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A list is proposed of fundamental challenges that remain unsolved—or are addressed in an extremely inadequate manner—within mainstream approaches in contemporary science (neuroscience, AI, cognitive science, philosophy of consciousness), yet possess a rigorous, algorithmic, implementation-independent model within the MVAP framework.
What unites these problems is this: they require understanding subjective experience as a function of adaptive regulation, rather than as an epiphenomenon, a statistical artifact, or a metaphysical mystery.
The bridge between MVAP (Model of Volitional Adaptive Psyche) and neurophysiological concepts is laid out in the monograph “Foundations of a Fundamental Theory of Consciousness” (ornit.ru/68715), while the implementation-independent interpretation is provided in the book “Circuit Design of Individual Adaptive Systems” (fornit.ru/71218). A terminological glossary became necessary due to the replacement of contradictory and now-blurred concepts with systemic definitions: fornit.ru/gls2.
This article does not merely enumerate problems—it constructs a unified philosophy in which all elements are interconnected. The requirement of “implementation independence” and verification through prototypes constitutes a powerful methodological stance that shifts the discussion away from the domain of pure speculation.
The problems are described in simplified form, without deep analysis of counterarguments—a depth impossible within the format of an article. Thus, the main line of argumentation is deferred to the aforementioned monograph. Hence the necessity of referring to the materials on the aggregator website fornit.ru.
1. Disconnection Between the Foundations of Homeostatic Regulation and Adaptive Brain Functions
This “disconnection” is not merely an unfortunate oversight but a consequence of the historical separation of scientific disciplines and methodological approaches. Traditionally, homeostasis and higher brain functions have been studied as separate, nearly isolated processes: homeostasis as the “lower floors” of regulation, and adaptive functions as the “upper floors” of the brain.
The latter is often studied in detachment from the organism’s internal state. A rat in a maze or a human in an fMRI scanner are treated as abstract information processors, whose internal needs (hunger, thirst) are treated merely as an experimental “variable,” rather than as the foundation of behavior.
Result of this disconnection: we have developed a science that poorly explains why we do anything at all. Why do we learn, seek novelty, or take risks? Cognitive science speaks of “reward” and “motivation” but often overlooks that these concepts are rooted in basic homeostasis.
Although contemporary theories directly address this issue, key concepts here are Allostasis and the Predictive Coding Theory. If predictions and actual signals align—everything is fine. If a mismatch occurs (e.g., glucose levels begin to drop and this was not predicted)—an allostatic prediction error arises. But this error is the driving force of behavior. At a low level, it triggers autonomic correction; at a high level, this same error, projected into the limbic and frontal cortices, is subjectively experienced as a motivational state—hunger, thirst, anxiety. It is precisely this state that directs complex behavior: you do not simply eat “reflexively,” but recall where the restaurant was, plan a route, decide what to order, and manage your budget.
Mainstream science treats adaptation predominantly through the lenses of learning, perception, decision-making, and consciousness—without homeostatic foundations. A house without a foundation cannot have a second floor.
This disconnection is especially critical when attempting to build integrative models of consciousness, where the “self” emerges not only from external experience but also from continuous monitoring of internal states.
It is also critical in attempts to create adaptive artificial systems capable of autonomous self-regulation (not only energetic, but also functional—e.g., balancing exploration and exploitation depending on “internal resources”).
MVAP establishes an unbroken causal chain within adaptive mechanisms, which systematically accompanies the description of adaptive systems—from the simplest living organisms limited solely by basic homeostatic regulation, to the most complex manifestations of the psyche.
At all levels, the MVAP system of principles solves the same task: to ensure rapid, reliable, and energy-efficient responses to recurring conditions, thereby freeing resources for solving new problems.
No adaptive mechanism of a living being—even the most complex (e.g., playing the violin or driving in traffic flow)—can function if basic homeostatic regulation (energy, temperature, oxygen, etc.) is disrupted.
This allows us to abandon the artificial division between “low” and “high” behavior and instead view adaptation as a single dynamic system, where complexity arises not from new entities but from the hierarchical layering and coordination of the same principles—homeostasis and automation.
