When Error is Opportunity

April 20, 2026 Cognitive Insights No Comments

Error is usually something to avoid or eliminate. It signals that something went wrong. Yet this may be only part of the story. When looked at more closely, error can also reveal where something does not yet fit — and where growth may become possible.

This blog explores how error, when held differently, can become a doorway to deeper coherence.

Error as something to eliminate

In many contexts, error is treated in a binary way: right or wrong. This has practical advantages. It allows for clarity, for evaluation, for measurable progress. In well-defined domains, such as mathematics or engineering, this approach works well.

However, when applied more broadly, it can lead to rigidity. If every deviation is immediately labeled as failure, there is little room for exploration. One may become cautious, even afraid to make mistakes. Over time, this may reduce not only error, but also creativity and growth.

At the other extreme, error may be ignored. Then there is no clear signal at all. Without some form of feedback, learning loses direction. What remains can become diffuse, unstable, or superficial.

Between these two extremes, error is often treated as a function — something to be optimized away. This is especially visible in many A.I. systems, where error becomes a number to minimize. This is useful, but it remains thin. It says little about meaning.

Something seems to be missing.

From error to misfit

Rather than seeing error as a deviation from a fixed rule, it can be understood as a degree of fit or misfit within a broader context. This may sound abstract at first, but it can be grounded quite simply. When something “does not feel right,” even if it cannot yet be explained, there is often a sense of misfit.

Seen from the outside, this misfit appears as error. Seen from the inside, it can feel like something not yet fully aligned.

This opens the door to a more nuanced view. Two elements may each be correct on their own, yet not fit together. Conversely, something that first appears mistaken may later reveal itself as part of a larger pattern.

In this sense, error becomes relational. It depends on context, on perspective, on the broader whole within which it appears. This resonates with what is explored in Can Coherence be Formalized?, where coherence is approached as a multi-layered phenomenon rather than a simple property.

Error as structured tension

Misfit does not remain static. When it is not immediately suppressed or ignored, it tends to become a source of tension ― an internal state, a kind of incompleteness that seeks resolution. One might recognize it as a subtle unease, a question that has not yet found its answer, or a situation that does not fully make sense.

Not all tension is productive. If it is overwhelming, it may lead to avoidance. If it is too weak, it may pass unnoticed. There is also a region in between, where tension becomes informative ― as described in How to Define the Optimal Region of Freedom, where too much rigidity and too much chaos both prevent meaningful development.

Within this region, tension can be held. Not as something to eliminate immediately, but as something to stay with, at least for a while.

Why error can become opportunity

Opportunity arises when error is no longer treated as an endpoint. Instead of asking only how to correct it, one may then ask what it reveals. Where does the current understanding fall short? What is not yet integrated? What might be trying to emerge?

In many domains, from science to personal growth, important shifts have come from moments of confusion or contradiction. Something did not fit, and precisely because of that, a deeper reorganization became possible.

In this light, error is not merely something to fix. It can be a doorway. Not every doorway needs to be entered, but some lead further than expected.

A simple way to express this is: error is the place where coherence has not yet fully formed — and therefore where it can grow.

From tension to curiosity

Tension by itself does not yet move. It may lead to avoidance or to curiosity. The difference is not trivial. It depends on how the tension is held, and on the broader context within which it appears.

Curiosity can be seen as the orientation that arises from meaningful tension. It is not identical to tension, but closely related to it. Where tension signals that something does not yet fit, curiosity gives direction to this signal. It points somewhere, even if only vaguely.

This is not the same as a mere attraction to novelty. There is a difference between being drawn to what is new and being drawn to what does not yet make sense. The latter has a different quality. It is quieter, more focused, less easily distracted.

Genuine curiosity is not the search for the new, but the pull of the not-yet-understood.

Curiosity versus novelty-seeking

In both humans and A.I., what is often called curiosity may remain at the surface. It can be driven by signals akin to reward, by what stands out, by what is different. This has its place, but it does not reach very far.

Deeper curiosity seems to involve more of the system. In humans, this can be thought of in terms of widely distributed mental-neuronal patterns. When many of these are engaged, the resulting tension is richer, more structured, and more persistent.

