Читать книгу Predictive Minds Under Pressure - Андрей Власов - Страница 6

Here we ask what the brain is actually doing when an experience becomes conscious: where the «inner movie» is implemented, how signals become significant, and why timing matters.

If you ever wanted to know what NCC, gamma synchrony and predictive coding have to do with your everyday awareness, this is where the pieces start to line up.

#NCC #gamma #thalamocortical_loop #functional_systems #significance_detector #predictive_coding

Dialogue 5. Neural Correlates: Where in the Brain Is the «Movie»?

Teacher: Let us move from sunsets to neurons — which is a bigger conceptual leap than it sounds. If consciousness is not less than what the brain does, we should ask the rudest possible question: where in the brain is the «inner movie» implemented?

Student: You mean like «this spot in the cortex = consciousness»?

Teacher: That is the tempting version. It is also wrong — which, to be fair, is a useful thing to know early. Francis Crick — one of the co-discoverers of DNA — spent the last decades of his life trying to make this question precise. Together with Christof Koch, he proposed a more careful program: look for neural correlates of consciousness, the so-called NCC.

Student: What exactly counts as an NCC?

Teacher: A standard definition is this: an NCC is the minimal set of neural events that are jointly sufficient for a specific conscious experience, given that the rest of the brain is functioning normally.

«Minimal» matters. Every experience involves the whole brain in some way, but that is not informative. We do not want a vague statement like «consciousness happens when the brain is active». We want to know which specific patterns make the difference between a perception being conscious and being unconscious.

Student: How do we even isolate such patterns?

Teacher: By finding situations where the stimulus is the same, but experience differs — which is a cleaner experiment than it sounds. Think of ambiguous figures, like the Necker cube. The image on your retina does not change, but your experience alternates between two interpretations. When that switch happens, something in the brain also switches.

Crick and Koch looked at such phenomena in vision. They found that simple activation in early visual cortex is often not enough. The crucial change appears in patterns of synchrony and long-range communication between areas. This led to a hypothesis that gamma-band synchrony — oscillations around 40 Hz — plays a role as a temporal «glue» that binds distributed neural assemblies into a single conscious content.

Student: So, consciousness is a kind of synchronized party in the brain?

Teacher: That is a picture worth keeping. If the party is too local — if the rooms are not talking to each other — you get rich processing, but no unified conscious scene. When local groups start to synchronize and broadcast information widely, a content enters the spotlight of consciousness.

This brings us to another key player: the thalamocortical loop. The thalamus is not just a relay station. It is part of a recurrent loop with the cortex, and this loop seems necessary for maintaining conscious states. Under general anesthesia or in deep coma, this loop is disrupted. Local cortical activity may continue, but the global dynamics changes and conscious experience fades.

Student: So the problem is not that the brain is quiet, but that the guests are all talking in separate kitchens.

Teacher: Exactly. Consciousness is what happens when someone opens the doors — and the conversation becomes one.

Dialogue 6. The Soviet and Russian Contribution: Functional Systems and Significance Detectors

Student: You mentioned earlier that the Soviet tradition has something important to add here. How does that fit into the picture?

Teacher: More naturally than you might expect — and more historically honestly than most neuroscience textbooks acknowledge. Let us take two examples: functional systems and significance detectors.

First, Pyotr Anokhin and his Theory of Functional Systems. Starting from the 1930s and through the 1960s, he described behavior not as a simple reflex chain, but as a dynamic system centered around an «acceptor of the result of action».

Before an action is executed, the brain forms an anticipatory model of the expected outcome. Sensory feedback is then compared to this model. If they match, the system confirms the action and moves on. If they do not, it triggers corrections. In modern language, this looks very much like a predictive model plus error correction loop.

Student: That sounds almost like predictive coding.

Teacher: Almost is an understatement. In our ontology, we treat Anokhin’s functional system as structurally homologous to Friston’s Free Energy framework. The languages differ — one is physiological, the other is mathematical — but the core move is identical: anticipate, compare, correct. The nervous system constantly anticipates future states and uses mismatches to drive learning and control.

Now, the second example: Natalia Bekhtereva and the «detector of significance.» She worked on what we would now call the neural basis of relevance and salience. Her experiments showed that certain distributed networks — involving limbic structures, basal ganglia, and frontal cortex — act as a system that marks incoming signals as subjectively significant or insignificant.

Student: So, this detector decides what gets into consciousness?

Teacher: In a way, yes — though it is less like a single bouncer at the door and more like a distributed committee. No single neuron shouts «important!»; a whole network biases which signals are allowed to reach the global workspace.

The modern language of salience maps and attentional priority is a direct descendant of this idea. When we ask «what gets on the stage of consciousness,» we are really asking how the brain’s significance detectors and salience networks work.

In honest historical terms, Bekhtereva’s lab work anticipated some of the core insights of current computational salience models — decades before the computational vocabulary existed. Our ontology simply names that continuity rather than pretending it is a coincidence.

Dialogue 7. Neural Dynamics: Consciousness as a Process, not a Snapshot

Student: So far, we have talked in terms of structures: cortex, thalamus, networks. But earlier you said consciousness is not a static pattern, but a process. Can you unpack that?

Teacher: This is where neural dynamics comes in — and this is one more place where Russian science got there earlier than the international field quite noticed.

