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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