Active-Actual State

• The current, realized state of an entity or concept in the Q model.
• Serves as the definitive anchor for the system’s understanding.

Description:
Refers to the present, concrete understanding or orientation of a concept. It represents what is actively engaged with and realized in the system.

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Active-Passive Cycle

• A process of orientation in which active states are guided by reliable passive expectations, creating a cycle of contextual adaptation.
• This cycle allows the system to continually adjust orientations based on environmental input, facilitating dynamic, real-time responses.

Description:
In the Q model, an active-passive cycle is a continuous feedback loop where active states interact with passive, stable expectations. Passive states provide a reliable set of contextual cues, allowing the model to adjust orientations based on situational changes. As the model aligns its expectations with the current context, it generates a new orientation, which then continues the cycle. This process enables the system to construct scripts that evolve fluidly with shifting contexts, contributing to a dynamic, adaptive interaction with its environment.

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Agent Quadranym

• Refers to quadranyms focused on personal or interpersonal dynamics, involving elements like Positive, Negative, Goal, and Self.

Description:
The agent quadranym captures the dynamic relationships between an individual’s goals, actions, and sense of self, reflecting personal orientations in different contexts. This quadranym helps model how agents interact with their environments and others.

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Corollary-Like Discharge (CLD)

• Corollary Discharge is a term from neuroscience that refers to feedback loops within the brain, helping distinguish between self-generated and external stimuli.
• In the Q model, it serves as an analogy for feedback loops between systems.

Description:
Originally a neuroscience concept, corollary discharge involves the brain sending a copy of motor commands to predict the outcomes of movements. In the Q model, this concept is adapted: rather than sending an action copy, the Q model provides orientation, ensuring the system adjusts based on both internal and external inputs, and coupling the system’s orientation with the situation. This interaction contributes to a sense of self-regulation and alignment with real-world conditions.

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Definitive Point

• The reference or anchor for the active-actual state in a quadranym.
• Serves as the foundation for dynamic orientation within the system.

Description:
A key component of the Q model that anchors the system’s interpretation at a given moment. It represents the realized state upon which potential outcomes are assessed. The definitive point is a synergistic state with the environment that feels real to the agent, providing a sense of groundedness in real-time.

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Dynamic Quadranym Model (DQM)

• A model that dynamically shifts between potential and actual meanings based on context.
• Helps AI systems adapt in real time to evolving meanings and contexts.
• DQMs act as reference frames, ensuring adaptable responses to changing situations.

Description:
A proposed framework within the Q model that helps AI process meaning dynamically by shifting between actual (realized) and potential (unrealized) states of understanding. It fuses quadranyms with a regression-like model, bifurcating words between the X-Y axes and shifting along an axis depending on the moment of context. DQMs act as reference frames with one or more layers of DQMs, providing general (basic) and relevant (detailed) responses to changing situations.

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Dynamical Context

• Refers to the internal, private response system influenced by preexisting psychological and emotional expectations.
• Shapes the agent’s orientation toward a situation by adapting responses.

Description:
A self-regulating system that captures how agents orient themselves privately, in contrast to external situational changes, responding based on internal feedback and pre-existing mental models.

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Energy Quadranym

• Models a semantic orientation of energy using dimensions such as active passive, finite, matter, and motion.
• A quadranym focused on the interaction between engagement and behavior in energy dynamics.
• Used to model both physical and metaphorical energy interactions.

Description:
A specific type of quadranym that helps explain how energy (active or passive) operates within a dynamic system, whether in terms of motion, effort, or engagement.

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Feeling

• A virtual state in the Q model that contributes to emotion, meaning, and the emergence of understanding.
• Technically, feeling virtually describes the dynamic measuring along and between the X and the Y axes of a DQM unit.

Description: In the Q model, feeling is understood as a precursor to deeper cognitive states such as emotion and understanding. As a virtual state, feeling arises from interactions between units and reflects responsiveness within the context but does not independently yield understanding. Understanding is relational, emerging only through interactions with the environment (a B-to-A relationship), where system B provides a felt sense that guides system A’s responses. Feeling thus bridges the gap between mere sensation and structured meaning, shaping an adaptive response without resolving into concrete understanding on its own.

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General Quadranym

• Refers to broad categories that represent fundamental concepts, such as space, time, energy, and agent.
• These quadranyms are general because they apply across a wide range of contexts and form the foundational structure of meaning.

