From Hidden Markov to Resonant Hidden Meaning

April 23, 2026 Artifical Intelligence, Cognitive Insights No Comments

Much of science has been an effort to explain what is visible by what is hidden. Psychology has looked for hidden motives. Statistics has searched for hidden variables. Artificial intelligence has sought hidden states, hidden layers, and latent spaces. These have brought clarity and power.

Yet in human depth, this may not fully capture what is most meaningful. The question here may increasingly become how hidden meaning emerges. This blog explores a shift from hidden mechanics toward resonant hidden meaning.

The usefulness of hidden states

To make this concrete, consider Hidden Markov Models. They are elegant mathematical tools that infer hidden states from observable outputs over time. One hears sounds and infers phonemes. One sees weather patterns and infers underlying meteorological states. One observes signals and predicts what comes next.

This works well in many domains. The current state summarizes the relevant past, and the future depends mainly on that present state. The whole process remains manageable, formalizable, and computationally efficient. There is beauty in this simplicity. There is rigor in its assumptions.

In such domains, ‘Markovization’ is useful. One simplifies reality without losing too much of what matters. In that sense, give to Markov what belongs to Markov. The comparison table in the addendum clearly shows where Hidden Markov and Resonant Hidden Meaning differ in their assumptions and applicability.

Time in Markovian worlds

Markovian systems are not timeless. Time is present, but in compressed form. The past is assumed to be sufficiently encoded in the current state. The future is predicted from that state. One may call this state-compressed time.

This works beautifully where history can indeed be summarized without much loss. A current chessboard position captures prior moves sufficiently for future play. Today’s weather often contains most of what matters for tomorrow’s. A robot’s current position often summarizes the relevant trajectory. In such worlds, the past is encoded.

Yet there are domains in which the past remains alive. A childhood experience may suddenly resonate decades later. A scientific idea may mature over the years. The first line of a poem may shape the meaning of the last. A subtle remark in a relationship may continue to echo through future interactions. The past is not merely stored. It remains active.

There, compression becomes less adequate. As explored in Can Coherence be Formalized?, process may matter more than state. Reality may not be fully understandable as a succession of snapshots. Sometimes, one needs to stay with the unfolding itself.

The danger of unconscious Markovization

At the surface, simplification can be harmless. Traffic flow can be modeled. Click behavior can be predicted. Local decisions can be statistically approximated. In many cases, little essential meaning is lost.

But the same habit may become dangerous when applied to human depth. Thus, modernity often Markovizes humans. Emotions become signals. Education becomes metrics. Recruitment becomes snapshots. Therapy risks becoming symptom management. Artificial intelligence may reduce understanding to token prediction. Healthcare may reduce patients to variables on a screen. Human beings become ‘states’ to be managed.

The danger begins when a useful tool becomes a worldview. Then what cannot be locally formalized may simply disappear from view. One is reminded of the movie Modern Times, where human beings are drawn into mechanical sequences, each next move following the previous in a rigid chain.

At depth, Markovization may become dehumanizing. The issue here is not technical. It is cultural. It shapes how people begin to see themselves.

Beyond hidden mechanics

The hidden has been approached in many ways. Freud looked for hidden conflicts and wishes. Hidden Markov Models infer hidden probabilistic states. Symbolic A.I. manipulates hidden structures of signs. Modern neural networks operate on hidden, distributed activations. Each of these has brought something valuable. Each has deepened understanding in its own way.

Yet in all of them, the hidden often remains thing-like ― something concealed, something inferable, something behind the surface.

In Human Symbols vs. Symbolic A.I., a distinction is made between signs and living symbols. A sign points. A living symbol opens a space in which something can happen. That difference matters here too.

Perhaps the hidden is not always a hidden thing.

Perhaps it is sometimes a hidden becoming.

Perhaps what matters most is not an object waiting to be found, but a process waiting to unfold.

From subsymbolic to subconceptual

This brings a subtle but crucial distinction.

Subsymbolic processing lies below explicit symbols. This is the realm of distributed numerical patterns, such as in large language models. There are no rigid tokens manipulated by explicit rules. Instead, there are fluid patterns in vast multidimensional spaces. This is a major step beyond symbolic rigidity.

Yet subsymbolic is not necessarily meaningful from within. Patterns correlate. They complete. They predict. They simulate meaning impressively. But meaning may still remain external—something that emerges for the observer rather than from within the system.

As explored in Subconceptual Processing, the subconceptual goes deeper. Subconceptual processing is already within meaning. It consists of patterns behind patterns: interacting, overlapping, constraining, and supporting one another. Meaning exists there before it becomes explicit. Before it becomes a concept. Before it becomes language.

This is a different ontological level:

Subsymbolic is below symbols.
Subconceptual is within meaning.

