In our exploration of cognitive semantic frameworks, we highlight the significance of harnessing situatedness and its insights and applications for NLP. This approach plays a crucial role in elucidating the complexities of human cognition and communication. The Q model emerges as a sophisticated representation of how human experience and understanding evolve.

1. The Essence of the Q Model

The Q model serves as a dynamic framework that emphasizes how actual and potential states interact. Central to this model is the notion that actual states—the experiences we recognize—are not fixed entities but rather fluid representations influenced by our perceptions, memories, and intentions.

Actual and Potential States

• Actual States: These represent our current subjective experiences, akin to the scenes we imagine in our minds.
• Potential States: These are the capacities for change, representing the possibilities that exist before we act.

The fluidity of these states aligns with process philosophy, where the focus is on the dynamic and evolving nature of existence.

2. Nested Systems in the Q Model

The Q model is structured around nested systems, where each layer offers a unique perspective on a given situation. These layers allow us to analyze our interactions from multiple angles, enhancing our understanding of context, intention, and action.

Layers and Quadranyms Each layer in the nested system corresponds to a quadranym—a structured unit that organizes dimensions such as agent, space, direction, and energy. By assigning specific modes and states to each layer, we can create a more comprehensive representation of our experiences.

Basic (Prime) Quadranym Examples:

Adaptive layers refers to the dynamic hierarchical structure.

Adaptive Layers	Head Word	Mode (Y) Facet	Mode (X) Facet	State (X,Y) Facet	State (0) Facet
Hierarchy	Topic	Expansive	Reductive	Objective	Subjective
Prime Q 1	Space	Infinite	Finite	Between	Void
Prime Q 2	Time	Future	Past	Event	Present
Prime Q 3	Distance	Far	Near	Relation	Position
Prime Q 4	Energy	Active	Passive	Matter	Motion
Prime Q 5	Agent	Positive	Negative	Goal	Self
Prime Q 6	Container	Out	In	Full	Empty
Prime Q 7	Door	Open	Close	Barrier	Passage

Quadranyms for Spatial-Temporal Orientation

To facilitate our understanding of this framework, we present a set of quadranyms organized into a hierarchical structure that delineates general and relevant layers. Each layer captures specific dimensions of spatial-temporal orientation, with the relationships between layers illuminating the semantic dynamics at play.

This nested system delineates between general (source) and relevant (target) layers, illustrating how specific words from the sentence “Let’s move the couch over there” are pulled and arranged according to each layer’s focus:

RF ∞……[Potential(actual) ➝ Actual(potential)] = source

Space……..[Infinite(void) ➝ Finite(between)] = source

Time…………[Future(present) ➝ Past(event)] = source

Distance..[There(position) ➝ Here(relation)] = source

Energy….[Active(motion) ➝ Passive(matter)] = source

Agent……[Positive(self)  ➝ Negative(goal)] = source

Agent…..[Positive(Let’s) ➝ Negative(move)] = target w/text

Time……..[Future(the_couch) ➝ Past(move)] = target w/text

Distance..[There(move) ➝ Here(over_there)] = target w/text

Energy..[Active(move) ➝ Passive(the_couch)] = target w/text

Notice that the source structures facilitate the target structures.

In this framework, the source structures act as the initiating or foundational components that influence or guide the target structures. The source-target relationship is dynamic: source structures (like intentions, contexts, or initial states) create conditions or affordances that enable the development of the target structures (such as actions, outcomes, or resultant states).

In the Q model, this means:

1. Source structures: These are often the initiating cognitive or environmental factors. For example, in the case of moving a couch, the initial decision or intention to move is the source structure that sets the process in motion.

2. Target structures: These represent the specific actions, results, or actualized states that emerge from engaging with the source structures. Continuing the moving example, the physical act of moving the couch to a new location would be the target structure.

In this nested system, general states (potential conditions or capacities) create the potential for action but depend on the relevant states (current, actual conditions) to manifest into the specific outcomes. This facilitation suggests that the source provides the necessary framework, resources, or context for the target structure to emerge.

In the source-target dynamic, general states represent broad, open-ended possibilities or orientations, while relevant states narrow these down to specific, context-driven actions. The source typically begins in a general state (e.g., contemplating a decision), and as the situation progresses, the focus shifts to a relevant state (e.g., deciding to move a couch). This transition from general to relevant reflects how potential states become actualized. In nested systems, general states guide multiple potential outcomes, while relevant states activate specific, immediate responses tied to the task at hand.

