Tag: artificial-intelligence

Q Model: Index of Key Terms

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.

Dynamical Context, Orientation, and Corollary Discharge in the Q Model

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

Understanding the Q Model’s Nested Systems: A Journey Through Scripts

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 … Continue reading Understanding the Q Model’s Nested Systems: A Journey Through Scripts

The Q Model: Triadic Semantic Architecture

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A Theoretical Look at the Role of Words for AI

A system summary.

In this framework, we conceptualize the system as analogous to a nervous system, operating through natural language processing. The connections between systems function like neural pathways, facilitating the seamless flow of information and insights. This interconnected structure enables a nuanced understanding of user input, as each system plays a role in interpreting and responding to situational contexts. Serving as the central processor, and guided by Minsky’s six-layer model (The Emotion Machine, 2006), the framework integrates emotional and cognitive dynamics with the situational and dynamical contexts of the Q model.

Q Model: About

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A Theoretical Look at the Role of Words for AI

A summary of the article: About

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.