Why Every Brain Metaphor in History Has Been Wrong [SPECIAL EDITION]

Science operates by simplifying complex reality, but this necessary act raises a fundamental question: have we found a deep truth about the world, or are we mistaking our simplified model for the actual thing? This tension is embodied by the “spherical cow” joke in physics and is central to modern neuroscience and AI. As Professor Mazviita Chirimuuta explains in her book, The Brain Abstracted, we are limited creatures who must build models and leave things out. The critical disagreement, however, is what this success implies about reality itself.

This can be framed as a conflict between two perspectives:

• Simplicius: Believes that science works because the universe is fundamentally simple and orderly underneath its apparent complexity. An elegant equation reflects reality.
• Ignorantio: Argues that we simplify because we are cognitively limited. Our models are useful fictions—maps, not the territory—that work for our specific purposes, which doesn’t prove that nature itself is simple.

Chirimuuta aligns with “learned ignorance” (docta ignorantia), the idea that true learning includes understanding the limits of what you know.

The Kaleidoscope Hypothesis: Is Reality Fundamentally Code?

Francois Chollet proposes the “kaleidoscope hypothesis,” suggesting that beneath the messy surface of reality lies an intrinsic, underlying structure composed of simple, repeating “atoms of meaning.” Much like a kaleidoscope creates infinite complexity from a few pieces of colored glass, the world is generated by the repetition and composition of these fundamental elements. Intelligence, in this view, is the process of mining experience to extract these abstractions.

Chirimuuta frames this not as a scientific certainty but as a philosophical bet, akin to Plato’s theory of Forms. It’s a wager that “real reality is neat, mathematical, and decomposable” beneath the complicated world of appearances.

The Ultimate Metaphor: Is the Mind Software?

The most pervasive simplification today is the idea that the mind is a computer running software. This has moved from a metaphor to what many consider a literal truth. Joscha Bach argues provocatively that this is not a metaphor at all: “Software is spirit.” He posits that abstract patterns, like software or money, have real causal power, independent of their physical substrate. A program produces the same effects whether on a Mac, a PC, or potentially even neurons, because the causal power lies in the invariance of the pattern itself.

The counterargument is that this “sameness” is not inherent in nature but is imposed by a human observer. Physically, completely different things are happening inside different computer chips. The invariance exists only in our description. The causal power of money, for example, isn’t in the paper or electrons but in the shared social agreements and interpretive practices of humans. The critique is that this view mistakes an elegant description for the fundamental structure of reality.

Historically, our metaphors for the brain have always tracked our most advanced technology:

• Descartes: Hydraulic pumps in French royal gardens.
• 19th Century: A telegraph network.
• 20th Century: A telephone switchboard.
• 21st Century: A digital computer.

As Jeff Beck bluntly states, “It will always be the case that our explanation for how the brain works will be by analogy to the most sophisticated technology that we have.”

Ontology vs. Metaphysics: It Depends on Why You’re Asking

Professor Luciano Floridi offers a framework to navigate this, distinguishing between metaphysics (reality as it is in itself, which is inaccessible) and ontology (the structure we impose on reality for a specific purpose). Our models of the world are not absolutely true or false; their value is relational.

Is it the same ship of Theseus? The question is a mistake. It provides no interface, what computer scientists call a level of abstraction chosen for one particular purpose… If it is the tax man asking, you’re doomed, man… It’s the same ship. I don’t care. But if it is a collector, that ship is worth zero.

The question “Is the universe computational?” is similarly meaningless without context. However, modeling it as such can be incredibly useful for making sense of our digital lives as “informational organisms.” The error is forgetting that it’s a model—a fallacy of misplaced concreteness.

Prediction vs. Understanding

A critical trade-off has emerged in science, particularly with the rise of powerful AI models: the conflict between prediction and understanding. Nobel laureate John Jumper offers a clear distinction:

• Predict: To state a future value or measurement.
• Control: To make a future measurement come out to a desired value.
• Understand: A human-centric process of prediction using a small, communicable collection of facts—a model that “fits on an index card.”

AI excels at prediction and control. AlphaFold can predict 200 million protein structures, but it doesn’t “understand” biology. That task is left to the human in the loop. The goals can be contradictory; optimizing for predictive accuracy often requires black-box models that sacrifice the legibility required for human understanding. As Noam Chomsky notes, a theory of “anything goes” perfectly predicts all outcomes but explains nothing because it fails to answer the question, “Why are things not that way?”

The Cultural Illusion of AGI and the Limits of Knowledge

The pervasiveness of the computational metaphor contributes to what Chirimuuta calls a “cultural historical illusion”—the feeling in places like Silicon Valley that AGI is inevitable. This confidence stems from a mechanistic view of life and mind, which presupposes that cognitive processes are substrate-independent mechanisms that can be replicated in a machine. If this core mechanistic hypothesis is wrong, the claims for AGI’s inevitability are not well-founded.

This leads to the concept of “haptic realism.” Instead of viewing knowledge as a passive, visual process of observing reality from a distance, we should see it as a form of touch. We must poke, prod, and engage with the world to learn about it, and in doing so, we partially create the patterns we find.

Ultimately, we are finite biological creatures with a “cognitive horizon,” as Chomsky puts it. Just as a rat cannot be trained to run a prime number maze, there are likely inherent bounds to our cognitive capacities. Our best theories will always bump up against these limits. The goal is not to find the one final, true description of the brain, but to understand what our models help us do, what they illuminate, and what they leave in darkness. The brain is not a computer, just as it wasn’t a hydraulic pump. It is something we can only ever glimpse through the lens of our limited, albeit powerful, simplifications.

• Tags:
• Philosophy of Science
• Feature
• AGI
• Neuroscience
• Functionalism
• Metaphor
• Simplification
• Ontology
• Prediction vs Understanding