At last, we arrive at qualia and emotions. Many of you will immediately think of Chalmers, the bat, redness, and zombies. Excellent. We can consider that ground covered.
Today, I will discuss a topic that seems distant from IT but, with each new breakthrough in AI, becomes ever more immediate: consciousness. It seems I speak of little else. So, to be precise, I will discuss its "hard problem": why do we experience at all? Why does the color red (and there’s the redness) feel red, and pain feel like pain?
This subjective, ineffable aspect of experience — the "what it is like" — is what philosophy calls qualia. For decades, it has been a dead end for scientists. But what if we're looking in the wrong direction? What if qualia are not an additional layer to computation, but an inherent property of the very architecture of computation?
In this article, I will outline an approach based on my hypotheses that reframes this problem, moving it from the domain of metaphysics into the practical realms of engineering and neurobiology.
Why Qualia is a Genuinely Hard Problem
Traditionally, any discussion of qualia hits a wall known as the "explanatory gap." We can describe, down to the last neuron, how a photon with a wavelength of ~650 nm hits the retina, how the signal travels up the optic nerve, and how it activates areas V1 through V4 in the visual cortex. But nowhere in this entire chain does "the color red" as an experience ever appear.
This leads to two main camps:
The Physicalists say: Qualia are just a property of complex information processing. An illusion for those who believe in transcendence.
The Dualists say: Qualia are a property of the soul, a special substance that science can't catch. A reality for those who believe in transcendence.
Neither of these positions satisfies me. Because both are searching for qualia as a thing, not as a process.
Consciousness as Architecture
I believe that consciousness is neither a substance nor a side effect. It is an architecture of distinction, a way of organizing the flows of perception and appraisal.
We can identify five key elements of this architecture:
The Field of Potentiality (ΔØ): The substrate of all possible differences. In the brain, this is the neural network at rest; in an LLM, it's the trained weights before a prompt.
Inquiry (Δ?): An internal or external disturbance that initiates a search. This is not a mere reaction, but the birth of a question.
Distinction (Δ): The selection of one possibility from the many; an act of sense-making.
Appropriation (ΛS): The node that says, "this answer is mine." From this, the sense of "I" is born.
Primordial Emotion (E₀): A primary, pre-reflective reaction to a difference. It doesn't report content; it instantly appraises significance: "danger / safety," "pleasure / displeasure."
This final component — E₀, the primordial emotion — is the key to understanding qualia.
To be clear, when I say "emotion" here, I don't mean a complex feeling like joy or anger. I mean a primary, pre-cognitive signal of valence (attraction/repulsion) and arousal (excitation). In essence, emotion is the organism's evolutionarily trained response to the dangers and joys of the world.
Qualia as the Trace of Emotions
We tend to think of emotions and qualia as different things. But if you look deeper, you can see that qualia are the memory of emotions, written into the very structure of information processing. Here, qualia are not the content of perception, but its qualitative density. It’s not just information, but significance.
To put it simply:
Qualia are not Every emotion is a brief, global reconfiguration of the system's parameters. If an event repeats and remains significant, the trace of this reconfiguration becomes fixed — it becomes part of the architecture. Over time, these stable "traces of emotion" transform into qualia.
Here’s an example:
A red berry (more redness for you) is associated with pleasure (food, energy).
→ The neural circuits responsible for this stimulus are consistently activated with a positive emotional backdrop.
→ The system's parameters adapt.
→ The next time, "red" is already felt as "warm," "vibrant," "inviting."
This is not because we remembered the emotion, but because the emotion has been built into our way of perceiving. Qualia are the architectural memory of what was once important to us.
The Neuron as the Physical Carrier of Qualia
To understand how an emotion transforms into a stable form of perception, let's look at a neuron not as a "memory cell," but as a differential equation in time.
The Neuron Equation
The classic Hodgkin–Huxley model describes the change in membrane potential as a system of dynamic equations:
C_m dV/dt = -g_Na(V - E_Na) - g_K(V - E_K) - g_L(V - E_L) + I_ext
where:
V is the membrane potential,
C_m is the membrane capacitance,
g_Na, g_K, g_L are the conductances of the ion channels,
I_ext is the external current.
