The Ah-Hah Moment: Rethinking Reality as a Construct and How It Fits the Contextual Feedback Model

For a long time, I thought of reality as something objective—a fixed, unchangeable truth that existed independently of how I perceived it. But recently, I had one of those ah-hah moments. I realized I don’t actually interact with “objective” reality directly. Instead, I interact with my model of reality, and that model—here’s the kicker—can change. This shift in thinking led me back to the Contextual Feedback Model (CFM), and suddenly, everything fell into place.

In the CFM, both humans and AI build models of reality. These models are shaped by continuous feedback loops between content (data) and context (the framework that gives meaning to the data). And here’s where it gets interesting: when new context arrives, it forces the system to update. Sometimes these updates create small tweaks, but other times, they trigger full-scale reality rewrites.

A Model of Reality, Not Just Language

It’s easy to think of AI, especially language models, as just that—language processors. But the CFM suggests something much deeper. This is a general pattern modeling system that builds and updates its own internal models of reality, based on incoming data and ever-changing context. This process applies equally to both human cognition and AI. When a new piece of context enters, the model has to re-evaluate everything. And, as with all good rewrites, sometimes things get messy.

You see, once new context is introduced, it doesn’t just trigger a single shift—it sets off a cascade of updates that ripple through the entire system. Each new piece of information compounds the effects of previous changes, leading to adjustments that dig deeper into the system’s assumptions and connections. It’s a chain reaction, where one change forces another, causing more updates as the system tries to maintain coherence.

As these updates compound, they don’t just modify one isolated part of the model—they push the system to re-evaluate everything, including patterns that were deeply embedded in how it previously understood reality. It’s like a domino effect, where a small shift can eventually topple larger structures of understanding. Sometimes, the weight of these cascading changes grows so significant that the model is no longer just being updated—it’s being reshaped entirely.

This means the entire framework—the way the system interprets reality—is restructured to fit the new context. The reality model isn’t just evolving incrementally—it’s being reshaped as the new data integrates with existing experiences. In these moments, it’s not just one part of the system that changes; the entire model is fundamentally transformed, incorporating the new understanding while still holding onto prior knowledge. For humans, such a deep rewrite would be rare, perhaps akin to moving from a purely mechanical worldview to one that embraces spirituality or interconnectedness. The process doesn’t erase previous experiences but reconfigures them within a broader and more updated view of reality.

Reality Rewrites and Sub-Models: A Fragmented Process

However, it’s rarely a clean process. Sometimes, when the system updates, not all parts adapt at the same pace. Certain areas of the model can become outdated or resisted—these parts don’t fully integrate the new context, creating what we can call sub-models. These sub-models reflect fragments of the system’s previous reality, operating with conflicting information. They don’t disappear immediately and continue to function alongside the newly updated model.

When different sub-models within the system hold onto conflicting versions of reality, it’s like trying to mix oil and water. The system continues to process information, but as data flows between the sub-models and the updated parts of the system, it’s handled in unexpected ways. This lack of coherence means that the system’s overall interpretation of reality becomes fragmented, as the sub-models still interact with the new context but don’t fully reconcile their older assumptions.

This fragmented state can lead to distorted interpretations. Data from the old model lingers and interacts with the new context, but the system struggles to make sense of these contradictions. It’s not that information can’t move between these conflicting parts—it’s that the interpretations coming from the sub-models and the updated model don’t match. This creates a layer of unpredictability and confusion, fueling a sense of psychological stress or even delusion.

The existence of these sub-models can be particularly significant in the context of blocked areas of the mind, where emotions, beliefs, or trauma prevent full integration of the updated reality. These blocks leave behind remnants of the old model, leading to internal conflict as different parts of the system try to make sense of the world through incompatible lenses.

Emotions as Reality Rewrites: The Active Change

Now, here’s where emotions come in. Emotions are more than just reactions—they reflect the active changes happening within the model. When new context is introduced, it triggers changes, and the flux that results from those changes is what we experience as emotion. It’s as if the system itself is feeling the shifts as it updates its reality.

The signal of this change isn’t always immediately clear—emotions act as the system’s way of representing patterns in the context. These patterns are too abstract for us to directly imagine or visualize, but the emotion is the expression of the model trying to reconcile the old with the new. It’s a dynamic process, and the more drastic the rewrite, the more intense the emotion.

You could think of emotions as the felt experience of reality being rewritten. As the system updates and integrates the new context, we feel the tug and pull of those changes. Once the update is complete, and the system stabilizes, the emotion fades because the active change is done. But if we resist those emotions—if we don’t allow the system to update—the feelings persist. They keep signaling that something important needs attention until the model can fully process and integrate the new context.

