The Architecture of Meaning: How Hierarchy Collapses into a Network
Open a dictionary at random and look up a word, for example: river. The entry explains it using other words: a natural stream of water flowing toward a sea, lake, or another such stream. Each of those words has its own entry, defined again in terms of still others. If you trace these links outward, you find chains of definitions that branch, merge, and sometimes loop back upon themselves. Meaning, in a dictionary, lives not inside the words themselves but in the pattern of relationships between them.
A dictionary is a static record. It is a map of meaning rather than a history of how meaning is learned. It captures the end state of many interlocking conceptual structures without preserving any trace of the process by which those structures came to exist. It is a network of relationships compressed onto a printed page.
Human concepts arise the same way but through experience rather than editorial labor. We do not learn river by memorizing a definition. We accumulate examples that resemble one another: the cupped flow of water in a stream, the widening channel before a lake, the pull of a current. These experiences combine into a concept that did not exist before. And that concept, in turn, becomes a building block for newer and more abstract concepts. Over time, each idea is constructed from many others through a layered sequence of integrations. This is the mind’s hierarchy. It is a real structure, built step by step from the bottom up, and it retains the sequence in which each step originally occurred.
Yet the mind does not preserve this elaborate construction history. If it tried to maintain the full hierarchy in explicit form, the structure would grow unmanageably deep. Every new concept would require referencing the entire chain of earlier concepts that contributed to it. This recursive expansion would eventually overwhelm both memory and computation. The historical sequence of learning is useful for forming concepts but not for storing them. Once an idea becomes stable, the path that produced it no longer needs to be maintained in detail.
Instead of storing the full hierarchy, the brain collapses it into a more efficient format. Redundant patterns across experiences allow the mind to compress many similar episodes into a single representation. Once that compression occurs, the emergent concept stands on its own within a flattened network of other concepts. The hierarchical history that produced it recedes, leaving a tightly connected web in its place. This collapse is a necessity, even though it discards much of the historical structure that produced the concept. The collapse is not universal. In perceptual hierarchies, where transformations are computationally manageable and the depth is limited, the mind can maintain explicit layers for direct use. But language and higher-level abstraction introduce recursion, allowing conceptual structures to grow without bound. It is in these domains, where depth becomes intractable, that the hierarchy must eventually give way to a compressed network. The hierarchy is expensive because it grows by recursion, each abstraction built from earlier abstractions. The network is economical because it retains only what is relevant for interpreting future experience. The hierarchy is the process. The network is the product. Meaning lives in the product even though it could not have formed without the process.
Compression depends on redundancy. The mind cannot form the concept of river from a single observation the way it can form a food aversion or a reflexive association. One-time learning can occur, but it does not produce rich conceptual structure. Robust concepts emerge when many experiences share enough similarities that the underlying pattern can be extracted. Redundancy is the raw material from which the mind distills invariants. Those invariants form the stable concepts that populate the associative network.
Once a concept joins this network, it interacts with others not as a historical pathway but as a node in a relational space. This space is what machine-learning researchers call a latent space, a compressed internal geometry in which distance reflects similarity and connection reflects shared structure. The brain’s latent space is not geometric in the mathematical sense, yet it performs a similar function. It organizes meaning into a landscape where related ideas sit near one another and where activation can spread quickly along relevant pathways.
This network provides speed and efficiency. It also provides coherence, allowing concepts to be retrieved in the right contexts and combined in flexible ways. The cost of this efficiency is that the full developmental path is lost. The mind no longer remembers how it constructed a concept. It retains only the stable residue that matters for recognition, interpretation, and use. Yet this trade-off is exactly what allows meaning to be both stable and expandable. The network can grow without becoming unwieldy because each new concept is integrated into a structure that stores relationships rather than histories.
Human cognition demonstrates this pattern clearly, but artificial neural networks reveal the same principle through a different medium. A large language model is trained on an immense hierarchy of patterns drawn from text. During training, the model must integrate millions of local regularities, building layer upon layer of abstraction. But after training, the internal structure collapses into a compressed lattice of parameters. The model retains no record of the specific steps through which each abstraction was formed. It preserves only the relationships that proved stable under repeated exposure. The developmental path has vanished, leaving behind a network ready for rapid activation.
This convergence is not an accident. It reflects a constraint imposed by the physics of information. Any system that must learn from vast amounts of structured input while remaining finite in capacity will tend toward compression. A hierarchy is the natural route by which structure is discovered. A network is the natural format in which a mature system stores what it has learned. Minds and machine-learning systems arrive at similar representational strategies not because they share ancestry or architecture, but because they are both solutions to the same underlying problem: the need to extract stable patterns from an overwhelming stream of complexity.
Within this compressed network, meaning has a static and a dynamic aspect. The static aspect is the stored structure itself, the settled arrangement of concepts and their associations. The dynamic aspect is the pattern of activation that flows across that structure in context. When we hear a word or consider an idea, only a small region of the network becomes active, guided by attention, memory, and relevance. This spreading activation resembles the reader moving through a dictionary: following a selective path through a fixed landscape. The network remains stable while its local activations change. Meaning is the interaction between the two.
The dictionary captures this idea in miniature. It is the network without the learning. It preserves the relationships that define meaning but not the developmental history behind them. The brain does the same on a much larger scale. It builds concepts through a long chain of experiences, compresses them, and then stores the compressed structure for future use. The hierarchy disappears, and the network remains. Meaning survives as the compressed memory of its own formation.
In this sense, meaning is not a thing inside a concept but a place within a network. It is not defined by a single experience but by the stable relationships that endure after many experiences have been distilled. The brain’s great achievement is not its depth of hierarchy at any single moment, but its ability to collapse that depth into a relational structure that can be navigated quickly and flexibly. Meaning is the architecture that emerges from this collapse: the durable shape of what remains after learning has done its work.
This, then, is the architecture of meaning. A hierarchy builds the concept. A network preserves it. And the collapse from one into the other is what allows a finite mind to understand an infinite world.