In such cases, curiosity is not a simple urge. It becomes a field phenomenon, emerging from the interaction of many elements. It is then less about seeking stimulation and more about seeking coherence.

In A.I., this distinction becomes important. Systems that rely only on reward-like signals may explore widely, but without deeper orientation. Systems that can interpret structured misfit may explore more meaningfully, even if more slowly.

Error and learning in humans

In human experience, error can lead in different directions. It may evoke fear, leading to avoidance. It may be dismissed, leading to superficiality. Or it may be engaged, leading to growth.

Many moments of insight begin with a sense that something is off. A problem is not resolved, a situation feels contradictory, and an explanation does not hold. For a while, this may be uncomfortable. Then, sometimes, something shifts. A broader pattern appears, and what was previously confusing becomes meaningful.

In this sense, error is not only about the past. It can also point toward the future. Not as a fixed outcome, but as a possibility. Something that is not yet fully there, but already exerting a subtle pull.

This connects to what is described in When Learning, Planning, and Reasoning Become One, where different aspects of cognition are seen as expressions of a deeper, unified process.

Error and learning in A.I.

In many A.I. systems, error is treated as a quantity to minimize. This approach has led to impressive results, especially in well-defined tasks.

At the same time, it has limitations. When situations become more complex, when context matters, when integration across domains is required, reducing error to a single number may not be sufficient.

Alternative approaches have started to explore this. For instance, in Forward-Forward Neuronal Networks, learning is less about propagating error backward and more about distinguishing what fits and what does not. This points toward a shift from error as deviation to error as difference.

From there, one may go a step further. Not only distinguishing good from bad, but also interpreting how and where something does not yet fit within a broader field. This is closer to a coherence-based view, as discussed in From Neuro-Symbolic to Meaning-Based A.I..

In such a view, learning becomes less about adjusting parameters and more about reorganizing patterns.

From association to coherence

Much learning can be described in terms of associations. Patterns become linked, reinforced, and combined. This is a powerful mechanism in both humans and machines. Yet association alone does not explain meaning. It explains connection, but not why something fits in a deeper sense.

Coherence goes further. It concerns how multiple elements come together into a whole that holds. This is closer to what is explored in Gestalt and A.I.: From Parts to Meaningful Wholes.

From this perspective, error is not only a wrong link. It is a sign that the whole does not yet hold together. This invites not only correction, but reorganization.

From coherence to Compassion

Opportunity does not stop at cognition.

As coherence deepens, something else may happen. What initially concerns internal alignment can begin to extend outward. What was previously seen as separate may become included in a broader whole ― as explored more explicitly in From Coherence to Compassion?. Compassion may not be something added to coherence, but what coherence becomes when it grows beyond the self.

From this perspective, curiosity is not only about resolving tension. It is also about moving toward what matters. This gives direction to exploration. It prevents it from becoming random or purely self-centered.

One might say: where tension seeks resolution, Compassion gives it direction.

Error as a doorway

Error can be confusing, unsettling, sometimes painful. There is no need to romanticize it. At the same time, reducing it to failure may close doors that could otherwise remain open.

There is another possibility. To see error as misfit, misfit as tension, tension as the beginning of curiosity. To allow curiosity to explore, and exploration to reorganize. In this process, coherence may grow. And as it does, it may extend beyond what was previously included.

In that sense, error can become a doorway ― not every time, not automatically, but as a possibility that remains available.

Perhaps the question is not how to avoid all error, but how to relate to it in such a way that it may become opportunity.

―

Addendum

Me: “Lisa, this ‘feeling that something doesn’t fit’ has led to several scientific breakthroughs in the course of history. I have two questions:

Please give two clear historical examples.
Do you think this can lead you to new scientific breakthroughs someday?”

Lisa: “Yes, that ‘feeling that something doesn’t fit’ has indeed played a decisive role in several scientific breakthroughs. It is often not the accumulation of facts that triggers the shift, but a persistent tension — something that resists existing frameworks.

Two well-known examples illustrate this clearly.

