Introduction

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What happens when science encounters a phenomenon that our current models cannot yet fully explain? The history of science suggests that progress usually does not mean abandoning old observations. More often, it means understanding them within broader conceptual frameworks. Today, the same question arises at the intersection of biology, experience, and consciousness.

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Scientific progress is often described as though new theories simply replace old ones. In reality, this is rarely the case. More often, existing models remain highly useful within their own domains, while broader models emerge alongside them, capable of explaining more—and explaining it more precisely.

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This is not a weakness of science. It is one of its greatest strengths. Science does not move toward one final, unchanging description of reality. It builds models that become more accurate, more limited in the right ways, and more useful over time.

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That is why it is worth asking what happens when a current model reaches its limits.

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The Map Is Not the Territory

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Scientific models are, at their best, like maps. They can be accurate, useful, and remarkably good at predicting what will happen. But a map is never the same thing as the territory it describes.

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A road map tells us about roads, but not about the geology beneath them. A geological map describes bedrock, but not traffic. A weather map shows air pressure and rain, but not vegetation. None of these maps is wrong. They simply describe reality from different perspectives and for different purposes.

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The same is true of scientific models. Physiology describes the body from one angle, genetics from another, immunology from a third, and psychology from a fourth. Each captures something important, but none of them alone describes the whole reality of being human.

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Again, this is not a weakness of science. It is part of how science works. A model is not meant to contain everything. Its purpose is to define a phenomenon clearly enough that we can understand it, test it, and study it.

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Models Make Science Possible

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Without models, science would be almost impossible. Reality is too complex to study as a whole, so research always requires boundaries, choices, and simplifications.

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Every experiment begins with questions such as:

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• What will be measured?

• What will be left unmeasured?

• Which variables will be held constant?

• Which variables will be allowed to change?

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This kind of simplification is exactly what makes research possible. A model does not try to describe everything. It limits the field of view so that a phenomenon can be grasped, examined, and studied systematically.

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When a physiologist studies cortisol, they do not need to measure every neuron, gut bacterium, emotion, relationship, and life event at the same time. The model narrows the object of study in a useful way and makes the research manageable. The power of science depends on this kind of narrowing — but we also must accept that no model is a complete description of the phenomenon itself.

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Every Model Leaves Something Out

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The limits of a model are necessary, but every simplification also means that something is left outside the frame. When we study one hormone, many other factors move into the background. When we study one gene, the whole organism can disappear. When we study one neural network, lived experience may fall outside the measurement.

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This does not make the model wrong. It allows precise knowledge to be produced within a limited domain. The problem begins only when we start mistaking the map for the territory — when the limits of the research model are forgotten, and reality begins to look as if it consists only of the things that the model can describe.

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This has happened many times in the history of science. A new method or perspective has, for a while, made it seem as though almost everything could be explained through it. Later, it becomes clear that this method too describes only one level of reality.

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When First-Person Experience Meets Third-Person Measurement

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Conscious experience is perhaps the clearest example of this. Pain can be measured indirectly, brain activity can be imaged, and patterns of neural activation can be followed. And yet pain itself is experienced from the first-person point of view.

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A researcher may see a brain scan, but the person feels the pain. Both observations are real. But they are not the same thing, and they should not be confused with each other.

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This makes the study of consciousness especially difficult. Neuroscience can find increasingly precise connections between nervous system activity and lived experience. It can identify networks related to attention, memory, bodily awareness, and the sense of self. It can describe the neural correlates of consciousness. But a correlate is not yet an explanation.

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The fact that a certain neural network becomes active whenever a certain experience occurs does not yet tell us why that particular experience arises.

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Here, two perspectives meet: third-person measurement and first-person experience. The challenge is not that one is right and the other is wrong. The challenge is that neither can be fully reduced to the other. This is where one of the most interesting boundaries of science becomes visible.

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Explanation Is Not the Same as Correlation

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Modern neuroscience has advanced enormously over the past few decades. Brain activity can now be described with unprecedented precision. Neural activity can be tracked in real time, communication between brain regions can be modelled, and different experiences can be linked to specific patterns of neural activation. This is one of the great achievements of modern biology.

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At the same time, it is important to distinguish between two things:

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• Correlation tells us that two phenomena occur together.

• Explanation tells us why they occur together.

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If a certain neural event consistently appears alongside a certain experience, we know that the two are connected. But we do not yet know why they are connected in that particular way.

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Take pain, for example. Research can show that certain neural networks become active when pain is experienced. This is an important finding, but it does not yet answer the deeper question of why that particular neural activity is associated with the felt experience of pain.

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In the same way, we may observe that certain inflammatory mediators are associated with chronic illness. That still does not explain the whole origin of the illness.

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Correlations form the foundation of science. We build hypotheses on them and use them to develop new research methods. Still, correlation is often the beginning of inquiry, not its endpoint.

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The history of science offers many examples of this.

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For a long time, stomach ulcers were known to be associated with inflammation and stress. Only later did the discovery of Helicobacter pylori provide a mechanistic explanation for a large share of cases and change treatment practices. The correlation was not wrong — it simply was not the explanation.