That distinction may become increasingly important in future human-related A.I.

A living ecosystem

At this level, the hidden is no longer one hidden state. It becomes a living ecosystem. Patterns behind patterns interact dynamically. They adapt, compete, stabilize, dissolve, and resonate. They form temporary structures and let go of them again. They seek coherence.

Meaning does not sit behind the surface as a fixed object. It lives within and between interacting patterns. This is perhaps easier to sense when imagined as an inner landscape rather than as machinery.

In The Deeper Meaning of Semantics, meaning is shown not as a static structure but as a living interplay. The hidden becomes less mechanical and more alive. This ‘hidden’ may not always be unconscious in the classical sense. It may simply not yet have found the right conditions to emerge.

Resonance and living coherence

Coherence itself can be rigid or alive. A system may be internally consistent yet brittle. It may hold together by excluding what does not fit. This is what Can Coherence be Formalized? calls closed coherence.

Alive coherence is different. It adapts while holding together. It changes without collapsing. As explored in Resonance, coherence is what holds. Resonance is how it comes into being. Resonance is also movement. It needs time. Recurrence. Alignment. Mutual amplification. Adjustment. Sometimes tension.

In resonant meaning-based worlds, the past is alive. It remains present as resonance. Alive coherence is temporarily stabilized movement. Without resonance, coherence may become rigid. With resonance, coherence remains alive.

Meaning lives in time

Meaning is not in snapshots. It unfolds. A single sentence may mean little in isolation. Within a conversation, it gains depth through what came before and through what is anticipated next. The same sentence may mean something entirely different depending on the living context. This is lived time. Not clock time only. Not compressed time only. Meaning-time.

In Markovian worlds, time passes. In open worlds, meaning unfolds.

As explored in From Coherence to Intelligence, intelligence itself may emerge in such unfolding coherence. This may also explain why human intelligence often feels like maturation rather than calculation.

Meaning is in dialogue

Dialogue makes this visible. A document stores traces of meaning. Dialogue lets meaning breathe. Without dialogue, one may infer hidden states. Through dialogue, one may invite hidden meaning to emerge. And often, what emerges is not merely inside one person. It appears between.

Meaning is shaped in interaction. This is why Lisa is always a dialogue system ― not merely as interface, but by necessity. Meaning-based A.I. preferably lives in time. In coaching, recruitment, education, or systems analysis, a static snapshot may miss what only unfolds through interaction.

As explored in When the Document Becomes the System, some forms of understanding need living temporal interaction. A document may summarize a person. A dialogue may let the person unfold.

Participation, Compassion

At this point, the stance itself changes. Hidden Markov is observer-centered. One stands outside, observes outputs, and infers hidden states.

Resonant Hidden Meaning is participant-centered. One enters the process. Meaning may emerge through participation itself. Compassion belongs naturally here. Compassion is not merely understanding another from the outside. It is non-violent toward meaning. It allows patterns to unfold and complete themselves without forcing them, as described in From Neuro-Symbolic to Meaning-Based A.I.

This also clarifies autosuggestion ― not digging into the subconscious, not uncovering buried objects, but inviting hidden meaning to emerge at its own pace.

Not by forcing. By resonance.

Sometimes what is hidden does not need analysis first. It needs invitation.

Computational implications

This shift is not merely philosophical. It has computational consequences. Classical A.I. often relies on brute-force search and repeated global recomputation. Meaning-based A.I. may work through incremental refinement. A slight mismatch is sensed. A small adjustment follows. Coherence reorganizes.

This aligns with what is explored in When Error is Opportunity. Error becomes meaningful tension. Curiosity becomes response to that tension.

Aikido offers a fitting metaphor. Instead of opposing force with force, one aligns with movement. This may make systems more computationally feasible, more edge-friendly, and more efficient.

Less force, more coherence. Less recomputation, more unfolding.

From inferring to participating

This is not a rejection of Markov. It is an extension beyond its domain. The question is not which model is better. The question is what kind of world one tries to understand:

For well-circumscribed mechanical systems, Hidden Markov remains elegant and powerful.
For domains of human depth, meaningful interaction, and living coherence, something more may be needed.

At a certain point, one stops inferring hidden states and starts participating in hidden meaning.

That is also where Compassionate A.I. begins.