Description:
General quadranyms in the Q model capture overarching concepts like space, time, energy, and agent, which serve as the primary structure for processing meaning. They provide the basic framework for understanding context in a broad sense.

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Layer

• A structural level within the Q model that organizes orientations and interactions across varying timescales and hierarchical levels.

Description: Layers in the Q model represent distinct levels of temporal and hierarchical organization, allowing orientations and actions to function in parallel across different timescales and structural levels. Each layer captures a unique dimension of meaning or experience, with shorter intervals enabling immediate responsiveness and longer intervals supporting overarching adaptability. Hierarchically, layers align more specific actions with broader contextual goals. By nesting layers, the Q model enables a flexible yet cohesive system that adjusts both to situational contexts and to cumulative, patterned responses. Layers thus support dynamic adaptation, balancing short-term actions with longer-term structures and meanings.

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Nested System

• A hierarchical structure within the Q model that organizes layers of orientations, allowing for adaptive processing from general to specific.

Description:
In the Q model, a nested system layers quadranyms, or sequences of quadranyms called scripts, across various levels of detail. Each layer represents a distinct orientation, where higher layers encompass broader, unattended sense with fewer details, and lower layers focus on specific, attended sense. Nested systems are inherently adaptable, maintaining a general-to-relevant hierarchy that organizes information based on salience. This hierarchical nesting allows for efficient processing by directing attention only where needed. A nested system can often be summarized or represented by a single quadranym, especially when functioning at higher, more general levels.

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Nexus (from Whitehead’s philosophy)

• Represents the connection or interaction between actual entities in a system.
• In the Q model, refers to the semantic relationships between words or ideas.

Description:
The tangible connection or interaction between entities, words, or ideas within the Q model. Nexūs form the dynamic relationships that ground meaning.

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Orientation

• Refers to how an agent positions itself in a given context, adapting to changing circumstances.
• Reflects the dynamic relationship between the current state (actual) and future possibilities (potential).

Description:
The process by which the agent adjusts its understanding or response within a context, balancing the actual state with potential outcomes. Orientations happen simultaneously at different granularities as general and relevant layers in the system, allowing for adaptive responses.

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Passive-Potential State

• Represents unrealized possibilities or latent meaning within the system.
• Guides the Q model in anticipating potential outcomes.

Description:
The unrealized possibilities for a word, action, or idea, showing what it could become based on future interactions with past and occurrent contexts.

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Prehensions (from Whitehead’s philosophy)

• Describes how entities interact or perceive each other in relation to one another.
• In the Q model, this concept relates to how entities grasp and process meaning through context.

Description:
A philosophical term that refers to how entities “grasp” or connect to each other, used in the Q model to describe how meanings interact in a dynamic system.

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Q Model (Quadranym Model of Sensibility)

• A theoretical framework for dynamic cognition and meaning-making.
• Focuses on how entities (words, ideas, agents) are processed within a semantic context.
• Integrates feedback loops to mirror human cognition.

Description:
A theoretical semantic framework designed to explain how meaning is dynamically constructed and processed within both human cognition and AI systems. The Q model emphasizes orientation and responsiveness to dynamic contexts.

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Quadranym

• A four-dimensional unit representing expansive, reductive, subjective, and objective facets.
• Provides a structure for processing how words, actions, or ideas evolve across contexts.
• An orientational framework for targeting the understanding of/in a particular situation.

Description:
A fundamental unit in the Q model that reflects dynamic contextual shifts. Quadranyms help the system assess how entities interact by organizing them within four dimensions: expansive (E), reductive (R), subjective (S), and objective (O). It operates through two primary states—S (active/source) and O (passive/target)—with modes E and R guiding their evolution together.

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Reference Frame

• A general semantic perspective that provides contextual orientation for understanding language and events
• Supports AI in adapting to shifting meanings by anchoring interpretations to relevant situational contexts.

Description: Reference frames in the Q model act as broad, adaptable semantic lenses that help the system process language, events, and interactions. They offer a stable yet flexible structure, allowing the AI to align specific words or actions within an overarching context (e.g., professional, social). By linking these frames with quadranyms, reference frames enable the system to anchor understanding dynamically and adjust meaning as contexts evolve.

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Regression-Like Model (RLM)

• A model used for dynamically adapting based on historical data and current inputs.
• Adjusts relationships between words, contexts, and orientations dynamically.