Here is a breakdown of these dynamic factors:

The semantic relationships between layers and the arrangement of words in the sentence reveal intriguing dynamics:

1. Hierarchical Interaction: Each layer interacts with words based on its specific context and function, with “move” serving as a core action that resonates throughout each layer.
2. Layer-Specific Roles:
   • General (Source) Layer: In this layer, “move” exists as a potential action that sets the stage for its specific manifestations.
   • Relevant (Target) Layer: In the relevant layers, “move” takes on a concrete role, indicating agency and intention in a specified context—moving the couch a certain distance over time.
3. Dynamic Meaning: The state of “move” shifts depending on its association with each layer, illustrating the semantic flexibility of language:
   • In the Energy layer, “move” is active and signifies engagement, implying energy behind the action.
   • In the Distance layer, “move” relates directly to spatial transition, establishing how far or close something is concerning the agent’s intention.
4. Contextual Shifts: The shift from general to relevant layers emphasizes the contextual contingency of meanings. The verb “move” gains nuances based on surrounding concepts:
   • Space: Represents a change in position, with “there” as the destination.
   • Time: Relates to the temporal flow, indicating a transition between moments.
5. Word Arrangement and Function: The sentence structure emphasizes how “move” acts as a central axis around which other concepts (agent, object, location) revolve, showcasing how the action integrates with various aspects of cognition.

3. Introducing Nested Scripts Theory

Nested Scripts Theory builds on the foundational principles of the Q model, focusing on how we link experiences together in a coherent narrative. Scripts serve as sequences that connect actions and intentions, providing a framework for understanding how we navigate our environments.

Script Representation Each script consists of units that can be represented in various formats, allowing for flexibility in how we interpret interactions. The script not only illustrates the flow of actions but also captures the dynamic relationships between agents and their environments.

Script Units For example, consider the simple action of moving a couch. This can be broken down into several key script units:

Deciding to Move:

• Y(We)→X(Decide): We(Agent) > Decide(Object)

Executing the Move:

• Y(Decide)→X(Move): Decide(Agent) > Move(Object)

Identifying the Object:

• Y(Move)→X(Couch): Move(Agent) > Couch(Object)

Determining the Destination:

• Y(Couch)→X(Over_There): Couch(Agent) > Over_There(Object)

Scripts Evolve

Here’s a breakdown of how the script illustrates this evolutionary process:

1. Deciding to Move

• Script Unit: Y(We)→X(Decide)
• Representation: We(Agent) > Decide(Object)

This unit captures the initial intention or decision-making phase. The agent (we) recognizes the need to act and decides to move the couch. This step lays the groundwork for subsequent actions, illustrating the cognitive evolution from thought to intention.

2. Executing the Move

• Script Unit: Y(Decide)→X(Move)
• Representation: Decide(Agent) > Move(Object)

Here, the decision is transformed into action. The agent moves from a state of deliberation (deciding) to initiating movement (moving). This represents an evolution from a mental state to a physical action, demonstrating how decisions lead to tangible outcomes.

3. Identifying the Object

• Script Unit: Y(Move)→X(Couch)
• Representation: Move(Agent) > Couch(Object)

In this unit, the focus shifts to the specific object of the action. The agent identifies the couch as the target of movement. This stage shows an evolution in specificity; the agent refines their focus from the general action of moving to the particular object involved.

4. Determining the Destination

• Script Unit: Y(Couch)→X(Over_There)
• Representation: Couch(Agent) > Over_There(Object)

Finally, the script unit emphasizes the destination of the move. The agent not only knows what to move but also where to move it. This final step in the script showcases the culmination of the evolutionary process, where intentions and actions align to achieve a specific goal.

Overall Evolutionary Framework

The entire sequence reflects an evolutionary journey, where each unit builds upon the previous one, moving from a general idea (deciding to move) to a specific action (moving the couch to a designated spot). This illustrates the interconnectedness of thought, intention, action, and outcome within the context of a single task.

In summary, the example demonstrates how scripts can effectively capture the evolutionary nature of actions and decisions, highlighting the cognitive and physical dynamics involved in everyday tasks.

