The Mirage of Innate Modules
When I first read Steven Pinker’s The Blank Slate, I found myself captivated by the idea that the genes could somehow code for complex, even subtle, patterns of human behavior. It was thrilling; an elegant story in which evolution had carved our minds into specialized tools, each tuned for survival in ancestral environments. But something about that picture never sat comfortably with me.
What kind of mechanism could make that leap: from a sequence of base pairs coding for proteins to the wiring of neural networks capable of the intricacies of human language, complex emotions like jealousy, humor, or moral outrage? It felt like a vast and unacknowledged jump in complexity. Genes could certainly shape an eye, already a miracle of evolutionary engineering! But an eye, for all its intricacy, is a relatively stable structure. A brain is a storm of connections, billions upon billions of synapses forming and dissolving, learning on the fly. The idea that such a system could be hard-wired for specific social modules struck me as implausible. For a long time, I wondered if my skepticism was simply a failure of imagination.
Only later did I realize that this discomfort was my first glimpse of what I would later call mapping complexity. The chain of causation from genes to behavior is not a line but a labyrinth, winding through development, self-organization, and experience. The genome can shape the conditions for learning, but it cannot dictate the final network any more than a seed can specify the exact shape of a tree.
The early brain, far from being modular, is almost too connected, an overgrown forest of synapses waiting to be thinned. It learns by pruning. Evolution, it seems, solved the problem of flexibility not by prewiring specific behaviors but by producing a substrate rich in potential and cheap to refine.
The genome contains roughly three billion base pairs, a staggering number until you realize how little information that actually is compared to what would be required to specify the detailed wiring of a human brain. Each neuron can form thousands of connections, and there are tens of billions of neurons. Even if each synapse could somehow be assigned by explicit genetic instruction (which it cannot) the informational bandwidth of DNA would fall short by orders of magnitude.
Evolution had to find a different strategy. It could not write a wiring diagram; it could only write the rules for wiring: local constraints, gradients, feedback loops, and molecular signals that guide development without dictating the outcome. These rules allow a brain to construct itself through interaction, first within the womb and then within the world.
Seen this way, the apparent miracle of the human brain is not that it is pre-programmed for complexity but that it starts out so excessively connected. During early development, the infant cortex contains far more synapses than it will ever keep. The process of becoming an individual mind begins not by adding connections, but by losing them. Synapses that prove useful, those that participate in coherent patterns of activity, are strengthened, while others quietly retract.
Pruning is nature’s great sculptor. It is cheaper, faster, and more adaptive to remove what does not work than to specify what must. The brain’s initial exuberance of connections is an evolutionary investment in possibility, and pruning is the return on that investment. The result is a network tuned not by genetic fiat but by experience, with the environment itself serving as teacher.
From this perspective, “innate modules” are not prewired structures but stable outcomes of developmental dynamics. What looks like a specialized circuit for language or face recognition may be the product of selective pruning under the influence of early exposure. The genome provides the growth medium; interaction with the world provides the sculpting hand.
One of the strongest arguments for innate modularity comes from the apparent geography of the brain. Certain functions tend to appear in the same places across individuals: language in the left hemisphere, face recognition in the fusiform gyrus, visual processing in the occipital lobe. To many evolutionary psychologists, this consistency seems decisive, proof that evolution installed specialized modules whose blueprints are written in our genes.
But regularity does not imply design. Hierarchical systems, by their very nature, tend to organize themselves into similar forms whenever they develop under similar constraints. River deltas, tree branches, and lung airways all converge on the same branching patterns not because they were designed that way, but because the physics of flow and efficient distribution makes branching the stable solution.
The hierarchical layers of cortex are not a random tangle; they are ordered by the logic of information flow. Signals arriving from the sense organs climb upward through a structured hierarchy, from simple features to complex abstractions. At each step, new regularities emerge and are preserved because they are useful, efficient, and energetically stable.
If the same kind of hierarchy unfolds in every human brain, and the same sensory organs feed it, then similar patterns of specialization will naturally emerge even without genetic blueprints for their exact location. Function follows flow. Circuits for vision must sit where visual input arrives, circuits for hearing where sound does, and circuits that integrate both where their pathways intersect. Hierarchical distance, not physical distance, determines where these abilities settle.
The visual system is a perfect example. The eyes are in the front of the head, yet their cortical projection lies at the back. To a casual observer this seems inefficient, as though evolution took a wrong turn. But the cortex did not start from scratch. It grew around older midbrain structures that already handled vision. As cortical tissue expanded, it did so posteriorly, wrapping new layers around the primitive visual nuclei. The occipital lobe, far from the eyes in space, is close to them in hierarchy. It is the first cortical station in a long chain that transforms photons into perception.
This is not a sign of modular design but of developmental geometry. Long axons are metabolically expensive, but high bandwidth justifies the cost when it allows the brain to unfold its layers efficiently. The posterior pole offers the largest available sheet of cortical real estate for high-resolution mapping. Once this arrangement emerged, it became a developmental attractor: gradients of growth and connectivity reliably recreate it in every new brain. What repeats is not a coded plan, but a self-organizing pattern stabilized by the physics of development.
Modern neuroscience offers an even more striking case of this convergence. Primates, including humans, have two fusiform regions specialized for recognizing faces. But in literate humans, one of them (typically on the left) has been repurposed into the visual word form area, responsible for recognizing letters and printed words. Reading, however, is an invention only a few thousand years old, far too recent for evolution to have written a new module into our genes. The transformation of a face-recognition circuit into a reading circuit (a typeface recognition circuit) happened through experience alone, repeated anew in every child who learns to read.