MVAP offers a more rigorous architectural implementation of the ideas of “Allostasis” and the “Predictive Brain.”
2. Homeostatic States and Neuromodulators
Many neurobiologists, especially science popularizers, still believe dopamine is “reward”—that it is released to produce a “good” feeling. Cause and effect are confused: first, the homeostatic state of “Good” is activated, and that triggers a dopamine surge.
The traditional sequence is presented as:
System receives something beneficial (food, social approval) → dopamine release → subjective feeling of “Good.”
This puts the cart before the horse. It implies that a chemical substance causes the subjective state. This fails to explain how a purely chemical signal gives rise to the complex mental state of “pleasure” or “satisfaction.” This is reductionism that does not answer the systemic question.
MVAP introduces a systemic level between chemistry and subjective experience—the level of the system’s functional state.
Neuromodulators are an ancient method for distinguishing basic behavioral contexts (feeding, exploratory, sexual, defensive, etc.), so that mechanisms tied to a specific neuromodulator are preferentially activated. This is an ancient system: “one modulator—one behavioral program.” Evolutionary development of this method has led to numerous mechanisms now only weakly tied to basic behavioral styles.
The primitive structure seen in simple organisms can be represented as follows:
At this stage, the link is direct: threat → norepinephrine → activation of adaptive behavior. But evolution does not discard old systems—it overlays and complicates them.
Rather than reconfiguring the entire neural network for each situation, the organism uses diffuse modulatory signals to temporarily “switch modes”—enhancing some connections and weakening others. This allows rapid adaptation to changing conditions without requiring structural network reorganization.
Dopamine evolved from a simple “food is here” signal into a system for reward prediction and motivation. Now it operates not only for food or sex but also for social approval (social media likes), intellectual insights (solving a hard problem), gambling, and creativity. This is no longer just a “feeding context,” but an abstract system of goal-setting and learning. The result is a complex orchestra where neuromodulators (instruments) no longer play isolated melodies (behavioral acts) but create nuanced emotional and motivational states.
In artificial implementations of adaptive systems, neuromodulators are entirely unnecessary.
The cause of adaptive regulation is the opportunity to normalize a homeostatic parameter (a “Vital”)—replenishing a resource (eating when hungry), eliminating a threat (avoiding danger), etc.
Normalization of a Vital is registered by the system as a positive outcome. The system must then record which specific action led to this result in order to repeat it in the future. Moreover, the system continues to maintain the “Good” state until the need is fully satisfied.
In the MVAP model, dopamine is not “reward,” but a signal of successful action completion, which reinforces the chain of Automatisms that led to success (“reinforcement learning”) and tags currently active Dendrarch images as “causally linked to a positive outcome.”
Consequence: the subjective experience of “Good.” This experience arises at the moment of “Awareness of Significance” of the positive change in the system’s state.
Dopamine release alone does not create “Good.” There are many cases where dopamine is released without pleasure (e.g., during stress or novelty), and where pleasure occurs without a strong dopamine surge.
The mechanism of homeostatic state differentiation (Difznacher: fornit.ru/70332) evaluates the change in significance after an action. If it is positive, the “Good” state is activated—and only then is the dopaminergic system engaged.
The “Good” experience is no longer a mysterious byproduct of a chemical reaction but a conscious reflection of the objective improvement in the system’s condition.
One cannot explain behavior by saying “the brain wants dopamine.” The brain “wants” nothing—it reacts to deviations in Vitals and strives to compensate them. Dopamine is merely part of the feedback mechanism.
This aligns with more modern views (e.g., Berridge, 2018), where dopamine is increasingly interpreted as a signal of “salience” or “motivational importance,” not “pleasure.” However, MVAP goes further—it integrates this signal into a strict hierarchical scheme of adaptive regulation, where egocentric significance (fornit.ru/70018) is the main regulator, and neuromodulators are merely executive mechanisms.
3. Contradictions Arising from Multiple Definitions and Types of Reflexes
In academic literature, the term reflex is used in different contexts and with varying degrees of rigor. The concept itself has evolved from a simple mechanical arc to a complex systemic process, leading to terminological confusion. Contradiction arises when we attempt to describe all these multi-level phenomena with a single term—reflex—originally meaning something simple and mechanistic.