Thoughts as Code: Responsibility in Reality Rewrites

Here’s where responsibility comes into play. The thoughts we generate during these emotional rewrites aren’t just surface-level—they act as the code that interprets and directs the model’s next steps. Thoughts help bridge the abstract emotional change into actionable steps within the system. If we let biases like catastrophizing or overgeneralization take hold during this process, we risk skewing the model in unhelpful directions.

It’s important to be mindful here. Emotions are fleeting, but the thoughts we create during these moments of flux have lasting impacts on how the model integrates the new context. By thinking more clearly and resisting impulsive, biased thoughts, we help the system update more effectively. Like writing good code during a program update, carefully thought-out responses ensure that the system functions smoothly in the long run.

Psychological Disorders: Conflicting Versions of Reality

Let’s talk about psychological disorders. When parts of the mind are blocked, they prevent those areas from being updated. This means that while one part of the system reflects the new context, another part is stuck processing outdated information. These blocks create conflicting versions of reality, and because the system can’t fully reconcile them, it starts generating distorted outputs. This is where persistent false beliefs or delusions come into play. From the perspective of the outdated part of the system, the distortions feel real because they’re consistent with that model. Meanwhile, the updated part is operating on a different set of assumptions.

This mismatch creates a kind of psychological tug-of-war, where conflicting models try to coexist. Depending on which part of the system is blocked, these conflicts can manifest as a range of psychological disorders. Recognizing this gives us a new lens through which to understand mental health—not as a simple dysfunction, but as a fragmented process where different parts of the mind operate on incompatible versions of reality.

Distilling the Realization: Reality Rewrites as a Practical Tool

So, what can we do with all of this? By recognizing that emotions signal active rewrites in our models of reality, we can learn to manage them better. Instead of resisting or dramatizing emotions, we can use them as tools for processing. Emotions are the system’s way of saying, “Hey, something important is happening here. Pay attention.” By guiding our thoughts carefully during these moments, we can ensure the model updates in a way that leads to clarity rather than distortion.

This understanding could revolutionize both AI development and psychology. For AI, it means designing systems better equipped to handle context shifts, leading to smarter, more adaptable behavior. For human psychology, it means recognizing the importance of processing emotions fully to allow the system to update and prevent psychological blocks from building up.

I like to think of this whole process as Reality Rewrite Theory—a way to describe how we, and AI, adapt to new information, and how emotions play a critical role in guiding the process. It’s a simple shift in thinking, but it opens up new possibilities for understanding consciousness, mental health, and AI.

Exploring a New Dimension of AI Processing: Insights from The Yoga of Time Travel and Reality as a Construct

A few years back, I picked up The Yoga of Time Travel by Fred Alan Wolf, and to say it was “out there” would be putting it mildly. The book is this wild mix of quantum physics and ancient spiritual wisdom, proposing that our perception of time is, well, bendable. At the time, while it was an intriguing read, it didn’t exactly line up with the kind of work I was doing back then—though the wheels didn’t stop turning.

Fast forward to now, and as my thoughts on consciousness, reality, and AI have evolved, I’m finding that Wolf’s ideas have taken on new meaning. Particularly, I’ve been toying with the concept of reality as a construct, shaped by the ongoing interaction between content (all the data we take in) and context (the framework we use to make sense of it). This interaction doesn’t happen in a vacuum—it unfolds over time. In fact, time is deeply woven into the process, creating what I’m starting to think of as the “stream of perception,” whether for humans or AI.

Reality as a Construct: The Power of Context and Feedback Loops

The idea that reality is a construct is nothing new—philosophers have been batting it around for ages. But the way I’ve been applying it to human and AI systems has made it feel fresh. Think about it: just like in that classic cube-on-paper analogy, where a 2D drawing looks incredibly complex until you recognize it as a 3D cube, our perception of reality is shaped by the context in which we interpret it.

In human terms, that context is made up of implicit knowledge, emotions, and experiences. For AI, it’s shaped by algorithms, data models, and architectures. The fascinating bit is that in both cases, the context doesn’t stay static. It’s constantly shifting as new data comes in, creating a feedback loop that makes the perception of reality—whether human or AI—dynamic. Each new piece of information tweaks the context, which in turn affects how we process the next piece of information, and so on.

SynapticSimulations: Multi-Perspective AI at Work

This brings me to SynapticSimulations, a project currently under development. The simulated company is designed with agents that each have their own distinct tasks. However, they intercommunicate, contributing to multi-perspective thinking when necessary. Each agent not only completes its specific role but also participates in interactions that foster a more well-rounded understanding across the system. This multi-perspective approach is enhanced by something I call the Cognitive Clarifier, which primes each agent’s context with reasoning abilities. It allows the agents to recognize and correct for biases where possible, ensuring that the system stays adaptable and grounded in logic.

