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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
  1. Overview
  2. Introduction
  3. Theory & Approach
  4. The Principle of the Orientation of Interactivity
  5. Word Sensibility
  6. Psyche & Eros
  7. Driven

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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):

  1. More separate region is -x. This could refer to solid or distance.
  2. In this context, it is solid (door). If x = 1, then y is potential for x = 1.
  3. Left of x is a single region. Right of x is multiple regions (adjacent).
  4. This is important because an x region can close in all the way to self.
  5. The question is… does whole region y increase in or out of region x = 1?
  6. The self has affordance of space based on the potential void in region.
  7. If y variance is inside x = 1 then y is potential space only for x = 1.
  8. If self x = 1 moves to the right to x = 2 then a new region is inputted.
  9. This means that the y potential now involves two actual regions.
  10. The new region is a new input and new viewpoint for a new cycle.
  11. Cycles are quadranym frames that stitch together to make scripts.

Affordance Parameters: topic space (above)

  1. x refers to multiple, separate, actual regions.
  2. y refers to a single, whole, potential region.
  3. Separation is solid not spatial in this context (door).
  4. The origin is the central point to both views of region.
  5. Left of x input refers to the separate region of the origin.
  6. All of y is the afforded amount of spatial potential for origin.
  7. If region y increases then it is either inside or outside x region.
  8. If x represents origin region then y potential in only for that region.
  9. If open door adds new region input then y potential spans both regions.
  10. This means that the y potential now includes two actual regions.
  11. Affordance potential connects. It is less separate because of access.
  12. The new region is a new input and new viewpoint for a new cycle.
  13. 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).”
James J. Gibson, 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.

As a public interface, quadranyms are best represented on a mode y and state x axes. They also configure for dependent & independent variables.

Slide2

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.

  1. Modes: E = potential ⊇ R = actual (a.k.a. predicates, functions & action y)
  2. States: S = actual ⊇ O = potential (a.k.a. subjects, arguments & being x)

There are various ways to unpack contextual functions from a quadranym.

  1. active-sense f(x) (function & argument) = Open(passage)
  2. 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.

door

(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]

  1. Far(x) is a potential dynamic that predicates continuing out i.e., globally.
  2. 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:

Slide2
Q-Unit
cart2-56a602235f9b58b7d0df6f58
Cartesian Coordinate System (C)

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

(note: we have been using <find> to mean, find ordered pairs i.e., an initial quadranym is chosen to begin a script and the procedure occurs again at each flux point i.e., virtual point between Q units. Just part of Q pseudo code at this point.)
Motivations are layered strategically into contextual timelines. A timeline is driven by a flux. For instance, the act of smashing can be driven and motivated simply by the dynamic sense of breaking something into fragments. This motivation may be constrained by other motivations like hunger. Animals like primates, birds and otters are known to all use rocks or hard surfaces to crack nuts or shells. What becomes understood is the concept of domain responsiveness. That is, how to respond in a domain. Theoretically, these strategies are expedited in social systems. Delineated domains are shared. This can begin in a domain general framework. Constraining systems can be rearranged into different strategies. General constraint strategies can be reused in different domains of responsiveness.

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.



Final Thoughts & Additional Notes
Related Ways of Thinking:
Q-Unit & Systematics
Notes: Consider the pattern and order of wholes as described by J.G. Bennett (1897-1974). By drawing upon the qualitative significance of number (Ed. 1993), Bennett illustrates a method in which the underlying patterns of things are sensed. That is, through a sense of numberness, a oneness, twoness, threeness and so on.
“The twelve systems are not scientific models but insights into degrees of organization. Everything ‘begins’ from simple wholeness and has the promise of progressing in depth to something significant for the greater whole.” (Systematics)