The computational approach looks at the end product of our minds and tries to “reverse engineer” how they function. But instead of working backward, we might instead examine the long arc of evolutionary history to build our model from the ground up. This is the approach favored by Paul Cisek, a neuroscientist at the University of Montreal, who’s patiently developed a biological model of brain and nervous system development spanning millions of years.
Cisek contends that rather than information processors, our brains are better understood as feedback-control systems. Our bodies don’t just receive input, they take action to adjust what that input is, contingent on what options are available. As Cisek himself is quick to note, this is not a new idea — writing at the turn of the 20th century, philosopher John Dewey described the mind as a circuit, “more truly termed organic than reflex, because the motor response determines the stimulus, just as truly as sensory stimulus determines movement.”
What exactly is the difference between the two approaches?
Here’s a classic example from baseball: catching a fly ball in the outfield. According to the computational model, solving this problem must involve some complicated and subconscious mental calculus wherein the outfielder estimates the ball’s velocity, calculates the effect of gravity, and undertakes untold other mathematical procedures to “compute” where the ball will go.
In contrast, the feedback-control approach suggests a simple heuristic — essentially, “keep the ball in the same position within your visual field, and then move to maintain that situation.” We take action to adjust the stimulus we receive.
This is not only more true to the experiences of anyone who’s played center field, it also avoids separating out the mental process from physical movement, and avoids invoking the use of complex calculations that the computational model requires. Humans are more dynamic than that. .
This approach also neatly maps to the biological architecture of brains as they’ve evolved over time. From ancient fish to amphibians to mammals to primates and eventually modern humans, what we observe is that new behaviors emerge in response to new environmental possibilities. When dinosaurs died off, for example, this meant some nocturnal creatures could move about the world in the daytime with less risk of being eaten, giving rise to a variety of new capacities. The history of our nervous system, Cisek observes, is one of “continuous extension of control further and further into the world.” (His forthcoming book will explore all this in greater detail, and yes, I’m hoping this essay puts subtle pressure on him to finish it.)
This leads to an alternative and very different diagram than the one above. We can both lay out a map of behaviors and abilities as they emerged over time and we can overlay the specific physical components of the brain to these capabilities, like so:
Instead of moving from left to right as in the computational model, this model should be seen as unfolding from top to bottom over evolutionary time. For example, a long time ago, as vertebrate animals developed more mobility, it became useful to have specialized systems for exploration — by using landmarks, say, or navigating at night. This led to the development of what we now call the hippocampus. Importantly, this also led our ancestors to remember important moments of scurrying from one spot to another, leading to the development of “episodic memory” of past experiences.
We can’t do anything remotely like this with the computational model of the mind; it simply does not sync up to observable neuroscientific structures. What’s more, it obscures that so much of what brains are doing involves controlling living organisms’ interactions within their environments. As such, Cisek suggests we need to dramatically shift the paradigm we’re using to understand the relationship between our brains and behavior, moving away from algorithmic input-outputs and toward more dynamic feedback systems.
Thus far, our story of human development has largely centered on feedback from the physical world. But one of humanity’s most important “feedback loops” arises from our profoundly *social *dispositions. The computational model, it turns out, doesn’t account for this well either — and neither does the AI industry. We’ve spent thousands of years building institutions and norms for learning from and communicating with each other, and over the course of less than a decade, Big Tech companies have systematically worked to dismantle them.
How? Again, we’ll need some historical context.
At some point around many hundreds of thousands of years ago, our distant ancestors did something incredible: They learned to imitate each other. Mimicking gestures and body movements allowed us to pass along successful practices — like chipping away at a stone tool — and coordinate more complex activities through shared ritualistic behaviors, which in turn shaped our cognition. It was the dawn of human culture.
We’ve spent thousands of years building institutions and norms for learning from and communicating with each other, and Big Tech companies are systematically working to dismantle them
Humans soon progressed to imitating sounds and, in turn, to oral language. As we’ve covered previously, language is not the same as thought, but it enables us to communicate our thoughts to each other. At some point, our wandering hunter-gatherer ancestors also started to settle down into non-transient communities, developing complex agricultural practices that allowed us to cultivate food rather than migrating to find it.
