Section Five

Reference Frames: Viewpoints & Disambiguation

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

― James J. Gibson

Modes provide measure to balance relations between state conditions.

  • Modes: Homeostatic measure for responses.

Calibrate Word-Topics to Situational Context:

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

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

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

Dynamical Context Responds to Situational Context:

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

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

Viewpoint: Less whole region and more separate region illustrates the spatial context.
  • I will know as soon as I walk through the door.

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

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

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

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

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

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

Notes for Space Topic Graph: 

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

Script Example (basic):

Space topic blanks: (2 perspectives)

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

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

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

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

Contextual Timeline Layers: ( A brief description.)

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

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

Reference Frames Shape Contextual Viewpoints:

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

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

What Follows are Multiple Quadranym Subsets:

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

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

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

Potential time depends on actual time to be temporal event.

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

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

Notes for Time Topic Graph:

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

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

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

                  Viewpoint: Less observation and less information illustrates the mental context.

Observation depends on information to be knowable event.

  • Mental is constrained by the X1 spatial and temporal dynamics.

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

  • Increased observation to information is represented in time topic X2.

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

Notes for Mental Topic Graph:

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

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

Distance – Additional Layer Example:

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

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

Remote depends on proximity to be spatial relation separation.

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

Notes for Distance Topic Graph:

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

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

The Script’s System Summary:

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

Conative Script: between dynamics between knower and knowable.

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

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

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

Disambiguation: Reference Frames & Viewpoints

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

Example:

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

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

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

The truth may be that high level commonsense awareness like this requires human like experiences. Although machines are mostly incapable of that, they may be able to someday better address our intentions and viewpoints.The reference frames above anchor a responsiveness dependent on other reference frames.

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

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

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

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

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

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

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

Conative Script: exposure dynamics between cover and rain.

Umbrella Skeletal System:

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

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

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

Bathtub Skeletal System:

Conative Script: full dynamics between contain and water.

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

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

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

Homeostatic Measures for Source & Target Conditions:

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

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

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

System layers (Onion):

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

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

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

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

Target A:

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

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

Target B:

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

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

A likely scenario is derived between the two targets above.

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

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

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

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

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

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

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

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

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

From General to Relevant Layers:

  1.  T=Space: …………[ E=infinite | R=finite | O=between | S=void]
  2.  T=Climate: ….. [E=temporate | R=sever | O=area | S=condition]
  3.  T=Shelter:………………..[ E=out | R=in | O=exposure | S=protect]
  4.  Umbrella:  ……………….[ E=out | R=in | O=rain | S=cover]
  5.  Tub:……………………..[ E=out | R=in | O=water | S=contain]
  6. T=Agent:…………….[ E=active | R=passive | O=goal | S=self]
  7. T= Maintenance: [ E=preserve | R=neglect | O=object | S=keep]
A possible contextual onion (ecosystem). Each layer provides being-becoming cycles.

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

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

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

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

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

Model

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