Its consistent location across individuals tells us nothing about genetic preprogramming and everything about hierarchical constraint. To recognize words, the system must receive high-resolution visual input (hence its proximity to the early visual cortex) and connect to phonological and semantic networks (hence its position near language areas in the left hemisphere). The same geometry and the same data streams produce the same outcome: a cultural adaptation expressed through the flexibility of a biological substrate. The brain did not evolve a reading module; it evolved a general-purpose sheet of cortex that can be trained into one.
Recognizing typefaces and human faces even rely on similar computations. Both require rapid pattern recognition in a two-dimensional field, scanning for specific spatial relationships among a limited set of distinguishing features. In each case, the cortex learns to detect the faintest differences in configuration (the distance between eyes or the spacing between letters) and to treat them as meaningful. The shared geometry of these tasks explains why they converge on the same cortical territory.
The reproducibility of cortical maps, then, is an illusion of design created by lawful self-organization. Hierarchy channels development into familiar attractors, just as gravity channels rivers into valleys. What we see as innate structure may simply be the landscape of constraints through which experience always flows.
Once we recognize how reproducible specialization can emerge from shared constraints, a deeper picture of the brain comes into view. The cortex is not a mosaic of purpose-built modules but a continuous sheet of adaptive tissue, a self-organizing substrate tuned to learn whatever patterns the world presents.
Evolution’s true innovation was not in scripting discrete functions but in shaping an architecture that could invent them. Its achievement was a kind of meta-design: a structure that structures itself. Each cortical region begins with roughly the same six-layer anatomy and similar microcircuits. What differentiates them is the data they receive and the tasks they are trained to perform. Experience sculpts function the way a river carves terrain, deepening some channels and abandoning others until a stable network of flow emerges.
The face and typeface example captures this perfectly. Both tasks demand the rapid recognition of subtle spatial relationships: the spacing of eyes and mouth in one case, the spacing of lines and curves in the other. Each involves scanning a two-dimensional configuration for a small number of discriminative features whose combination conveys identity or meaning. It should be no surprise that the same cortical neighborhood, honed for detecting delicate topographies of shape, can serve both masters. The left fusiform gyrus, already wired for pattern recognition at high visual resolution, simply learned a new dialect of the same visual language.
This adaptability echoes what we now observe in artificial systems. Transformer models, trained on text, learn to parse syntax, reason by analogy, and even imitate emotional tone, all from exposure, with no modules pre-assigned for those tasks. Their architecture, like the cortex, is a general-purpose substrate: layers upon layers of learned abstraction. The resemblance is more than superficial. Both systems begin overconnected and overparameterized, and both arrive at efficiency through pruning, weakening or discarding connections that fail to contribute to stable prediction. What remains is a hierarchical resonance tuned to the statistical structure of its world.
In this light, the cortex’s apparent specializations: vision, language, music, mathematics, are emergent equilibria, not innate domains. They are the steady states that form when a general-purpose learning engine interacts with a structured environment. The brain’s organization is less like a Swiss Army knife and more like a sheet of clay: given enough exposure, it can be shaped into whatever tools culture requires.
Evolution, in short, built flexibility. It produced a machine capable of discovering structure on its own, within the bounds of energy, geometry, and experience. Every infant brain begins as an overgrown forest of connections, and through the gentle violence of pruning, it becomes a landscape, distinct, efficient, and uniquely adapted to its environment. The result is not a collection of pre-fabricated modules but a self-tuning hierarchy of resonances, shaped by the very world it comes to know.
The picture that emerges from all this could hardly be farther from the modular mind of classical evolutionary psychology. The genome does not contain blueprints for behaviors or circuits; it encodes the rules for self-organization and the parameters of plasticity. Evolution’s triumph was not to predefine the contents of thought, but to construct a system capable of discovering them. What natural selection perfected was trainability.
And what trains it is not merely the environment, but culture. Culture is the world’s memory of its own learning, an externalized neural network storing the adaptive patterns of countless generations. Each child’s brain is initialized by evolution but tuned by immersion in this vast reservoir of accumulated structure. Every language learned, every story told, every imitation observed is a transmission of pattern from one mind to another. Through this continuous process, the species as a whole evolves on a timescale far swifter than genes could ever manage.
Seen this way, the supposed modules of the human mind are not genetic relics but cultural attractors: stable equilibria that reappear wherever brains grow up under similar conditions. They are the echo of training, not the imprint of design. The same forces that carve a river valley into the earth carve conceptual valleys into the cortex: patterns reinforced by use, deepened by repetition, and transmitted by imitation.
This perspective reframes the entire question of innate structure. The regularities we see across brains do not prove that evolution prewired specific behaviors. They reveal something subtler and grander: that evolution produced an architecture so elegantly constrained that culture could teach it almost anything. In the space of a few millennia, writing systems colonized visual cortex, music exploited auditory and motor loops, and abstract thought built recursive towers atop the same neural scaffolding that once tracked prey or recognized faces. Evolution did not predict these developments; it built a brain that could accommodate them.
When I first read The Blank Slate, I thought the failure of imagination was mine, that is, my inability to see how genes could possibly generate minds. Now I think the failure belonged to those who underestimated what happens when a hyper-plastic hierarchy meets the informational flood of experience. Six months of sensory exposure, a year of interaction, a lifetime of culture. These are epochs in the developmental history of an individual mind. Across generations, culture is evolution, sculpting the same substrate anew each time.
Evolutionary psychology sought to locate human nature in a catalogue of modules. But the deeper story is that our nature lies in the absence of such limits. We are not born with a fixed inventory of cognitive tools; we are born with a system that can make tools out of anything. The brain’s genius and evolution’s greatest invention is not specialization, but the power to learn whatever the world requires.