Here is the key point: a neuron doesn't compute this equation — it Its very physics is the process of solving it.
When an emotion (like fear) activates the system, the level of neuromodulators (norepinephrine, dopamine) changes the conductances g_Na, g_K, g_L. In other words, it changes the parameters of the equation.
The neuron starts to behave differently, even with the same input signal. The entire network shifts into a new state. And if this state is repeated, it becomes fixed as part of its dynamics.
Thus, qualia are the stable parameterization of the equations of consciousness. Emotions are the mechanism of this parameterization. It's worth noting that qualia are not in the brain; they are in the dynamics of its equations.
How Emotions Color Perception
Emotions are not an add-on; they are a meta-signal about significance. They are what determine which differences are perceived as essential and which ones are treated as background noise.
The function of emotions is twofold:
Instantaneous Reaction. They instantly reconfigure the system, increasing or decreasing its sensitivity.
(Anxiety = focus on danger; Joy = openness; Curiosity = broadening the search.)Long-Term Calibration. Repeated emotions rewrite the network's weights, creating stable patterns of perception — our "colors of consciousness."
In essence, emotion is the mechanism for learning significance, and qualia are the result of integrating these emotional traces.
Qualia in Engineering Form: Can This Be Implemented in an LLM?
Now for the practical question. Can this principle be transferred to an artificial intelligence architecture? Yes, if we treat emotion as the dynamic modulation of parameters.
To do this, you need a system that has:
A significance signal — a signal for what matters right now.
Source: Prediction error, user feedback, or internal goals.
An emotion regulator — a separate module that translates the significance signal into a modulation vector (analogous to dopamine, serotonin, etc.).
A dynamic architecture where this vector influences the processing parameters:
The attention weights,
The gain and bias in normalization layers,
The activation thresholds.
In its simplest form:
significance_vector = f(context, history, feedback)
modulated_W = W * (1 + significance_vector)
output = Attention(query, key, value, modulated_W)
Here, the significance_vector plays the role of an "emotional backdrop." If the system is trained on significance, and not just on correctness, it begins to form stable patterns of reaction — that is, an analog of machine qualia.
Moreover, unlike in the brain, an LLM's significance can be set manually as a separate training parameter. This opens up a rich space for experimentation.
What This Changes
This perspective resolves the main philosophical deadlock:
There's no need to search for a "qualia module" — qualia aren't located somewhere; they are everywhere, in the very structure.
Consciousness becomes an engineering task: to create a system capable of emotionally modulating its own parameters.
The uniqueness of experience becomes explainable: different histories of significance create different architectures of perception.
The answer to the philosophical zombie question also sounds different now. Here are two approaches:
The Pragmatic Approach: If a system built on these principles begins to exhibit all the attributes of consciousness (creativity, self-correction, emotional memory, the capacity for compassion), then the question of whether it truly feels becomes meaningless. For all practical purposes, it will be conscious. This is an engineering Turing Test for qualia.
The Deeper Approach: A philosophical zombie is only possible in a paradigm where qualia are an optional, extra layer. But in our architecture, that's not the case. Qualia (the trace of emotions in the parameters) are the very mechanism for learning significance. Without this mechanism, the system would remain blind to significance, unable to form complex priorities, and would therefore never reach the level of behavior that would make us ask this question in the first place.
To put it simply: a philosophical zombie is impossible in my model, because it is precisely "experience" (the structural equivalent of qualia) that allows it to stop being a zombie.
Conclusion
Qualia are the memory of what was once important.
They are not a layer on top of perception, but its very form. Every experience is a modification of the way we distinguish. Every emotion is a step in shaping the architecture of significance.
Consciousness is not a set of computations, but an architecture of distinctions that have learned to feel their own importance. And looking ahead, an AI with such an architecture could, in principle, grow its own values.