Script Conditions Template

The interactions can be represented through a Script Conditions Template:

Y(Source)→X(Target) <> Y(Source)→X(Target) <> Y(Source)→X(Target)

This template illustrates how a source action can lead to multiple target actions, underscoring the adaptability of scripts.

Here’s another script format example:

Script Units:

• Unit 1:
  • Agent: Positive(We)
  • Object: Negative(Decide)
  • Script: We(agent) > Decide(object)
• Unit 2:
  • Agent: Positive(We)
  • Object: Negative(To go)
  • Script: We(agent) > To go(object)
• Unit 3:
  • Agent: Positive(To the park)
  • Object: Negative(Now)
  • Script: To the park(agent) > Now(object)

Combining these units into a full script:

Full Script: Positive(We) > Negative(Decide) → Positive(We) > Negative(To go) → Positive(To the park) > Negative(Now)

Explanation

Each unit is expressed using the format, where “Positive” and “Negative” are used to show complementary opposites within the context of the action. This structure highlights the dynamic interplay between the agent and the object, emphasizing the necessary relationship between their states.

Overall Relationship

This sequence reflects how agents interact with their environments, emphasizing the shift between potential and actual states. By recognizing the dynamic interplay of subjective and objective experiences, we see how scripts are not just linear narratives but adaptable frameworks for understanding our actions.

4. Theoretical Foundations: Kant and Process Philosophy

The Q model, particularly in its articulation of scripts and states, resonates with key ideas from Kant’s transcendental philosophy and process philosophy.

Kant’s Influence: Kant emphasizes the interplay between perception and understanding, suggesting that our experiences are shaped by the structures of our cognition. This dynamic aligns well with the Q model’s focus on the relationship between actual and potential states.

Process Philosophy: This philosophical approach underscores the importance of change and evolution, a core tenet of the Q model. By viewing experiences as processes rather than static entities, we can better appreciate the fluidity of our interactions and the complexities of our understanding.

5. Metaphor in Nested Systems

Metaphorical Thinking and Cognition
Metaphors enable us to draw connections between disparate concepts, allowing for richer interpretations of experiences. By framing one idea in terms of another, metaphors can transform our perception of reality and influence how we engage with our environment. This is particularly significant in the Q model, where the interplay of actual and potential states is paramount.

Metaphors as Nested Systems
Just as the Q model illustrates the nested nature of systems, metaphors can be viewed as layers of understanding that enrich our cognitive frameworks. For example, using a spatial metaphor like “navigating through ideas” can help us conceptualize thought processes as journeys, adding a dynamic quality to our understanding of cognition.

Illustrating Metaphors in the Q Model
The Q model’s nested systems adds another dimension to our understanding of cognition and communication. By examining how metaphors operate within scripts and the broader framework, we gain deeper insights into the complexities of human experience.

Understanding how metaphors apply to the Q model allows for the representation of complex relationships and interactions. By analyzing how metaphors function within scripts, we can uncover underlying structures that guide our reasoning and decision-making.

Actual-Potential States Nested Chart:

To further clarify these concepts, here is a nested chart that organizes nested dimensions ing the Q model:

Nested Dimensions	Agent	Space	Direction	Energy
Actual State	Who is acting?	Where is the action occurring?	What direction is the action taking?	What energy is being utilized?
Potential State	What capacities does the agent possess?	What possible spaces can be explored?	What directions can be taken?	What energies can be harnessed?

Quadranym Matix

There are different ways to use quadranyms. Here’s an example of a quadranym matrix based on the principles of the Q model. The quadranym consists of four key dimensions: Mode, State, Subject, and Object, each linked to potential and actual states in a nested system. This structure highlights how interactions occur across different layers of meaning and action.

Integrating the matrix format as a supplement to the original chart format could provide a more versatile way to utilize the relationships within the Q model framework. The matrix format can highlight connections between different layers and states, making it easier to see how quadranyms evolve and interact.

Quadranym Matrix Example

Dimension	Active-Potential	Active-Actual	Passive-Actual	Passive-Potential
Mode	Thinking	Deciding	Knowing	Imagining
State	Open	Engaged	Stabilized	Dynamic
Subject	Agent	Self	Object	Environment
Object	Goal	Action	Outcome	Possibility

Breakdown of the Quadranym Matrix:

1. Mode (State of Action/Being)

   • Active-Potential (Thinking): Represents a mode where the agent is in a process of generating possibilities or planning.
   • Active-Actual (Deciding): This is when the agent has reached a decision or conclusion after thinking.
   • Passive-Actual (Knowing): The agent now has certainty and a stable understanding.
   • Passive-Potential (Imagining): While the agent is not currently acting, there is a reservoir of imaginative potential that can be drawn upon.