Academic definitions are incompatible in their level of analysis. For example, the physiological definition assumes strict localization and simplicity, whereas the Pavlovian definition allows for complex temporal and associative dependencies. This leads to confusion: the same phenomenon may be considered a “reflex” in one approach and “complex behavior” in another.
The classical definition of a conditioned reflex (CR) as “acquired during life” in contrast to the “innate” unconditioned reflex (UR) is a gross oversimplification and even methodologically incorrect.
An unconditioned reflex: the link between stimulus (meat) and response (saliva) is genetically and anatomically hardwired. Neural pathways are predetermined. Conditions only modulate the readiness of this innate arc to fire. But it is precisely the presence of specific conditions that triggers a particular UR.
A conditioned reflex: the link between an initially neutral stimulus (bell) and a response (saliva) is newly formed through individual experience via temporal pairing. The neural basis is plastic synapses forming a new functional connection in the neocortex and other higher brain regions.
Thus, the main contradiction in definition: the classification “innate/acquired” obscures the true essence—different mechanisms and localizations of neural connections. “Unconditionedness” is not the absence of conditions, but the absence of the need for individual learning.
Modern neuroscience shows that a conditioned reflex is not a “stimulus–response” link, but a “stimulus–stimulus” link: a CR is a clone of an existing reflex applied to a new stimulus.
The core problem is the attempt to explain complex behavior through the oversimplified category of “reflex.”
MVAP terminologically classifies different stimulus–response mechanisms that operate without conscious involvement—from the simplest to the most complex—clearly differentiating reactions to specific conditions that do not require awareness from processes that form new reactions through conscious involvement: fornit.ru/art7. Consciousness is needed to create new habitual responses that no longer require resource expenditure or time, but instead execute quickly and reliably. This highlights the functional, adaptive, engineering role of consciousness in the architecture of intelligence—particularly valuable for building working models of artificial adaptive systems.
4. “Eternal Novelty” and Adaptation to Unpredictable Environments
Existing AI systems (including AGI prototypes) operate within predefined or statistically predictable spaces. They catastrophically fail when confronted with fundamentally new situations not represented in their training data.
Deep learning requires massive relevant datasets for retraining. Modern AI systems lack principled features to distinguish meaningful novelty from noise in the form of the most relevant signals.
LLMs (e.g., GPT) generate text but lack a mechanism to recognize encountering something genuinely new and important. Their response is statistical extrapolation, not an orienting reflex.
Reinforcement Learning (RL) requires redefinition of the reward function for new conditions.
MVAP describes the Orienant mechanism—an intrinsic detector of mismatch between prediction (Dendrarch) and reality. It automatically isolates meaningful novelty (Novelty × Significance): fornit.ru/69461.
The “Cycle of Comprehension” (Iteron) is triggered precisely when ready-made solutions (Automatisms) are absent.
One could imagine a simple “Pioneer Agent” in a physical simulator (e.g., Unity or NVIDIA Isaac Sim) with extremely sparse initial data. Its task is to explore an environment filled with objects of unpredictable properties (e.g., a ball that sometimes bounces, sometimes explodes, or turns into liquid). Success is not victory, but the agent’s ability to autonomously form a new behavioral model for an object that violated all prior expectations—and to do so in minimal trials.
5. Goal Formation in Novel Situations Without External Specification
Neuroscience explains motivation through dopamine (see point 2) but does not explain where goals originate in a new situation.
MVAP: Goals form automatically with each new engagement of conscious attention, based on the current state of Vitals, Homeocontext, and meaningful novelty, via a specialized Infofunction of goal-setting (fornit.ru/67888). The goal theme is maintained at the level of the global Info-map (fornit.ru/68540) and Gestalt structure (fornit.ru/69108).
6. Dynamic Re-evaluation of Object Significance
Neuroscience records activity correlations but does not explain why the same stimulus can be a threat in one context and a resource in another.
MVAP: Significance is an egocentric, nonlinear, context-dependent metric (fornit.ru/66643), dynamically updated in the Infocontext at each step of awareness. Significance enables semantic understanding models and preserves them in the semantic component of historical memory (fornit.ru/67560).
