Orientation precedes meaning. PD (novelty) presses; ND (coherence) holds. A gate condition—ND@S≥PD@O—governs when arcs admit potential, close, perish, and egress as superjective lineage. DQM instruments Whitehead’s creativity/concrescence/satisfaction/superject so we can diagnose, preserve, and reuse meaning as managed illumination.
The Unknowable Engine: Blind Spots as Foundational Features of Situated Cognition argues that intelligence does not rest on perfect knowledge but on structural limitations that guarantee perpetual motion. The Dynamic Quadranym Model (DQM) identifies two blind spots of orientation: the epistemological blind spot, rooted in the opacity of perception, and the procedural blind spot, rooted in the opacity of action. These blind spots are not errors but essential design features: they ensure that coherence is never final and that reorientation is always necessary. Distinguishing them from perturbations—situational disruptions that launch new arcs—the essay shows how blind spots are permanent constraints that make process thinking indispensable. Intelligence, in this view, is not the elimination of uncertainty but the art of navigating it, turning the opacity of being into an engine of change.
Orientation Beyond Language and Music: Introducing the DQM’s Semantic Core” explores how the Dynamic Quadranym Model (DQM) offers a groundbreaking framework for understanding meaning not as fixed content, but as emergent coherence shaped by embodied orientation across systems. Drawing from evolutionary musicology, cognitive science, and AI, the article traces how thinkers like Gary Tomlinson and Elan Barenholtz set the stage for the DQM’s central innovation: the Semantic Core—a procedural engine that tracks, relates, and resolves tensions across linguistic, perceptual, and motor systems. With detailed examples, including the “door” quadranym, and a Q&A that addresses the limitations of traditional models, this piece provides both a conceptual foundation and a practical lens for rethinking orientation, coherence, and intelligent behavior—human or artificial.
Dual bifurcation not only allows for independent shifts between semantic polarities but also situates an orientation within its context. By enabling two related yet distinct poles to interact dynamically, it maintains both stability and adaptability in the orientation process. Unlike linear bifurcation, which tracks simple, one-dimensional relationships (e.g., more light = less dark), dual bifurcation allows an orientation to emerge from the interaction of two independently adjusting poles, each rooted in a different perspective—expansive (e.g., ambient light), reductive (e.g., dark contrast). This dynamic interplay ensures that the orientation is always situated to the input context.
The DQM emphasizes that meaning evolves through process. It is not static but shifts in response to context, interaction, and orientation. This evolution is where the model focuses—not on meaning as a fixed entity but on the orientation that enables it to change.
Introduction: The Dynamic Quadranym Model (DQM) provides a semantic framework for analyzing situational and orientational dynamics. It captures transitions through Hyper Q layers and resolves contextual arcs within Q Units. Bifurcation and conflation serve as core mechanisms, adjusting expansive (potential) and reductive (actual) measures to … Continue reading DQM: Framework Overview
By distinguishing between the dynamic context (external, situational) and the dynamical context (internal, orientation-driven), orientation grammar captures the interplay between meaning and process. This distinction ensures a coherent framework for analyzing how orientation aligns with potential while responding to situational goals and movements. Through this lens, narratives like Jan’s dive reveal how orientation and situation dynamically coalesce to create meaning.
Imagine a race car speeding down the track, its engine roaring as it navigates sharp turns and accelerates on straightaways. The race car—sleek, agile, and immensely powerful—represents the cutting-edge Large Language Model (LLM), while the carefully engineered track, with its twists, turns, and guardrails, symbolizes the Classic AI (CAI) system that provides the structure and direction of the Dynamic Quadranym Model (DQM). Together, they form a hybrid system that blends raw computational power with refined, rule-based guidance, creating a dynamic interplay that ensures both speed and stability.
The DQM targets understanding as an evolving process, coupling the agent’s orientation with the situation to produce dynamic-semantic-responses. By organizing semantic elements into quadranyms—four-faceted structures—the DQM bridges contextual analysis with adaptive reasoning. It integrates foundational concepts with AI systems such as Large Language Models (LLMs) and Classic AI (CAI).
The Quadranym Model uses quadranyms as building blocks to establish semantic orientation at every instance and level of its system. At first, it might seem like there are too many blocks to manage, but the key is understanding how they naturally stack and fit together.
Introduction The Dynamic Quadranym Model (DQM) is a framework for analyzing and generating meaning dynamically. It bridges contextual analysis and adaptive reasoning by organizing semantic elements into quadranyms—four-faceted structures that guide understanding across layers. This guide will introduce the model step-by-step, from foundational concepts to … Continue reading DQM Summary
Summary of original article: The Dynamic Quadranym Model (DQM): Integrating Semantic Structure and Responsiveness for a Situating AI Introduction Language is alive, shifting with context and intention. Yet, AI often treats meaning as static—a pattern to retrieve rather than a process to adapt. Imagine a … Continue reading DQM Summary: Integrating Semantic Structure and Responsiveness for a Situated AI
Imagine sitting on your couch, remote control in hand. You press a button to change the channel, adjust the volume, or browse streaming options. The remote, a simple and familiar tool, embodies an intricate interplay of meaning and action. It is proximal—immediate and tangible—while the … Continue reading From Remote to Rhythm: The Dynamic Symphony of Meaning-Making
Introduction In cognitive science, Daniel Kahneman and Amos Tversky revolutionized our understanding of human decision-making with their dual-system framework—System 1 (fast, intuitive) and System 2 (slow, analytical). This dual-system model has become a cornerstone in both psychology and artificial intelligence (AI), offering a structured way … Continue reading From System 1 & 2 to Adaptive Intelligence: Extending Kahneman and Tversky’s Insights
Introduction: The Child and the Doll Imagination is often simple to observe in daily life. Think of a child playing with a doll. There’s no deep or mysterious process happening—the child knows the doll isn’t real, but they still interact with it as though it … Continue reading How Orientation Supports Imagination
We invite you to peruse the index as a quick and easy way to familiarize yourself with the basic aspects of the Q model. It offers a concise overview of key concepts, helping you grasp the fundamental semantic structure and ideas that drive the model’s dynamic approach to cognition and meaning-making.
In the Q model, we think of every word as representing a discrete dynamical system—each word actively drawing meaning from the environment while simultaneously contributing back to it. This dynamic interplay creates an ecology of dynamical systems. In essence, language operates like a network of … Continue reading Dynamical Context, Orientation, and Corollary Discharge in the Q Model
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A Theoretical Look at the Role of Words for AI
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This site explores “word-sensibility,” highlighting how machines can improve their understanding of human experiences by emulating the swift and effective responses people have to real-world situations. Such responsiveness shapes their comprehension of words and concepts. The model introduces a framework featuring active-actual states (subjects using energy) and passive-potential states (utilized energy or resources), underscoring the necessity for machines to replicate this human adaptability to enhance their language processing capabilities.
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