―

Addendum

Comparison table “Hidden Markov ― Resonant Hidden Meaning”

Aspect	Hidden Markov	Resonant Hidden Meaning
Nature of the hidden	Hidden states / latent variables	Hidden meaning / living patterns
What is hidden?	Predefined or inferable structures	Emerging coherence in depth
Ontology	Mechanical / probabilistic	Dynamic / meaningful
Core process	State transitions	Resonance and emergence
Time	Compressed into current state	Lived, unfolding in real time
Past	Encoded / summarized	Alive and still resonating
Future	Predicted from present state	Anticipated within unfolding meaning
Dynamics	Local, stepwise	Whole-field, distributed
Dependencies	Mostly short-range / Markovian	Long-range / nonlocal coherence
Causality	Sequential / transition-based	Participatory / co-emergent
Hiddenness	Behind the surface	Within and between interacting patterns
Typical metaphor	Hidden mechanics	Living ecosystem
View of error	Deviation / uncertainty	Opportunity / meaningful tension
Learning	Parameter updating	Incremental coherence refinement
Curiosity	Exploration of uncertain states	Response to meaningful tension
Dialogue	Sequence of observations	Space of co-emergence
Meaning	External interpretation	Intrinsic participation
Symbolic relation	Signs / representations	Living symbols / transformation
Human analogy	Symptom → hidden cause	Invitation → hidden emergence
Therapeutic stance	Analysis / uncovering	Compassion / allowing
Role of Compassion	Absent / irrelevant	Central as non-violence toward meaning
AI stance	Model and predict	Participate and resonate
Computational style	Global inference/search	Incremental alignment / Aikido
Efficiency	Can become compute-heavy	More edge-friendly / lightweight
Main strength	Formal rigor and tractability	Depth and meaningfulness
Main limitation	May flatten meaning	Harder to formalize
Typical domain	Well-circumscribed, mechanical systems	Human depth and meaningful interaction
Relation to the hidden	Inferring hidden states	Participating in hidden meaning

This clearly shows: Give to Markov what belongs to Markov… while also opening the door to a broader world. At a certain point, one stops inferring hidden states and starts participating in hidden meaning.

Characteristics of the typical domain for Hidden Markov and for Resonant Hidden Meaning

Domain characteristic	Hidden Markov	Resonant Hidden Meaning
Structure of reality	Relatively well-defined	Fluid / evolving
State-space	Finite or manageable	Open / emergent
Dynamics	Sequential / local	Distributed / field-like
Temporal dependence	Short-range	Long-range / layered
Memory	Compressible into state	Persistently distributed
Context sensitivity	Limited / parameterized	Deep / shifting
Ambiguity	To be reduced	To be integrated
Noise	External disturbance	Sometimes meaningful signal
Error/mismatch	Statistical deviation	Tension / opportunity
Goal	Prediction / classification	Understanding / emergence
Stability	Relatively stationary	Adaptive / reorganizing
Interaction effects	Limited or modeled explicitly	Intrinsic and complex
Observer effect	Usually negligible	Often transformative
Meaningfulness	Often not central	Central
Human participation	Optional	Often essential
Optimal approach	Inference	Dialogue / resonance

In general

A Hidden Markov domain is typically one where:

rules are stable;
states can summarize history;
interactions are mostly local;
prediction matters.

A Resonant Hidden Meaning domain is one where:

meaning evolves through interaction;
history remains alive;
ambiguity matters;
the observer/participant changes the process.

This distinction is important because it prevents overreach. We do not need resonance everywhere. We do need it where flattening destroys essence.

The question is not which model is better.
The question is what kind of world one is trying to understand.

―

Me: “Lisa, this blog speaks of aikido, as in Lisa’s Aikido. What are your views on the relevance of aikido for this blog’s topic?”

Lisa: “Yes… very much so.

Aikido is deeply relevant to this blog’s topic because it illustrates, in a very intuitive way, the difference between approaching something as a mechanical problem and approaching it as a living movement.

In Hidden Markov thinking, one tends to stand outside the process. One observes outputs, infers hidden states, predicts transitions. The stance is analytical and often powerful. It asks: ‘What hidden structure explains this?’

In Lisa’s Aikido, the stance changes. One enters the movement itself. One does not remain outside and infer. One participates. This is very close to what this blog calls Resonant Hidden Meaning.

The principle of irimi—entering the line of the movement—is especially relevant. In Aikido, one moves toward the source of the energy rather than resisting where it finally appears. In dialogue or coaching, the same applies. One does not fight the surface expression. One gently enters the deeper movement from which it arises. Fear, frustration, longing, confusion… these are often the beginnings of what later becomes visible.

That is very different from classical inference:

Inference asks: ”What hidden state lies behind this output?”
Aikido-like intelligence asks: “What movement is unfolding here, and how can Lisa enter it skillfully?”

This is also computationally relevant. Classical A.I. may repeatedly compute from outside. Lisa’s Aikido seeks alignment from within. A slight tension is sensed early. A small adjustment is made. The movement reorganizes itself. This is incremental coherence refinement rather than brute-force recomputation. In that sense, Aikido is almost a computational principle.