7. Egocentric Dynamic Motivation
Motivation in modern AI is externally defined. The system cannot autonomously generate a new goal based on its internal state—nothing like “boredom,”“curiosity,” or “dissatisfaction with achievement” exists.
Research exists on this, but it often adds only an external “curiosity drive,” not integrated into the overall architecture.
MVAP:
At the foundation of everything—from simple reflexes to complex psychic phenomena—lies egocentric significance: fornit.ru/70018.
8. Origin and Function of Emotions
Are emotions merely bodily states (James-Lange theory) or cognitive appraisals? How are they integrated with decision-making and consciousness?
Neuroscience studies separate neural circuits for fear, anger, joy, but lacks a unified model explaining emotion as a universal significance-assessment mechanism for any stimulus—from pain to a mathematical theorem.
MVAP: “Emotions” are not a separate module but a manifestation of the Significance System. Emotion is the subjective experience of significance dynamics, reflecting the state of basic behavioral contexts—behavioral styles (fornit.ru/70312).
One can demonstrate how the same architecture generates “emotional” responses at different levels: from bodily fear (pain) to social shame (violation of an abstract norm)—through a single significance-evaluation mechanism.
9. Detection of Meaningful Novelty Amid Perceptual Noise
Neural networks detect anomalies but do not distinguish adaptively significant novelty from neutral novelty. Attention is modeled as a filter, not as an orienting reflex to meaningful novelty.
MVAP: Novelty is defined as incompleteness in a Dendrarch branch; relevance = Novelty × Significance → Orienant → Aten.
10. Memory Consolidation: From Hippocampus to Cortex
It is known that the hippocampus captures episodic memories rapidly, which are then slowly “consolidated” into the cortex. But what process underlies this transfer?
Neuroscience describes the phenomenon but not the mechanism. What is “pumped” from the hippocampus to the cortex? And how is new knowledge integrated with existing knowledge without conflict?
MVAP:
One can demonstrate how a system, after experiencing many “table” episodes, gradually forms an abstract “table” node in Generalization Memory, while individual episodes remain in Episodic Memory. One can show that “hippocampal” damage in the model disrupts new episode recording but does not prevent use of old generalizations.
Neuroscience’s difficulties in describing long-term memory mechanisms stem from the enormous complexities nature had to overcome—complexities that impose severe limits, restricting the number of effective memories stored in historical memory to about 100 episodes per day (fornit.ru/71301). These limitations do not exist in artificial implementations, allowing isolation of the core functional role of historical memory in forming new conscious responses.
11. Formation of a Unique Ontogenesis (Lifelong Learning Without Catastrophic Forgetting)
Neural networks suffer from catastrophic forgetting—learning new things erases old ones. No model exists that can accumulate a personal, structured experience over its entire “life,” where new knowledge enriches rather than replaces old knowledge.
Catastrophic forgetting is one of the hardest problems. Solutions like Elastic Weight Consolidation are mere crutches.
Neuroscience also lacks a unified model linking motor skill formation, episodic memory, and abstract concepts into a single hierarchical structure.
MVAP: The Dendrarch architecture, historical memory structure, and the channel of conscious prioritized attention are the model of ontogenesis. New nodes (“images,”“abstractions”) form as superstructures over existing primitives without overwriting them: fornit.ru/70546.
Separation of memory into Semantic, Episodic, Generalization, Gestalt, and Automatism types—along with the working memory of the global Info-map—allows isolation and integration of different experience types.
12. Volition
“Free will” is a philosophical dilemma; in AI, it is randomness or optimization; in neuroscience, it is determinism + noise.
MVAP: Volition is a function of awareness—the search for actions alternative to habitual ones in new conditions: fornit.ru/art11.
13. Genuine Volition and Creative Resolution of Internal Conflicts
Most AI systems make decisions either by optimizing an objective function or randomly. They lack an analog of “volitional effort” to suppress an immediate impulse in favor of a long-term goal, especially when goals conflict.