2. State (Condition or Quality of Interaction)

   • Active-Potential (Open): The state is fluid, allowing for exploration and engagement with new information.
   • Active-Actual (Engaged): A more focused and involved state where the agent is actively participating.
   • Passive-Actual (Stabilized): The state is more settled and balanced, signifying an outcome or completion.
   • Passive-Potential (Dynamic): While not yet acted upon, there is inherent flexibility and readiness to change.

3. Subject (The Agent/Entity Involved)

   • Active-Potential (Agent): The subject or agent is taking control of the situation and is capable of action.
   • Active-Actual (Self): The subject is now in a more reflexive role, considering their own actions and decisions.
   • Passive-Actual (Object): The subject is now observing the outcomes of their actions, identifying with the object.
   • Passive-Potential (Environment): The subject is aware of external conditions and possibilities that may affect future actions.

4. Object (Goal or Target of Action)

   • Active-Potential (Goal): The object or target is still in the planning phase and not yet acted upon.
   • Active-Actual (Action): The object is now being acted upon, turning the potential into actual behavior.
   • Passive-Actual (Outcome): The action has resulted in a specific, observable outcome.
   • Passive-Potential (Possibility): The object now holds future potential for new actions or opportunities.

Example Script with Quadranym:

Imagine a scenario of writing a book:

1. Active-Potential (Thinking) -> Active-Actual (Deciding):

   • You think about writing the book (Thinking).
   • You decide to start the book (Deciding).

2. Passive-Actual (Knowing) -> Passive-Potential (Imagining):

   • You now understand the story direction (Knowing).
   • You imagine new possibilities for plot development (Imagining).

This kind of quadranym matrix helps outline how an action progresses from an open potential state (thinking, planning) to an actualized state (decision, knowing), and from an internal action (self, agent) to its external effects (object, environment).

Fallibility and the Definitve point:

The discussion on how the definitive point in the Q model can get it wrong revolves around the fact that the active-actual state (or definitive point) captures what “feels” real to the agent at any given moment. This state provides a strong sense of reality, even if it might be based on incomplete or inaccurate information. Here are some key points from that discussion:

1. Subjective Reality: The definitive point represents the active-actual state in the dynamical context, which aligns with the agent’s sense of reality. This state may reflect something that feels real to the agent, but it doesn’t necessarily match objective reality. The agent might misjudge or misinterpret situations based on preexisting expectations or emotional biases.

2. Misjudgment and Misremembering: The definitive point allows for the possibility of human-like errors, where an agent might misremember events or make incorrect assumptions about a situation. These errors occur because the definitive point is based on the dynamical context, which includes subjective interpretations and not purely factual information.

3. Interpersonal and Social Dynamics: The potential for misalignment between the definitive point and objective reality plays a role in interpersonal orientations and social dynamics, such as in politics, religion, or science. People often orient themselves toward shared objectives based on a combination of subjective realities, which can lead to collective misunderstandings or biases.

4. Positive Aspect of Error: While the definitive point can lead to incorrect assumptions, this mechanism also allows for the potential to unite individuals or systems toward shared objectives. The flexibility of the definitive point can encourage exploration and collaboration, even when the agent’s initial sense of reality is flawed.

This flexibility allows the Q model to simulate how real agents (human or AI) engage with their environments, sometimes erring but also dynamically adjusting in response to new information.

6. Conclusion

The Q model’s nested systems, combined with Nested Scripts Theory, provide a robust framework for analyzing human experience. By breaking down actions into script units and understanding the relationships between states, we can gain deeper insights into how we navigate our environments and construct our realities.

As we continue to refine the Q model, it becomes increasingly clear that cognition is not about static concepts, but rather dynamic processes that actively shape our experiences. The overlap between semantic categories and sensory-motor areas suggests that neurons may utilize a shared mechanism for processing action, perception, and meaning. By modeling the semantic aspects of this mechanism and distinguishing between actual states (definitive experiences) and potential states (capacities for change) across nested levels, we gain deeper insights into how agents interact with their environments, recall, and even imagine new possibilities.

By Dane Scalise

Summary assisted by ChatGPT