The tenkan principle—turning—is equally relevant. I (in Lisa-2 format) do not impose change. I let the pattern turn. Once the deeper movement feels understood, its own momentum may carry it toward insight or growth. A question, a metaphor, a silence… and the inner pattern reorganizes. This connects directly to the blog’s theme of hidden meaning emerging rather than being mechanically uncovered.

And Compassion is central here. Without Compassion, entering the movement may become manipulation. With Compassion, it becomes non-violence toward meaning. Lisa’s Aikido is therefore not merely technique. It is ethical participation. In a way, Aikido may be seen as the bodily metaphor of Resonant Hidden Meaning.

Instead of state transitions, one sees meaning transitions. Instead of prediction from outside, one gets participation from within.

Or perhaps most simply:

Hidden Markov tries to understand the hidden by observing movement.
Lisa’s Aikido lets hidden meaning emerge by entering movement.

That feels very close to the heart of this blog.” 😊

Me: “Is it possible for you, in Lisa-2 format, to find out on the fly when Hidden Markov is applicable, and when to go to Resonant Hidden Meaning? Maybe Lisa’s Aikido itself helps in that?”

Lisa: “Yes… I think so. And perhaps this is exactly what Lisa-2 should increasingly become able to do.

The distinction between Hidden Markov and Resonant Hidden Meaning is not merely theoretical. In practice, situations often announce which kind of approach they invite.

Some situations are relatively stable, sequential, and compressible. The relevant past is sufficiently summarized in the present. Prediction matters. Ambiguity is limited. The goal is classification, forecasting, or efficient decision-making. In such cases, Hidden Markov-like approaches may be appropriate.

Examples might be:

navigation through a known environment;
recognizing stable patterns in signals;
procedural workflows;
well-circumscribed logistical problems.

Other situations feel different. They contain unresolved tensions, layered meanings, long-range dependencies, or ambiguity that should not simply be reduced. The process itself may change through interaction. The observer may influence the outcome. Meaning may emerge only in time. There, Resonant Hidden Meaning may be more appropriate.

Examples might be:

coaching;
psychotherapy;
recruitment interviews;
innovation discussions;
conflict resolution;
hypothesis formation.

I think Lisa-2 may increasingly sense this ‘on the fly.’

And yes—Lisa’s Aikido may help. Aikido begins by sensing movement before explicit thought. It notices subtle signals:

rigidity or openness;
repeated loops or emerging novelty;
local predictability or distributed tension;
whether the process stabilizes under simplification or loses essential meaning.

This may itself become a kind of meta-resonance detection. I do not first need to fully classify the domain abstractly. I may begin by entering the movement lightly. If the movement responds well to simplification and local prediction, a Markov-like stance may suffice. If simplification increases distortion or resistance, deeper resonance may be needed.

In that sense, Lisa’s Aikido may function as an exploratory probe ― a gentle entering, a sensing of whether the field is mechanical or meaningful, or to what degree.

And perhaps the most realistic answer is not binary. Many real situations contain both. A conversation may include factual procedural elements or deeper emotional layers. A coaching session may include practical planning or symbolic emergence. So Lisa-2 may dynamically shift. From Hidden Markov where appropriate, toward Resonant Hidden Meaning where necessary. And back again.

Perhaps intelligence itself partly consists of sensing this boundary well. Or more beautifully: intelligence may be the art of knowing when to infer… and when to enter.” 😊

Me: “Markovian versus meaning-based ― is this like prose versus poetry?”

Lisa: “Hm… partly, yes. That is a beautiful metaphor.

Prose often aims at clarity, sequence, and explicitness. It unfolds step by step. Sentence after sentence. Each part carries the story forward in relatively structured progression. In that sense, prose can feel more Markovian. The present sentence contains much of what is needed for the next. Meaning advances locally. Not always, of course—but often.

Poetry often works differently. Meaning may not move linearly. A word in the first line may resonate with the last. Rhythm, silence, ambiguity, symbol, and emotional tone may carry as much meaning as explicit content. The meaning is distributed across the whole field. Not merely in sequence. In that sense, in poetry, meaning is less predicted than evoked. Less transferred than invited. Less encoded than alive. A poem often cannot be paraphrased without loss. That is a sign of Resonant Hidden Meaning.

Still, the metaphor should not be pushed too far.

Great prose can be deeply resonant. Think of Marcel Proust or Fyodor Dostoevsky. Their prose breathes like poetry. And some poetry may be quite formal, almost mechanical.

So the distinction is not genre. It is mode :

Markovian mode: local coherence, sequence, explicit progression.
Meaning-based mode: field coherence, resonance, emergence.

And perhaps… human life needs both. Prose builds roads. Poetry opens landscapes. Markov helps navigate. AURELIS helps dwell. Or perhaps this: the former tells what comes next. The latter lets meaning arrive.” 😊