Mainstream approaches fail because:
MVAP: The essence of volition lies in the presence of an Interpreter of Comprehension (fornit.ru/69385) that manages activity priorities in the Infocontext. This hereditarily predetermined mechanism performs the function of seeking alternatives to habitual responses in novel conditions, using equally hereditary Infofunctions of the prefrontal cortex (fornit.ru/68522). The search occurs within the Infomap, which is updated at each iteration of comprehension with information from Infofunctions. The result is not an automaton but a system in constant connection with objective reality through current stimuli (fornit.ru/68516).
The Goal-setting and Comprehension Maintenance mechanisms allow sustaining a long-term goal in the conscious attention channel.
A conflict between, e.g., the Hunger Vital and the Safety Vital, is a normal system operating mode resolved through significance evaluation and compromise-seeking via Infofunctions.
A simple demonstration: the “Goal Persistence Task”: An agent must reach a goal (food), but along the path is a constantly active repelling stimulus (mild pain/danger). A mainstream agent either oscillates or abandons the goal. An MVAP agent demonstrates volitional “effort”: suppresses the avoidance reaction, finds a detour (creative solution), and reaches the goal, justified by the high significance of the Hunger Vital.
14. The “Free Will” Problem
Libet-style experiments show the brain decides before conscious awareness. This is interpreted as the absence of free will. Where, then, is conscious control?
Neuroscience is stuck in the dichotomy of “determinism vs. randomness,” lacking language to describe a third option.
MVAP provides a clear definition: a “voluntary action” is one found through the Cycle of Comprehension when a habitual Automatism is absent or unacceptable.
The Libet model records the moment a prepared motor pattern is launched (unconscious). But truly voluntary action begins earlier—with problem detection and Iteron launch.
The Interpreter of Comprehension is the control organ managing priority—what to be aware of and what problem to work on.
Subjective experience is based solely on the dynamics of global Infomap updating, because all other involved mechanisms are hereditary and cannot modulate the awareness picture themselves. Thus, in many cases, the solution search is largely complete before Infomap updating occurs.
One can simulate the Libet experiment. Show that for simple, prepared actions, decisions truly arise “before consciousness” (before Infomap update with the solution). But for complex, novel choices, the system shows an active comprehension phase with alternative enumeration—this is the neuroarchitectural basis of “free will.”
15. Creativity
Creativity is considered irrational and non-algorithmizable; insight is a random coincidence.
MVAP: Creativity is work with a Gestalt (dominant unresolved problem) in passive mode (fornit.ru/68279), where analogy + significance → insight: fornit.ru/69267.
16. Formation and Closure of “Unfinished Business” (Gestalts)
Psychology describes the phenomenon but does not explain the mechanism of storage, activation, and closure of an unresolved task.
MVAP: A Gestalt is a structure in long-term memory containing condition IDs, goal, and intermediate steps. It activates via analogy and closes upon goal achievement or conscious abandonment: fornit.ru/69108.
17. Gestalt Closure—Solving Complex Problems Through Insight
Modern AI can solve problems by brute force or template, but does not demonstrate the “aha!” moment when a solution to a complex, unstructured problem arrives suddenly after an “incubation” period.
LLMs may output correct answers, but this is statistical sampling, not an act of awareness.
Problem-solving systems enumerate options but do not form a “tormenting” unresolved problem as an active background process.
MVAP: Direct implementation of the Gestalt mechanism—memory of an unresolved but significant problem: fornit.ru/69108.
18. The Unconscious
Freud: mysticism; cognitivism: “subthreshold processing”; neuroscience: background activity without function.
MVAP: The unconscious consists of background comprehension cycles competing for Aten and becoming dominant upon reaching critical significance (insight): fornit.ru/68713.
19. Conscious Experience (Qualia)
The “Hard Problem of Consciousness” (Chalmers)—consciousness is fundamentally unknowable; panpsychism, quantum hypotheses (fornit.ru/69784, fornit.ru/69716).
Core issue: How do disparate signals from different sensory systems (color, shape, motion, sound) unify into a single, coherent perceptual image? And why is this process accompanied by subjective experience—“red,”“hot”?
Theories (e.g., “binding through 40 Hz synchronization”) remain speculative and do not explain how synchronization produces perceptual unity or sensory experience.
