discrete variants i.e., plurality (e.g., remoteness).
(Production units, procedural units or sub-anchors are different terms used to think about the same thing; units constrained by an initial orientational unit.)
Virtual orientation shared between speaker & hearer:
- Distance → [Remote(central) ⊇ Proximity(location)]
[ Actual Roles = central ⊇ proximity → Potential Roles = remote ⊇ location ]
Key Idea: The situational context (text) is like an independent variable that influences the dynamical context or, as it were, the dependent variable.
Review Gloss Utility Layer:
(The M-role source and target of each unit are settled by copulas below.)
1, Position: IF position IS position THEN relation IS here_self OR there_other.
- For First Unit: [Far(position) ⊇ Near(relation)] → xhere OR xthere.
2, Relation: IF position IS relation THEN object IS this_self AND that_other.
- For Second Unit: [Far(relation) ⊇ Near(object)] → xthis AND xthat.
3, Object: IF position IS object THEN place IS here_self OR there_other.
- For Third Unit: [Far(object) ⊇ Near(place)] → xhere OR xthere.
4, Place: IF position IS place THEN object IS there_other NOT here_self.
- For Forth Unit: [Far(place) ⊇ Near(object)] → xthere NOT xhere.
Each dimension holds its own categorical set. For example, open holds over, inclusive, infinite, out and all as some terms that unpack given the context. Consider the text, “Come in from the cold”. This is a spatial sense involving door and container as basic topical dynamics of contextual responsiveness.
Below, the topic name door quadranym is illustrated structured as a statistical model. All quadranyms follow the same structure for these kind of models. That is, modes define dependent and independent variables as shown, i.e., close = independent variable, open = dependent variable.
The example shows how quadranyms can structure a statistical model. In this case, a unit structures how doors might optimize in a climate context.
“Come in from the cold”
If door remains open desired temperature will be reduced. Because door is a subset of the spatial domain it is nested in the spatial dimensions. This will be illustrated later in a table of nested dimensions. The point being, the variability in climate is dependent on the division of spatial modes, in and out.
Q Metalexicography: Relations of Locations Paradigm.
Here we illustrate Q context concepts. This page will be as self contained as possible. This page offers a direct and basic breakdown of the model’s basic components. It is helpful to also read the About Page and the Model Page.
Below is a Q table. It represents a Polynym. Each topic name represents one of its dimensions (ranks). Polynym dimensions can be of any number.
This one is fashioned in five dimensions (i.e., pentanym).
5 parts × Relations of Locations = [space, door, distance, direction, container]
| space | infinite | finite | between | void |
| door | open | close | barrier | passage |
| distance | far | near | relation | position |
| direction | there | here | to | from |
| container | out | in | full | empty |
| time | future | past | event | present |
In the matrix above, notice how temporal & spatial realms align or nest together. The columns provide opportunity for dynamic relationships.
Quadranyms are pre-textual anchors making ‘motivated-dynamical contexts’ out of words before any textual influence. It then reshapes for the situation.
Q analysis is about how dynamic roles are equivocated to satisfy the task of describing a condition. Roles of a column can be connected using relations.
- Conceptnet types e.g., between isA barrier, open motivatedBygoal out.
- Also, relations can cross rank topic terms e.g., out obstructedBy barrier.
Q analysis is based on the Dynamical Context and not the Situational Context. The differences between contexts is reviewed in the next section.
- The signifier requires two different systems of context to be signified. Q analysis makes a distinction between the situational context (conditions in the world) and the dynamical context (the organisms responsiveness).
Next post, we elucidate on pervading sense facets, realm schematics, trajectories, and the theoretical cognitive processes that it all pivots on.
Site Summary: There are innumerable situations that humans would know and machines would not. The basic idea is for machines to have the ability to abstract, at word level, Word-Sensibility – that aims to effectually anchor contextual responsiveness. That is, virtual responses to the world used to anchor the dynamic sense of a text. In this way, a machine can make better sense of the things that people typically know. A conceptual model and propositions are proposed that could form the basis for further research.
Summary: The basic idea is for machines to have the ability to learn to abstract the human experience of contextual expectation, building that abstraction up from the word-level, and in this way machines can make better sense of the things that people typically know. People experience the world and computers don’t. Essentially, this is why commonsense is a challenge for computers. Computers can’t possibly know all the things that people experience and know. However, machines able to improve their contextual expectations can better learn from their mistakes. The goal is textual analysis to assess word sense by keying on human responsiveness. That is, the ability to act quickly and positively to situations in the world.
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