Alexander Ivanitsky and his collaborators studied the time course of conscious perception. They showed that when a conscious experience forms, activity does not just «appear» at one moment. It evolves across time in a characteristic sequence.

A simple sketch looks like this:

— Early sensory areas respond to physical features of the stimulus.

— Associative areas integrate features into more complex patterns.

— Frontal and parietal areas integrate these patterns with context, goals, and meaning.

Conscious experience, in this view, is tied to the final integration phase — when sensation and meaning are woven together into a coherent, reportable state.

Student: Is this related to the idea of a «window of the present»?

Teacher: Yes — and this is one of those moments where a phenomenologist from 1905 and a neuroscientist from 2005 arrive at the same description from completely opposite directions. Empirically, this window seems to be on the order of a few seconds.

Neural dynamics gives this an implementation story: it takes roughly that long for the brain to complete one cycle of integration — from raw input, through intermediate processing, to a globally integrated, meaningful state. Consciousness is not a single frame. It is more like a short clip.

This time-based view will matter a great deal when we talk about stress. Under pressure, the integration window can be rushed, distorted, or flooded. But that is the next arc of the book — let us not get there before we have the tools.

Dialogue 8. Free Energy and Predictive Coding

Teacher: Now we can turn to a theory that has become genuinely central in computational neuroscience — and also, it turns out, a surprisingly good description of what it feels like to be wrong in a stressful situation. This is the Free Energy Principle (FEP) and its implementation through predictive coding.

The core claim is simple and radical at once: a brain can be modeled as a system that minimizes a quantity related to surprise about its sensory inputs.

Student: So, the brain is basically a Bayesian nerd?

Teacher: That is one good way to say it — the brain might object to the word «nerd», but the equations would quietly agree. A Bayesian reasoner constantly updates beliefs based on new evidence, always asking: given what I am sensing, what is most probably causing it, and what should I predict next? In predictive coding schemes, higher levels of the cortex send predictions downward; lower levels send back prediction errors — the gap between predicted and actual input. The system continuously adjusts its internal model to reduce these errors.

In this picture, perception is not passive reception. It is active inference: the brain is always guessing what is out there and checking those guesses against incoming data.

Student: And conscious experience appears when… what exactly happens?

Teacher: One intuitive proposal is that a state becomes conscious when prediction errors at relatively high levels of the hierarchy cannot be quickly suppressed — the model has to reorganize itself significantly. During that reorganization, the content becomes globally available and experienced.

Notice the thread back to Anokhin: his «acceptor of the result of action» is exactly a predecessor of the generative model. The mismatch he described is what we now call prediction error. The terminology changed; the insight stayed.

Correction of behavior is model updating. The Free Energy framework generalizes this logic to perception, action, and learning in a unified way.

In the ontology of this book, FEP is one of the main tools for describing how a predictive mind stays coherent in a noisy world — or fails to.

Dialogue 9. Integrated Information and Computational Mass

Student: Where does Integrated Information Theory fit into this? You said we would not treat it as a rival dogma.

Teacher: IIT comes at the problem from the opposite direction — which is exactly what makes it a useful companion to FEP, not a rival. Instead of starting with neurons and computation, it starts with the structure of experience itself and asks: what must any system have if it is going to have experience at all?

Giulio Tononi proposed a set of axioms: experience exists, it is structured, specific, integrated, and exclusive. From these, he constructed a formal quantity, Φ (phi), which measures how much information a system carries as a whole, over and above the sum of its parts. A system with high Φ is said to have a rich, integrated state.

In this book, we do not take the slogan «consciousness is Φ» literally — it is a tempting slogan but an overcommitment. Instead, we treat Φ as a candidate for a computational mass index: a way to ask how much structured, integrated «stuff» a system can carry in principle.

Student: And how does that relate to FEP?

Teacher: FEP tells us how systems maintain themselves over time by updating models and reducing surprise. IIT tells us something about how densely structured their internal states can be. They point to different, but potentially complementary aspects of conscious systems.

You could say, playfully, that FEP describes how a system moves through its state space, while IIT hints at how «heavy» or «thick» any given state is — how much is packed inside the moment. One tracks the journey; the other tracks the density of the traveller.

The deeper theory — if it ever arrives — might unify these. For now, we use both as tools. Neither breaks if you drop the other.

Dialogue 10. A Brief Truce

Student: So, we have gamma synchrony, thalamocortical loops, functional systems, significance detectors, predictive coding, and integrated information. That is a lot of moving parts.

Teacher: It is — and if your head is slightly overloaded right now, that is a sign the material has actually been landing. But notice a pattern before we move on.

— Neural correlates (NCC) tell us where and when conscious contents appear in the brain.

— Functional systems and predictive coding tell us how the brain anticipates and corrects its own states.

— Significance detectors and global workspaces tell us which contents make it into the spotlight.

— IIT and related measures hint at how structured and unified those states can be.

We do not need to collapse this into one slogan — and we should probably be suspicious of anyone who tries. For our purposes, it is enough to treat these as parts of a single conceptual toolbox, each with its own job.

In the next chapter, we will leave the brain for a moment and look at consciousness from another angle: as activity — as something that is produced in doing, in social interaction, and in meaning. This is where the Soviet tradition and modern enactive approaches have a lot to say.