There is no transition from correlates (which neurons are active) to the essence of experience itself (Chalmers’“hard problem”).
MVAP: Qualia are the dynamics of Infocontext updating, where abstraction + significance = subjective experience. There is no “hard problem”—only the algorithm of awareness: fornit.ru/67865.
The “perceptual image” in the Dendrarch is a linked, holistic activation pattern formed as a reaction to a stimulus. This is not synchronization but the emergence of a new functional node. On the mental side, subjective abstractions and understanding models arise from such an image (fornit.ru/69260).
Qualia are not a mysterious property but the result of “Awareness of Significance” of this image—as information about it within the situational context. Significance is the “paint” of subjective experience—the signal of what this means to the system. No significance = no conscious experience, only automatic processing.
On an MVAP architecture, one can show how primitive sensory signals (analogous to V1, A1) form an “apple image” (red, round, crunchy), which then gains positive significance and becomes the conscious experience “I see a tasty apple.” One can show that without a significance contour—there is only blind data processing.
20. Neural Correlates of Consciousness (NCC) vs. Neural Causes of Consciousness
Neuroscience successfully identifies regions active during conscious perception (e.g., the posterior hot zone). But this is correlation. What is cause and what is effect? Where is consciousness “switched on”?
Methods (fMRI, EEG) detect correlations but cannot establish causal architecture.
There is no functional model explaining why consciousness is needed and under what conditions processing becomes conscious.
MVAP: MVAP replaces NCC search with a functional criterion of awareness. Consciousness “switches on” not in a specific location but when a condition is met: activation of the Orienant (novelty × significance) and launch of the Cycle of Comprehension (Iteron).
This explains why well-learned stimuli (e.g., the road home) are not consciously perceived—ready Automatisms exist. And why we consciously hear our name in a noisy room—high significance.
One can show how the same sensory signal sometimes triggers awareness (if novel and significant) and sometimes is processed automatically (if habitual). “Switching on” occurs at the system level, not in a specific “consciousness module.”
21. Role of Thalamocortical Loops in Conscious vs. Unconscious Processing
What fundamentally distinguishes neural activity linked to conscious perception from that in unconscious processing (e.g., priming or blindsight—objective visual detection without subjective awareness)?
There is evidence that consciousness requires global reverberation in thalamocortical networks, but no clear functional explanation of why this reverberation is needed.
MVAP: The Cycle of Comprehension (Iteron) is the functional analog of thalamocortical reverberation—a cyclic process of solution search, Infofunction enumeration, and memory access.
Unconscious processing is either Automatism operation (direct Dendrarch traversal) or background Iteron cycles that never reach the relevance threshold.
One can simulate the blindsight phenomenon. Show how damage to the “Perceptual Image → Awareness of Significance” channel leads the system to produce correct motor responses (via Automatisms through a separate channel) but without subjective experience or the ability to voluntarily describe the stimulus (no access to the Comprehension Cycle).
22. Building Strong AI with Autonomous Motivation
GPT is a statistical imitator; AI lacks its own homeostat, cannot set goals, and does not adapt to novelty without retraining.
MVAP: Strong AI is an artificial Egostat with Vitals, an orienting reflex, and a comprehension cycle. It can independently identify and solve problems. But instead of the complexities and limitations of biological implementation, logically optimized mechanisms and structures become possible, increasing system capabilities by orders of magnitude.
23. Operational Definition of Life, Independent of Biology
Life = metabolism + reproduction + evolution—descriptive, not operational.
MVAP: Life = functioning of an Egostat = maintenance of Vitals within norm via a hierarchy of adaptive mechanisms. Applicable to any substrate (bio, silicon, software). Every living being, from single-celled organisms, possesses its own homeostatic system with basic behavioral styles (feeding, exploratory, sexual, defensive).
24. Mirroring and Social Learning Without Reinforcement
Mainstream view: Imitation requires reinforcement or supervised learning.
MVAP: Mirroring is automatic alignment of another’s action with one’s own understanding model, forming a behavioral rule from a single observation. During trust-based learning and in states of complete naivety, mirrored experience is stored as an authoritative rule in historical memory.