Finally, we started to use written marks to represent our spoken languages and other abstract ideas. We became capable of transmitting complex ideas across generations. Formal education, a process of ensuring knowledge is shared among a human group, emerged not long after. And so too in time did practices and institutions that collectivized human decision-making, such as “markets” and “law” and “democracy.”
Each claim I’ve just made is contestable, and cries out for far more detail than I can provide here. But this process is continuous with our biological evolution, in the sense that both foster an ever broader range of control over the world around us.
And sometimes this cultural change creates unintended consequences that hurt instead of help.
Human diets are perhaps the most obvious example. In our hunter-gatherer days, fatty foods were rare but precious to aid survival — just watch *Alone *if you doubt this — and we evolved to seek them out and store them in our bodies once found. But then we culturally developed the practice of farming and other agricultural techniques that, give or take 10,000 years, have made fatty foods plentiful today — if you want a hot dog, there’s plenty available.
AI clogs up our capacity to develop the knowledge we need to navigate the world
And that’s the problem. Hot dogs are a danger to our physical bodies, clogging our arteries with excessive fat we no longer need to store internally. AI poses a similar sort of danger to our cognitive capabilities, by clogging our capacity to develop the knowledge we need – in our heads – to navigate the world. It’s a cognitive hot dog.
The occasional hot dog won’t harm anyone, but it sure will if it becomes a regular meal at lunch or dinner. The same is true for AI — the harm is not from its occasional use, but making it part of our mental diet.
Yet that is exactly what Big Tech hyperscalers are trying to do.
In a July 2025 podcast about “how AI is transforming education,” OpenAI VP of education Leah Belsky boasted that “learners” made up more than half of the 900 million average monthly users of ChatGPT. The service was “the world’s largest learning platform,” she said. More recently, Anthropic announced a new initiative called “Claude for Teachers,” providing free access to premium models to practicing classroom educators.
These efforts will not benefit students, nor teachers. In fact, it’s the most obvious example of how AI weakens systems that we rely on to develop our cognition, for the benefit of convenience.
François Chollet, formerly a software engineer at Google, has memorably described AI as a tool of “cognitive automation,” which he defines as “encoding human abstractions in a piece of software, then using that software to automate tasks normally performed by humans.” Last year, an interdisciplinary group of scholars argued persuasively that we should view generative AI in the form of large language models not “primarily as intelligent agents, but as a new kind of cultural and social technology, allowing humans to take advantage of information other humans have accumulated” (emphasis added).
AI enthusiasts have made no shortage of predictions about the advantages of this, and they’ve largely dismissed the disadvantages as comparable to previous forms of automation, such as computers, calculators, and even the written word. (I’ve had citations to Plato’s alleged opposition to writing thrown at me so many times I’ve addressed them in a separate piece — he was a writer!)
Never before have we developed and broadly deployed something so explicitly intended to supplant human thinking
Whatever the effects of these earlier technologies, never before have we developed and broadly deployed something so explicitly intended to supplant human thinking — that’s what AI evangelists themselves argue.
Look at education. ChatGPT may be the world’s largest learning platform, but students are using it en masse to avoid the effortful thinking that’s necessary to build their durable knowledge. Evidence continues to mount demonstrating the negative impact of AI tools within education settings (and Gen Z generally hates it). A recent study from China revealed that thousands of students essentially stopped doing their homework once they started using AI (which substantially harmed their learning). There’s even evidence that when students use large language models for supposed learning purposes, they become habituated to relaxing their judgment and critical thinking in other contexts.
No other species’ brain and body develops over such an extended period as ours, and historically, we’ve complemented that with unique cultural institutions that transmit knowledge from one generation to the next. We should be zealously protective of this uniquely human endeavor, yet instead many university and school administrators, to say nothing of the Big Tech hyperscalers, are engaged in full-throated efforts to push AI as deep and as fast as they can into our education ecosystems.
If we *only *see the mind as a computer, AI might seem advantageous to learning — this is why many dream of building AI tutors. But already such efforts are failing, because our minds are more than just input-output devices. They exist, or rather *we *exist, within a broader cognitive ecosystem where we must make choices about how we choose to expend our cognitive energy. We do so to extend our agency, our control — however limited it may be — over our world.
If we *only *see the mind as a computer, AI might seem advantageous to learning — but this model is wrong