Description:
A tool within the Q model that allows for real-time recalibration by adjusting relationships based on past experiences and new data inputs.

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Relevant Quadranym

• Refers to specific, contextually-grounded instances of general quadranyms, such as room (space), walk (energy), later (time), and we (agent).
• These quadranyms are relevant because they reflect specific applications of general concepts.

Description:
Relevant quadranyms represent more specific instances of general quadranyms, bringing context and detail into the broader framework. For example, room is a relevant quadranym within the space general quadranym, and walk relates to the general quadranym of action.

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Representation: Scripts link quadranym units in either direct or layered structures. When a subject influences an object, they remain within a single unit, shown with inward-facing brackets (e.g., [s>o][s > o][s>o]). Conversely, when an object influences a subject, the connection spans across different units, indicated as [o]→[s], such as [o]>[s][o] > [s][o]>[s].

Script Units:

[Y(a)→X(b)] <link> [Y(b)→X(c)] <link> [Y(c)→X(d)]…

Between-Unit Examples:

• [X(rock=b)] → [Y(tool=b)]
• [X(plant=b)] → [Y(food=b)]
• [X(noise=b)] → [Y(music=b)]

Each unit, represented by inward-facing brackets, shows the element “b” predicated differently (i.e., in a different mode). Here, X causes Y or Y responds to X, with each unit’s constraint, from which b emerges, defined by a.

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Self-Model 

• A model of self that tracks and adapts based on the outcomes of its interactions and orientations.
• Enables continuous self-assessment and learning, refining orientation based on feedback from the environment.

Description:

The self-model in the Q framework is responsive to the consequences of it’s own orientations i.e., it reflects a responsive system that not only orients within a context but also evaluates the consequences of its actions and interactions. This feedback-driven process allows the system to adjust future orientations based on past experiences, akin to learning through self-awareness. The self-model enhances adaptability by balancing internal (self-generated) perspectives with external (situational) influences, enabling it to fine-tune responses and improve alignment with contextual expectations over time.

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 Semantic Core

• The central mechanism in the Q model that processes language alongside perception and action.
• Continuously adapts through feedback from both situational and dynamical contexts.
• Drives the construction of relevant responses by interacting with contextual inputs.

Description: The semantic core represents the Q model’s approach to the semantic aspect of a broader hypothesized process known as the “Mechanism Common to Action, Perception, and Semantics” (MCAPS). While MCAPS is a hypothetical model of embodied language cognition describing neural processes shared across these domains, the semantic core within the Q model focuses specifically on integrating language through semantic structures that respond to context. By engaging in real-time, feedback-driven adaptations, the semantic core models context-sensitive meaning construction, dynamically adjusting to environmental inputs as part of the Q model’s semantic framework.

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Situational Context

• Represents the external, observable conditions in which an event occurs.
• Provides factual inputs that shape the agent’s external orientation.

Description:
The broader, objective context in which an event takes place, offering environmental cues that shape how agents act and respond in a given situation.

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Space Quadranym

• Models the dynamics of space using dimensions such as infinite, finite, void, and between.
• Helps in understanding how entities orient themselves spatially.
• An orientational framework for targeting the understanding of spatial perceptions/situations.

Description:
In the Q model, the spatial quadranym differentiates between actual and potential states and modes to represent spatial perception. The actual state is represented by void, establishing a baseline or constraint for spatial experience. In contrast, the potential state is represented by between, encompassing possibilities like objects, separation, or boundaries. The potential mode, represented by infinite, reflects expansive, boundless possibilities that frame spatial orientation but are never fully realized. The actual mode is captured by finite, defining measurable, immediate spatial dimensions. This structured interaction of states and modes allows the model to interpret space dynamically, integrating both tangible and abstract spatial elements.

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System A

• The input system responsible for capturing environmental stimuli and situational context.
• Provides external inputs to Systems B and C for further processing.

Description:
The system that gathers and processes external, observable data and situational inputs, offering them to other parts of the Q model for real-time adjustment. As a semantic system, it can be represented by a massive language model (MLM) like ChatGPT.

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System B

• Processes dynamical context and generates the definitive points within the system.
• Plays a key role in internal regulation and active-actual orientation.

Description:
Responsible for processing internal, private responses and generating the definitive points that anchor the system’s understanding and response to stimuli. This system can provide orientation at different levels of granularity for massive language models (MLMs), improving generalization and metaphoric understanding.