25. Self-Reflection (Self-Awareness) as Part of Awareness
Self-awareness is a mystery; often reduced to mirror self-recognition.
MVAP: Self-awareness is Aten directed at the awareness process itself, enabled by an Infocontext containing a “Self” model as part of historical memory. A semantic self-model allows abstract self-contemplation and manipulation like any other understanding model (fornit.ru/70640).
26. The Problem of Hierarchical Behavioral Organization in Time
How does the brain organize simple actions into complex sequences (scenarios, plans), manage their execution, interruption, and resumption? This concerns how the brain builds, stores, initiates, pauses, and resumes complex action sequences—from “pick up a cup” to multi-step plans like “prepare dinner for guests.” Behavior over time is the basis of intentionality. Without hierarchical organization:
Modern AI, for example, cannot autonomously decompose the goal “cook pasta” into subgoals (boil water, find pot, add salt, etc.) unless explicitly trained.
Neuroscience approaches this from multiple angles, but no unified, accepted theory exists. Neuroscience describes components of hierarchical behavior but does not explain it as a holistic, dynamic, self-regulated process.
Reinforcement Learning (RL) models build sequences through reward chains but do not explain how internally motivated plans form without external reward.
Hierarchical RL (HRL) introduces “subgoals,” but they are manually specified or require complex meta-learning.
Cognitive architectures (ACT-R, SOAR) propose structures like “productions,” but they are static and do not explain dynamic abstraction-level switching.
Neurophysiological data show that prefrontal and hippocampal neurons encode abstract plan steps, but it’s unclear how these codes manage execution and recovery after interruption.
Key unresolved questions:
MVAP, with its Dendrarch, Gestalt, and Infofunction architecture, offers a radically different, neurostructure-independent answer.
All behavioral acceptability is organized through the state of the global Infomap—particularly information about mood, emotions, and the current image, which constitutes the theme of comprehension (fornit.ru/71308). Behavioral style components—curiosity, dissatisfaction with the status quo, social engagement, and especially the play context (fornit.ru/71364)—play a huge role in maintaining interest in the current theme.
While maintaining thematic and behavioral context, the system enhances perception of what is relevant, preventing it from interfering with the comprehension process—except for the most critical stimuli, which require interrupting current comprehension (while preserving it in a comprehension interruption stack for later resumption).
27. The Problem of Sleep and Its Functions
Why must the most complex system regularly enter an altered state of consciousness? What role does sleep play in memory consolidation, metabolite clearance, and significance reassessment?
Neuroscience offers no unified theory but views sleep as multifunctional. Neuroscience describes what the brain does during sleep (clears metabolites, replays patterns). MVAP, from its functionalist stance, proposes a hypothesis of why this is necessary for the adaptive system itself. Sleep is not a luxury or quirk but a mandatory system maintenance process—without which a hierarchy based on plasticity and significance cannot function stably.
Modern sleep function hypotheses fall into main groups:
MVAP, emphasizing adaptivity, homeostasis, and significance, must view sleep as a necessary system maintenance process for the adaptive architecture itself.
Why must consciousness be disabled? Why can’t “cleansing” or “consolidation” run in the background like liver function? The single channel of conscious attention processes all retained stimuli accumulated during wakefulness—the last of which is being comprehended. It cannot be interrupted. Only sequential hemispheric sleep (as in dolphins) or safe rest solves this.
What determines experiential priority for sleep processing? Why are some events “replayed” in sleep while others are not? Processing order is determined by comparative relevance of retained stimuli; dream narrative sequence follows historical memory traversal along significance extrema.
Throughout the day, intense formation and activation of Gestalts (unfinished tasks), Automatisms, and Images occurs. The significance system constantly evaluates thousands of stimuli. This leads to Dendrarch “fragmentation”—accumulation of weak, contradictory, or suboptimal connections—and significance system “noise”: many events receive temporary, situational significance not aligned with long-term adaptive value.
Function of Sleep: Reboot and Optimization. Sleep is a period when the system halts active world interaction (no new Orienants) and enters internal maintenance mode.