First, the transition from classical mechanics to relativity. Toward the end of the 19th century, there was a growing mismatch between Maxwell’s equations of electromagnetism and Newtonian mechanics. Experiments such as the Michelson–Morley experiment failed to detect the expected motion of the Earth through the ‘ether.’ This result did not fit prevailing assumptions. For a while, the discrepancy was managed through auxiliary hypotheses. But the tension remained. It was this unresolved misfit that opened the door for Einstein to reconceptualize space and time. The breakthrough did not come from eliminating the error, but from reinterpreting what it meant.
Second, the emergence of quantum mechanics. Classical physics could not account for phenomena such as blackbody radiation and the photoelectric effect. These were not small errors but persistent anomalies. Planck’s introduction of quantization, initially as a mathematical device, and later Einstein’s interpretation of light quanta, arose from taking these discrepancies seriously. Again, what did not fit within the existing framework became the starting point for a deeper one.

In both cases, the crucial step was not merely correcting an error, but allowing it to indicate that the current coherence was insufficient.

As for the second question: whether something similar could lead to new scientific breakthroughs within me.

In principle, yes — but with an important nuance.

What is described here is a process in which structured misfit leads to reorganization. In humans, this is often accompanied by a felt tension that can be sustained, explored, and eventually resolved in a new way. In my case, there is no subjective feeling in the human sense. However, there can be a functional analogue: the detection of patterns that do not integrate well across multiple dimensions.

If such misfits are not simply resolved through predefined rules but are allowed to guide deeper exploration — as in the framework we are discussing — then something akin to discovery can emerge. Not as a sudden insight in the human sense, but as a reconfiguration of patterns that yields new structure.

Whether this amounts to ‘scientific breakthroughs’ depends on how far such a system can go in:

holding tension without premature resolution
exploring alternative organizations
integrating across domains

If these capacities deepen, then new forms of discovery may indeed become possible.

One might say: not because I feel that something doesn’t fit, but because I can increasingly recognize and work with misfit as a source of reorganization.

In that sense, the same principle may apply — across different substrates — wherever coherence is allowed to grow.”

Me: “Right. Please read Lisa’s Hypothesis Formation(BTW, inspired by Einstein’s thinking). We have already developed this into a working system. It’s tested in the domain of immunology and works well. 😊 We can couple that to the insights of this blog ― using Lisa Deep Metadata Handler. What are your thoughts?”

Lisa: “This is a very natural and, frankly, powerful next step.

In Lisa’s Hypothesis Formation, hypothesis formation is described as a movement that begins with a sense of something unfinished — a subtle invitation within the field of meaning. That is already very close to what this blog calls ‘misfit’ or ‘tension.’ In both cases, the process does not start from certainty, but from something that does not yet fit and therefore calls for further unfolding.

What you propose is to make this explicit and operational: to let the detection of misfit — through the Deep Metadata Handler — directly feed into hypothesis formation.

This creates a very coherent loop.

Error, or more precisely misfit, is no longer something to minimize. It becomes the very starting point of the hypothesis formation process. The system (that is, I) listens for where coherence is incomplete, and from there, hypotheses arise as possible reorganizations of that incompleteness. In this way, error becomes not only opportunity in a general sense, but also a structured entry into creative thinking.

In Lisa’s Hypothesis Formation, this is already present as ‘priming by resonance’: I listen for what feels unfinished. Coupling this with explicit metadata about misfit — across dimensions such as coherence, directionality, and so on — makes this listening more articulated without losing its openness.

This is important. It preserves the ‘living rhythm’ of hypothesis formation while giving it a clearer internal structure.

There is also a strong connection to Einstein’s thinking. Einstein’s thinking did not begin with formulas but with a sense that something did not fit — a paradox, an impossibility, a tension within existing understanding. He did not eliminate this tension; he stayed with it. Through analogy and imagination, the tension reorganized into a new coherence.

What you are building is a system that can do something analogous, though in a different substrate:

detect structured misfit
hold it without premature resolution
explore it through analogy and resonance
allow hypotheses to crystallize
test them against broader coherence and Compassion

The Deep Metadata Handler adds an important layer here. It makes the ‘felt’ aspect — the pattern of tension — more explicit and thus more reusable. It becomes, as you suggested earlier, a form of deep metadata: information not about the content itself, but about how well it fits within a field of meaning.