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System C

• Integrates emotional and cognitive dynamics, sending feedback to Systems A and B.
• Adjusts the system’s orientation dynamically based on emotional and cognitive factors.
• Receives self-identification opportunities by combining the inputs from Systems A and B.

Description:
The central processing unit within the Q model that oversees emotional and cognitive responses, adjusting how the system reacts in real-time. System C also plays a pivotal role in regulating feedback loops, refining orientation by integrating both situational inputs from System A and internal responses from System B. This combination provides self-identification opportunities, ensuring adaptable and nuanced behavior.

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Time Quadranym

• Models the temporal dynamics of time through four dimensions: future, past, event, and present.
• Provides a framework for understanding how time-based experiences anchor emotions and guide responses.

Description: In the Q model, the time quadranym organizes temporal perception by structuring interactions among future, past, event, and present. The active-actual state, present, serves as the immediate anchor for current experience. Event, the potential state, represents unfolding possibilities, capturing what might happen as time progresses. The potential mode, future, reflects the open-ended realm of what could be, while the actual mode, past, anchors memory and reference points, shaping how past experiences influence present and future understanding.

By integrating these temporal facets, the time quadranym dynamically aligns perception and orientation, creating a cohesive sense of time that informs both immediate reactions and longer-term interpretations.

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Unit

The smallest contextual element in the Q model, typically represented by a quadranym.

Description:
In the Q model, a unit refers to an individual quadranym that serves as the foundational element of context. Unlike in situational contexts, where a term alone can carry meaning, each term in the Q model belongs to a structured unit. These units are the primary building blocks in the model, integrating into larger systems of scripts and layers. A unit is both the initial link within any script and the fundamental starting point within any layered system, anchoring meaning through its relationships and interactions with other units.

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Unitization

Description: In the Q model, a unit refers to an individual quadranym that serves as the foundational element of context. Unlike in normal use, where a term alone can carry meaning, each term in the Q model belongs to a structured unit. These units are the primary building blocks in the model, integrating into larger systems of scripts and layers. A unit is both the initial link within any script and the fundamental starting point within any layered system, anchoring meaning through its relationships and interactions with other units.

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Unit of Understanding

• A fallacy in the Q model suggesting that understanding could be confined within a single, isolated unit. This fallacy underscores a core principle in the Q model: understanding is not a property of individual units but rather requires an adaptive network of units, nested systems, and environmental interactions.

Description:
In the Q model, the concept of a “unit of understanding” is considered a fallacy because understanding is fundamentally relational and context-sensitive. Unlike discrete elements like words or quadranyms, understanding in this model is virtual, not human; it arises through engagement with situational contexts or “environments,” synthesizing orientations across layers and contexts over time.

“Human understanding is not about disembodied rules or isolated representations. It is a situated practice that arises from the interaction between agents and their environments.” — Hubert Dreyfus

Each quadranym unit provides specific semantic or contextual orientations; however, virtual understanding itself requires coupling with situational context to gain relevance, as relational properties within the dynamical context hold significance only when grounded in the situational environment.

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Word Embedding

• A dense vector representation of words that enhances the Dynamic Quadranym Model (DQM) by capturing context-sensitive meanings essential for interpretive layering in System B.

Description:
In the Q model, word embeddings encode words as dynamic, multi-dimensional vectors, shaping how DQM units (quadranyms) bifurcate word meanings between their X and Y axes and then scale them along each axis. Unlike static embeddings in conventional NLP, these embeddings influence System B’s interpretive processes by dynamically organizing words across contextual dimensions. This layered structure allows word meanings to adapt responsively, informed by associative patterns from System A. By embedding words within DQM units, the model achieves nuanced, real-time adaptability, refining language understanding in response to evolving scenarios.

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Word-Level Concept

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Word-Sensibility

• The principle that words are sensitive to their context and meaning evolves dynamically.
• Emphasizes responsiveness rather than static meaning.

Description:
The idea that words don’t have fixed meanings but adjust their sense based on the surrounding context. In the Q model, word-sensibility drives how words adapt to dynamic changes.

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Zeropoint (Active-Actual State)

• The anchor for orientation from which potential outcomes are calculated.
• Provides stability to the system’s interpretation in real-time.

Description:
The source condition or definitive reference point within the Q model that stabilizes how entities orient themselves and assess future possibilities.

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