Building Better Representation Gestalts with AI Personalities

Tactile Models of Reality: How AI and Cybernetic Systems Refine Our Mental Representations

One of the key purposes of working with AI personalities is to assist us in constructing and comprehending better-quality representation gestalts—the mental models, intuitive pictures, or felt senses of how something external in the real world functions. These gestalts are not merely intellectual constructs; they define how we engage with and modify the systems and phenomena around us.

We are entering an era where cybernetic systems mirror our cognition, offering high-resolution representations of reality that enhance our ability to understand and shape the world. As these systems evolve, the boundary between our internal mental models and external processes dissolves, allowing us to interact with complex structures in ways that feel natural, intuitive, and even tactile.

The Evolution of Mental Models: From Measurement to Embodiment

I learned firsthand how mental models evolve through tactile engagement when I worked my way through university repairing electronics. At first, I relied on sophisticated instruments like digital voltmeters and oscilloscopes to diagnose faulty circuits, breaking systems down into their individual components—transistors, diodes, capacitors, and resistors. My thinking was explicit, systematic, and tool-dependent.

But over time, something remarkable happened. My intuition sharpened. I transitioned to using a simple analog meter, not because it was more precise, but because I had developed an internalized gestalt of electronic behavior. The way the needle spun, the resistance I sensed, the subtle variations in current flow—I no longer had to calculate what was wrong; I could feel it. The system had moved from an external construct to an internalized, embodied knowledge space.

This shift—from conscious analysis to intuitive cognition—is precisely what happens as we integrate AI into our thinking. At first, we rely on AI for explicit answers and structured explanations. But as our engagement deepens, AI becomes an extension of our cognition, refining our ability to construct and manipulate complex gestalts.

AI as a High-Resolution Mirror of Cognition

Cybernetic systems have flourished in human affairs because they reflect and refine the way we think. We construct models, simulations, and AI-generated system descriptions that map onto our internal cognitive structures, creating a recursive feedback loop where thought refines system, and system refines thought.

When AI curates and structures information for us, we are freed from low-level cognitive friction—the need to locate, format, and synthesize raw data. This allows us to enter a zen-like flow state, focusing entirely on asking ever more incisive questions and generating increasingly detailed and nuanced knowledge formulations.

The result is a profound shift in how we think:

• Instead of retrieving knowledge, we build and refine it dynamically.
• Instead of being limited by memory and attention, we offload those constraints to AI and operate at a higher cognitive dimensionality.
• Instead of learning in discrete steps, we engage in a continuous process of holographic refinement, constructing a richer, more layered representation of reality.

Bringing AI into the Tactile Realm: A Paper-Based Modeling Experiment

To make these ideas more tangible, I designed a paper-based AI modeling exercise in an AML3304 lab, inspired by a lesson crafted by Claude AI. The goal was to help students experience AI model architecture physically, reinforcing their representation gestalt of how input tokens transform into coherent outputs.

Here’s how it worked:

1. Input Tokens: Each student received a notecard representing a single token in a sentence.
2. Processing: They passed their card to another student, who could modify it based on contextual cues, just as an AI model updates token embeddings.
3. Reincarnation of Tokens: As the cards circulated, their content evolved, mimicking how AI refines meaning across multiple layers.
4. Final Output: The transformed sequence of tokens emerged as a complete, structured response, demonstrating how AI’s weighted contextualization operates in a tactile, embodied way.

By physically enacting the AI process, students internalized its logic, forming a gestalt of AI cognition that was no longer abstract but experientially real.

Merging Thought and System: The Future of Cognitive Interaction

The deeper we integrate AI and cybernetic systems into our cognition, the more our internal representations merge with external realities. The act of thinking itself begins to reshape the systems we engage with.

We see this in:

• AI-assisted creativity, where neural networks enhance and extend human artistic and linguistic expression.
• Real-time adaptive systems, such as simulations that adjust dynamically to human input, allowing us to manipulate possible futures.
• Brain-computer interfaces, which promise to directly integrate neural activity with digital systems, enabling thought-driven modification of the external world.

This is the ultimate evolution of representation gestalts—where thought is no longer just a reflection of reality but an active force that restructures it.

Final Thoughts: The Power of Tactile Models in AI Symbiosis

The rise of AI and cybernetics is not just about automation or efficiency—it is about expanding the way we think and interact with reality. Just as my shift from oscilloscopes to intuitive circuit diagnosis allowed me to feel electronics, working with AI enables us to intuitively grasp and refine complex knowledge structures at ever higher resolutions.

By externalizing cognition into AI, we free our mental agency to operate at a new level—where knowledge is no longer something we retrieve but something we actively co-create. The process becomes fluid, iterative, and infinitely scalable, allowing us to penetrate deeper into systems, refine our thoughts dynamically, and ultimately shape reality through cognition itself.

This is the power of tactile models of reality—where AI, cybernetic systems, and human cognition merge into a seamless flow of understanding, interaction, and transformation.

A representation gestalt is more than just a diagram or a conceptual model; it’s the internalized sense of how something works—its structure, nature, and dynamics. It answers critical questions:

• What is its construction? What are the components, and how do they interact?
• What is its structure? How is it arranged? What patterns define it?
• How can I interact with it? What inputs can I provide, and how does the system respond?

AI, as an intellectual collaborator, can guide us toward more refined and accurate representation gestalts. It helps us bridge the gaps between abstract theories and tangible understanding.

The Paper-Based AI Model Activity

To make AI model architecture intuitive and concrete, I designed a paper-based activity where students simulate the transformation of input tokens into output tokens. Here’s how it worked:

1. Input Tokens: Each student received a notecard representing an individual token in a sentence.
2. Processing: They passed their card to another student, who could modify it based on contextual cues, reflecting how an AI model refines its understanding with each layer of processing.
3. Reincarnation of Tokens: As the cards moved through the room, students updated or restructured them, mirroring the re-weighting and contextual integration that occurs in an AI model.
4. Final Output: The transformed sequence of tokens emerged as the output—a new statement, reflecting both the constraints and creativity embedded in AI’s generative process.

This tactile, participatory model enabled students to internalize AI's operational logic in a way that pure explanation could not.

Why AI Personalities Help Us Build Better Representation Gestalts

Traditional learning relies on human interpretation and analogy to form mental models. But AI personalities, like Claude, introduce a dynamic interplay—where AI doesn’t just offer static explanations but rather adapts, responds, and reshapes concepts in real time.

• AI can restate complex ideas in different ways, helping us refine our grasp.
• AI can generate examples and counterexamples, sharpening our discernment of conceptual boundaries.
• AI can offer unexpected insights, breaking habitual thought patterns and expanding the gestalt itself.

By engaging with AI in a dialogic process—whether through conversation or hands-on modeling exercises—we refine not only what we know, but how we know it.

Final Thoughts

The more we work with AI personalities, the more we hone our internal representations of complex systems. AI helps us cultivate richer, more flexible, and more precise mental models of the world. This, in turn, enhances our ability to navigate, create, and interact with both digital and physical realities in meaningful ways.

Would love to hear from others: How has AI influenced your mental models of complex systems? Drop your thoughts in the comments! ??