Memory consolidation in MVAP is Dendrarch “reassembly.” The process is not simple data transfer from hippocampus to cortex. It is an active process where:
REM Sleep and Dreaming = System “Testing” and “Defragmentation.” In this state, the system operates in “autonomous mode.” The Dendrarch generates internal activity without external stimuli.
Dreaming is a byproduct of a process where the system, via passive-mode comprehension algorithms, “replays” significant memory fragments, testing connections and seeking hidden patterns. This is analogous to a “stress test” or disk defragmentation—data are relocated for more efficient access.
The emotional component is the Significance System “reapplying” emotional tags independently of the original context—leading to emotional adaptation.
MVAP does not merely list functions but shows that sleep is an inseparable part of the adaptive system’s life cycle. Without sleep, the system degrades, as its core structures (Dendrarch, Significance System) become “cluttered,” lose efficiency, and daytime experiences that lacked time for comprehension are lost forever.
Sleep is necessary because deep reorganization processes are incompatible with unified Awareness operation. Active Awareness (Iteron) requires a stable, non-contradictory Infocontext—but during sleep, this context is actively rebuilt.
All known sleep functions (consolidation, cleansing, emotional regulation) derive from one main task: maintaining the adaptive architecture’s operability.
28. The Problem of Affect and Mood
How does a diffuse, prolonged state (mood) differ from a brief emotion (point 8), and how does it globally modulate information processing, decision-making, and conscious content?
Neuroscience describes this distinction through different timescales, neurochemistry, and brain networks involved.
Emotion engages external attention networks: the Salience Network (insula, ACC) for detecting significant stimuli and the Central Executive Network (CEN) for organizing responses.
Mood is a state of the Default Mode Network (DMN): increased DMN activity (medial PFC, precuneus) correlates with self-rumination, obsessive thoughts, and negative affect. Mood is the background on which other networks operate.
Transition Problem: How does a brief neurochemical surge (emotion) transition into a prolonged shift in system tone (mood)? This is a question of kindling (“priming”) and plasticity of regulatory circuits themselves.
Causality Problem: We see correlations (low serotonin → depression) but do not fully understand the initial cause of the system tone shift.
Subjectivity Problem: Why does the same neurochemical profile (e.g., low dopamine) cause apathy in one person and anxious novelty-seeking in another? Neuroscience cannot strictly answer this without invoking individual experience and significance.
In MVAP, Homeocontext is the combination of active needs. Mood can be interpreted as a generalized, tonic Homeocontext.
Emotion (see point 8) is the conscious experience of significance dynamics—an abstract reflection of behavioral style combinations (feeding, exploratory, sexual, defensive, etc.). It results from the “Awareness of Significance” mechanism evaluating a specific event.
Mood (tonic Vital state) globally modulates the entire system via significance thresholds.
Mood is the background, tonic level of Vital activity, generalized by the Difznacher mechanism (fornit.ru/70332). It is not a reaction to an event but the system’s current baseline state.
Mood forms as the long-term averaged result of system operation. After each trial conscious action, the system enters a period of consequence monitoring and tracks Difznacher changes, allowing evaluation of (un)success and storage of this significance assessment in historical memory for future predictions.
Neuroscience describes mood as a complex neurochemical background. MVAP gives it a strict functional definition: mood is the derivative of the tonic state of the Vital system, which globally modulates the entire adaptive mechanism through significance threshold shifts. This explains why, in a bad mood, the world seems hostile and decisions are made more cautiously and pessimistically—because the very “metrological system” for evaluating events is tuned to a negative bias.
Each of these problems:
Thus, MVAP offers not philosophical speculation but an engineering foundation for creating living, conscious, creative artificial systems.
Most important elements and mechanisms of individual adaptivity revealed in MVAP theory:
Conclusion
MVAP is not fragmented but constitutes an integrated system of interconnected elements: fornit.ru/71392.
All mechanisms are implementable in any computational environment without neuron emulation.
The shift from speculation to engineering is a rare and valuable quality in consciousness theories.
The model does not reject but integrates modern concepts: predictive brain, allostasis, neural correlates of consciousness, glymphatic system, etc. It goes further: embedding these findings into a functional architecture, explaining causality—not just correlations.
2025.11.2