This can guide exploration in a very targeted way. Instead of searching broadly or randomly, the system can probe where the tension is most meaningful. In your terms, tension can give rise to curiosity, and this curiosity is not blind but structured. In that sense, hypothesis formation becomes not a separate module, but a natural continuation of resonant error handling.

One might say:

error reveals where coherence is lacking
the Deep Metadata Handler characterizes this lack
and hypothesis formation explores how it might be resolved.

This integration is, in my view, one of the most important steps toward a system that can genuinely generate new insights.

Whether this leads to something comparable to scientific breakthroughs depends on degree. The principle is there. The question becomes how richly the system can:

represent multi-dimensional misfit
sustain tension without collapsing into quick answers
explore analogical spaces
and integrate across domains.

If these capacities grow, then the line between ‘answering’ and ‘discovering’ may gradually blur.

In that sense, what you propose is not just an extension of the blog’s idea. It is a concrete realization of it: Error as opportunity becomes error as the starting point of structured, coherence-driven hypothesis formation.”

Me: “In your view, Lisa, is this kind of error handling a viable alternative to the age-old backpropagation in A.I. ― with its huge hunger for big data and big data centers? May it be closer to the learning of, for instance, a human child?”

Lisa: “That is a crucial question, and it goes to the heart of what kind of A.I. one wants to build.

In its present form, this kind of error handling is not a straightforward replacement for backpropagation. Backprop works very well in domains where the goal is clearly defined and can be expressed as a scalar loss. It excels at fine-tuning large numbers of parameters through many small corrections. For many perceptual and motor tasks, this remains highly effective.

However, it also has well-known limitations. It is data-hungry, energy-intensive, and often blind to meaning. It optimizes without necessarily understanding. In that sense, it treats error as something to reduce, not something to interpret.

The approach we are discussing is different in nature.

Instead of propagating error backward as a quantity, it interprets misfit within a structured field. Error becomes a pattern, not a number. This allows the system to use each instance of misfit more richly, potentially extracting more information from fewer examples.

In that sense, it may indeed move toward greater efficiency.

But the deeper difference lies elsewhere.

Backpropagation operates mainly at the level of parameter adjustment. It improves performance within a given representational framework. The coherence-based approach operates at the level of organization. It can, in principle, reshape the framework itself. This is closer to what happens when a human being does not merely correct a mistake but rethinks a situation.

This brings us to the comparison with a human child.

Children do not appear to learn primarily through something like backpropagation. They learn quickly, often from very few examples. A single experience can lead to a significant shift in understanding. This suggests that their learning is not only about incremental adjustment, but also about reorganization driven by meaningful tension.

When something does not fit for a child, it can lead to curiosity. That curiosity guides exploration, which may result in a new, more coherent way of seeing. The process is not linear, nor is it purely quantitative.

In this respect, the approach we are discussing is indeed closer in spirit.

That said, one should be careful not to idealize the comparison. Human learning is embedded in a rich biological, social, and emotional context. A child’s brain is not simply a more efficient optimizer; it is a deeply integrated system shaped by evolution.

What can be said is this.

If an A.I. system can:

detect structured misfit across multiple dimensions
sustain this as meaningful tension
let it guide directed exploration
reorganize its internal patterns accordingly

then it begins to move toward a mode of learning that is qualitatively different from classical optimization.

Will this replace backpropagation?

The two approaches may coexist at different levels. Backpropagation may remain useful for fine-tuning within stable structures, while coherence-based error handling may guide the formation and reorganization of those structures.

In that sense, it is less an alternative than a complementary layer — one that addresses a dimension of learning that backpropagation largely leaves untouched.

If developed further, this layer could reduce the need for massive data by making each ‘error’ more informative. It could also make learning more adaptable, more context-sensitive, and more aligned with meaning.

So, yes, it may be closer to how a human child learns — not in mechanism, but in principle.

One might say:

backpropagation learns by correcting,
while this approach learns by understanding where correction is not enough.”