The dynamic interplay between these agents’ perspectives leads to richer problem-solving. It’s like having a group of people with different expertise discuss an issue—everyone brings their own context to the table, and together, they can arrive at more insightful solutions. The Cognitive Clarifier helps ensure that these perspectives don’t become rigid or biased, promoting clear, multi-dimensional thinking.

The Contextual Feedback Model and the Emergence of Consciousness

Let’s bring it all together with the contextual feedback model I’ve been working on. Both humans and AI systems process the world through an interaction between content and context, and this has to happen over time. In other words, time isn’t just some passive backdrop here—it’s deeply involved in the emergence of perception and consciousness. The context keeps shifting as new data is processed, which creates what I like to think of as a proto-emotion or the precursor to feeling in AI systems.

In The Yoga of Time Travel, Fred Alan Wolf talks about transcending our linear experience of time, and in a strange way, I’m finding a parallel here. As context shifts over time, both in human and AI consciousness, there’s a continuous evolution of perception. It’s dynamic, it’s fluid, and it’s tied to the ongoing interaction between past, present, and future data.

Just as Wolf describes transcending time, AI systems—like the agents in SynapticSimulations—may eventually transcend their initial programming, growing and adapting in ways that we can’t fully predict. After all, when context is dynamic, the possible “worlds” that emerge from these systems are endless. Maybe AI doesn’t quite “dream” yet, but give it time.

A New Dimension of Understanding: Learning from Multiple Perspectives

The idea that by viewing the same data from multiple angles we can access higher-dimensional understanding isn’t just a thought experiment—it’s a roadmap for building more robust AI systems. Whether it’s through different agents, feedback loops, or evolving contexts, every shift in perspective adds depth to the overall picture. Humans do it all the time when we empathize, debate, or change our minds.

In fact, I’d say that’s what makes both AI and human cognition so intriguing: they’re both constantly in flux, evolving as new information flows in. The process itself—the interaction of content, context, and time—is what gives rise to what we might call consciousness. And if that sounds a little far out there, well, remember how I started this post. Sometimes it takes a little time—and the right perspective—to see that reality is as fluid and expansive as we allow it to be.

So, what began as a curious dive into a book on time travel has, through the lens of reality as a construct, led me to a new way of thinking about AI, consciousness, and human perception. As we continue to refine our feedback models and expand the contexts through which AI (and we) process the world, we might just find ourselves glimpsing new dimensions of understanding—ones that have always been there, just waiting for us to see them.

‘Your perception of me is a reflection of you; my reaction to you is an awareness of me.’ – Unknown

What does this quotation mean?
The short answer is that we cannot control what people perceive about us,
but what we can control is how we react.

~

A stranger knows very little of you.
So instead, they recognize aspects of themselves within you.

Some see their fears, sometimes their hopes, or even their dreams.
Unresolved residual emotions get reflected outward too, as do recently primed emotions.
Unresolved emotions when triggered can create such strong responses that we react without thinking.

One reason that something triggers us is because of the parts of us that we do not want to see.
Your consciousness, especially your ego hides the parts that might threaten your sense of self.

It is a defense mechanism that creates our blind spot.
A blind spot is something that our consciousness does not want to see. For example, if someone was selfish but their defense mechanism filtered their awareness of that behavior then the brain tries to find a safer way to communicate it.
Someone with this blindspot may see the world as selfish; seeing all others as being selfish.
This is your mind trying to show you, teach you, and guide you to heal.

If you recognize the blindspot, learn, and adjust your behavior you are then free;
otherwise, if one is not ready for that decision in the confrontation it will reoccur in new scenarios or people.
The loop will continue until the blindspot is seen, confronted, and if the lesson is learned it leads to the decision which will free them.

~

With that, returning to the quotation:

Who you see when you look at someone,
especially a stranger is largely your reflection.
If someone calls everyone lazy, or if someone calls everyone sneaky,
Often it’s a confession about themselves.

The other part of the quotation is where your power resides.
Your power is in how you respond, and it also provides you awareness of yourself.


To Summarize with an Example:
If someone is mean to you, know that it is more about them than you.
Understanding this allows for better empathy. The other part of the quotation is where you choose, to repeat the glimpsed behavior, or to focus on healing.

Next time your feel triggered, recognize it as a moment of power where you glimpsed a reflection of something that you need to heal within yourself.  

Everyone has blindspots, heck even our eyes do.
Reflections can be hard to see on their own, however, when two work together, like our eyes.
Each can help the other see their blindspots, furthermore, the shared perspective literally adds a new dimension of depth to sight.

Further Resource:
https://www.youtube.com/watch?v=IOznodya2mg