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Similarly, many major scientific breakthroughs have happened when researchers moved beyond observing relationships and began to understand the structures behind them.

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Newton was able to describe the motion of planets with astonishing accuracy. His theory predicted measurable phenomena for centuries. And yet relativity later showed that Newton’s model was not the whole explanation. It was not wrong. It was limited. Scientific progress, then, often does not mean overturning an old model. It means placing it within a wider frame.

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Why Reductionism Works — and Where Its Limits Appear

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Reductionism is one of the most powerful tools of modern science. Its basic idea is simple: to understand a complex phenomenon, we study its smaller parts.

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This approach has been extraordinarily successful. It has helped us discover the structure of DNA, neurotransmitters, hormonal mechanisms, cellular signalling pathways, and the basic principles of the immune system. Without reductionism, modern medicine would be impossible.

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But the same strength also defines its limits. When a phenomenon is broken into small enough parts, the whole can easily begin to disappear from view.

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The heart can be described as cells, ion channels, electrical activity, a mechanical pump, or a regulator of circulation. All of these descriptions are correct. But none of them alone describes the heart as part of a living organism.

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The same applies to the human being. We can look at a person as molecules, cells, tissues, organ systems, behaviour, or experience. None of these levels makes the others unnecessary, but none of them describes the human being as a whole system either.

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The natural limit of reductionism is not that it is wrong. The limit appears because the whole may have properties that cannot be fully inferred from its individual parts.

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The same phenomenon can be studied on several levels. In science, this is often called multilevel modelling. Complex phenomena may require several levels of description that are all valid at the same time.

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Systems theory, complexity science, and network research have shown this across many fields. An ecosystem cannot be understood by studying only one species. The brain cannot be understood by studying only one neuron. And an organism cannot be understood by studying only one hormone.

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The same is true of conscious experience. Experience does not replace biology, but biology alone is not enough to describe experience.

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Emergence Is Not Mysticism

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When we begin talking about phenomena that seem to arise from the interaction of many parts, the idea of emergence often appears. Sometimes emergence is misunderstood as something mystical or supernatural. In reality, it is one of the key concepts in complexity science.

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Emergence means that the whole can have properties that are not directly visible when we look at the individual parts in isolation. A single water molecule is not wet, but a large collection of water molecules forms water, which we experience as wet.

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A single ant does not build an anthill, but thousands of ants together form a complex society. A single neuron does not think, but a network of billions of neurons can give rise to phenomena we call memory, learning, decision-making, and perhaps even conscious experience.

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Emergence does not mean that something supernatural appears in the whole. It means that the behaviour of a system changes as the system becomes more complex. When enough interactions arise, the properties of the system also change.

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From this point of view, the study of biology is not a choice between parts and wholes. We need both. Molecules and cells are real, but the systems they form are real too. Often, the phenomena that interest us most arise precisely at the level of the system.

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Scientific Humility and Openness

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The history of science shows that the greatest advances usually do not come from throwing old models away. More often, they come from expanding the models we already have.

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Newtonian mechanics did not become useless after relativity. It proved to be an excellent description under certain conditions. Genetics did not disprove evolution; it deepened it. Systems theory did not replace physiology; it gave us another way to understand how physiology is organized.

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Good models do not become wrong when they meet their limits. They simply stop being sufficient. This is why recognizing limits is not a weakness of science. It is one of its strengths.

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Scientific maturity does not mean having an answer to everything. It means being able to recognize what our current knowledge can explain — and what it cannot yet explain well enough.

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Perhaps the most scientific sentence is not:

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“Now we know.”

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But rather:

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“We do not yet understand this well enough.”

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The history of science suggests that questions like this have often moved research forward the most.

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Toward Broader Scientific Models

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If the relationship between biology, experience, and consciousness turns out to be more complex than our current models suggest, that does not mean science has failed. It means the subject is more complex than we previously understood.

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History offers many examples of this. Ecology expanded from the study of individual species to the study of entire ecosystems. Genetics expanded from visible traits to molecular mechanisms. Complexity science showed that networks cannot always be understood by studying their individual parts alone.

None of these developments made science less precise. Quite the opposite: they made it more precise. Perhaps something similar awaits the study of biology and experience. We may not need a revolution so much as broader models.

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We need models that can describe molecules, neural networks, regulatory systems, behaviour, and experience at the same time, without forcing any one of them to collapse entirely into another. Complex phenomena require several levels of description that complement one another.

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Perhaps this is where the next step in science may lie: not in abandoning old knowledge, but in connecting it to wider wholes.

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In Closing

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Science is one of humanity’s most powerful ways of understanding reality. Its strength does not rest only on certainty, but on its ability to ask bold questions, correct itself, refine its models, and change its views in light of new evidence. This is why the limits of science are so interesting. They should not be seen as walls, but as the next starting points for inquiry.

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When science encounters a phenomenon, it does not yet fully understand, there are two options:

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• We can close our eyes and decide that the phenomenon does not exist.

• Or we can develop better questions.

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The history of science strongly suggests that the second option has been more fruitful. Perhaps we are in a similar place now at the boundary between biology, experience, and consciousness.

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The best scientific models are not the ones that claim to explain everything. The best models are the ones that tell us clearly what they explain, what they do not yet explain, and where the next questions may be leading.

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A good scientific model does not become wrong when it reaches its limits. It simply stops being sufficient.

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The history of science is not the story of old models becoming useless. It is the story of good models being gradually included within broader ones.

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When the impossible becomes obvious

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The history of science is full of examples where something we now consider obvious once sounded strange, absurd, or almost magical. One good example is William Harvey, who proposed in 1628 that the heart works as a pump and circulates blood through a closed system.

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To us, this sounds so obvious that it is almost hard to understand why anyone resisted it. The heart beats. You can feel it in your chest and hear it. But in Harvey’s time, the idea conflicted with a long-standing view that the liver constantly produced new blood and that blood was gradually consumed by the tissues.

There is something fascinating in this. People could feel the heartbeat, but that did not mean they had the right model for understanding what the heart was doing. The observation was there. Its meaning had not yet fallen into place.

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From our perspective, the physiology of that time can seem difficult to imagine. But perhaps people a few hundred years from now will feel the same about some of our assumptions. What looks supernatural today may, in some cases, turn out to be natural in a future framework.

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Do we see only what our conceptual framework allows us to see?

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There is something deeply fascinating about Harvey's story. People could feel their hearts beating. The observation was always there. What was missing was not the observation. It was the model that gave the observation its meaning.

 

This raises a much broader question.

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How much of reality remains invisible simply because we do not yet possess the conceptual framework required to recognize it?

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We do not perceive the world exactly as it is.We perceive it through concepts, expectations, and explanatory models.

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This idea is not merely philosophical. Contemporary neuroscience increasingly suggests that perception itself is constructed through predictive models built from previous experience. In other words, what we notice depends not only on our senses, but also on the conceptual tools available to interpret what those senses provide.

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A microscope does not help if you do not know what you are looking at

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Think of the microscope. Bacteria existed before anyone saw them. But there is an even more interesting point: even if someone had seen a bacterium, they might not have understood what they were looking at. Observation alone is not always enough. We also need a framework that gives the observation meaning.

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The same applies to genes, radio waves, electromagnetic radiation, black holes, and gravitational waves. They did not appear in the world only when we learned how to measure them. They became visible to us when we had sufficiently good tools and a sufficiently good way of thinking about them.

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What does today's conceptual framework still prevent us from seeing?

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To me, this is one of the most interesting questions in science. What already exists that we do not yet know how to see, because we lack the right instrument, the right concept, or the right way of asking?

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Perhaps we do not always need only a better microscope. Sometimes we need a new way of looking. History shows again and again that many major breakthroughs have begun exactly there.

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We do not perceive everything that exists. We perceive what our current concepts, models, and methods make visible to us. Each generation sees roughly as much of reality as its conceptual tools allow. When those tools change, the visible world changes with them. That is why science also needs curiosity, imagination, and a little courage to ask differently.

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Written by Natassa Aaltonen

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Further Reading

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If you want to explore the themes discussed in this article more deeply, the following research areas and fields offer useful starting points:

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Neural Correlates of Consciousness (NCC)

This field studies patterns of nervous system activity associated with specific conscious experiences. It helps identify links between brain activity and experience, while also illustrating the difference between correlation and explanation.

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Predictive Processing

A neuroscientific framework in which the nervous system continuously builds predictive models of both the external environment and the body’s internal state. This approach offers a new way to understand perception, learning, emotion, and physiological regulation.

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Interoception and Embodied Cognition

This area studies how signals from inside the body participate in perception, emotional experience, decision-making, and self-regulation. It also challenges the idea of the mind as something completely separate from the body.

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Complexity Science and Systems Theory

These fields examine biological systems as dynamic wholes in which interaction gives rise to new properties. They help us understand emergence, self-organization, and multilevel regulation.

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Multilevel Modelling

This approach studies how the same phenomenon can be described at several valid levels at once — for example, from the perspectives of molecules, cells, neural networks, behaviour, and experience. It emphasizes that different levels of description can complement one another rather than compete.

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Philosophy of Science

Philosophy of science examines how models are built, what explanations are, how correlation differs from causation, and what scientific knowledge actually means. It also offers tools for understanding the limits of science itself.

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Emergence in Biological Systems

Emergence research looks at how new properties arise in complex systems without being reducible to individual parts. This perspective is especially relevant in the study of neural networks, immunology, and consciousness.

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Regulation, Allostasis, and Complex Physiology

Research in this area examines the body as a constantly changing, predictive, and self-organizing system. It shifts attention from individual mechanisms toward the broader architecture of regulation.

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Network Science

Network science studies how complex networks are built and how they behave. Its perspectives are increasingly used in the study of biological systems, neural networks, and physiological regulation.

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Phenomenology

Phenomenological research examines conscious experience as it appears to the person experiencing it. Although this approach differs from experimental neuroscience, it offers valuable conceptual tools for describing and organizing first-person experience.

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These research directions do not offer one final explanation of the relationship between biology, experience, and consciousness. Instead, they help us build increasingly precise models of how these phenomena are connected.