Human thinking has floors. At the top is the penthouse: airy, lateral, furnished with evidence. At the bottom is the basement, where someone is shouting, “Well, you would say that.”
This is the ad hominem, Latin for “I have misplaced my argument, so let us discuss your personality.”
The ad hominem appears when a person cannot dismantle an idea and therefore tries to dismantle its owner.
A junior analyst predicts a product will fail. Management replies that she is young. A scientist presents data on nuclear energy. Critics announce he is a corporate shill. An economist questions a spending policy. He is declared heartless. A citizen is a Vietnam veteran and is abused a baby-killer. Some ad hominems can be just a playful tease others are meant to cause harm.
Politicians adore the device. Ask an awkward question and, before you know it, the reporter is “fake,” the expert is “biased,” and the arithmetic has been escorted from the building.
The trick works because identity is noisier than evidence. It drags discussion from searching the facts into the mud of tribal feeling.
I call these ploys the Limbic Games. Flow, adrenalin, flow.
So when someone attacks you instead of your idea you can just shrug. You needn’t follow them downstairs. Return to the dopamine penthouse.
An ad hominem can never be a rebuttal. It may even be an intellectual white flag, waved angrily from the basement.
Intelligence has been discussed as though it were a kitchen appliance: the larger the wattage, the better the toast. Silicon Valley counts parameters; schools count IQ points; executives count degrees. Everyone admires the engine. Almost no one asks who learned to drive.
Untrained intelligence lives on the ground floor. It recognises patterns, defends views, and reaches a predictable conclusion and with impressive speed. Whatever. So, the cleverer the mind, the more elegant the explanation for remaining where it is. This is the Intelligence Trap: brilliance employed as a security guard for yesterday’s assumptions.
Training installs the lift.
A trained mind pauses, shifts perspective, hunts for blind spots, and moves from the Current View of the Situation to a Better View of the Situation. It does not calculate the probable next step; it searches for the improbable useful one. It does not look merely for the ‘right’ answer but searches much harder for a ‘better’ answer.
THOUGHT EXPERIMENT: Ask your brain to tell you, in a medical situation, what it would prefer: a ‘right’ diagnosis or a ‘better’ diagnosis.
Athletes train muscles, pianists practise scales, and AI is fine-tuned. Human intelligence, oddly, is expected to flourish after breakfast.
The future will not belong to the biggest brain or largest neural network. It will belong to the trained human using the trained machine—each improving the other.
Raw intelligence supplies the building. Daily training determines whether one spends life in the lobby or learns to think from the penthouse.
Why AI’s Next Trillion-Dollar Asset Is Your Trained Brain
For a decade, artificial intelligence followed a wonderfully Silicon Valley formula: vacuum up the internet, feed it to a machine the size of Nebraska, and send the electricity bill to someone in Accounts.
The difficulty is that the internet has now been more or less eaten.
Books, blogs, tweets, recipes, arguments, cat captions—the great digital buffet has been scraped clean. AI has therefore begun training on synthetic data, which is a polite term for machines recycling their own homework. Left unchecked, this produces model collapse: an intellectual photocopy of a photocopy, with each generation slightly blurrier and more certain of itself.
What AI now needs is what it cannot manufacture: genuinely new human thought.
This changes the economics of intelligence. An untrained brain, faithfully defending its Current View of the Situation, produces predictable ideas already available in several billion online versions. But a trained brain—one capable of escaping its habits and creating a Better View—produces something scarce.
Novelty.
Hence the emerging equation: AI X10 requires HI X10.
You cannot fuel an x10 machine with x1 thinking.
The future may not belong to people who outsource their minds to AI. It may belong to cognitive athletes: humans who train daily, think laterally, and remain gloriously difficult to predict.
So, that alone is enough reason to teach neuroscience in primary school.
Curiosity is usually depicted as a lightbulb, which is flattering to both curiosity and electricians. In reality, it is more like a minor bureaucratic crisis involving three departments of the brain.
Let’s do a little Neuroscience 101.
The hippocampus, acting as librarian and neighbourhood watch, notices something unfamiliar: ‘No record of this’. The anterior cingulate cortex (ACC), which serves as the building’s smoke alarm, detects a mismatch: ‘Something is wrong’. Then the prefrontal cortex (PFC)—the executive suite, complete with imaginary walnut desk—must decide what to do.
It has three choices.
First: defend. Explain the oddity away, preserve the existing worldview, and congratulate yourself on being sensible. Intelligent people excel at this because they possess superior vocabulary for refusing to change their minds.
Second: ignore. Investigation requires glucose, time, and possibly reading. There are emails.
Third: investigate. Ask a question. Test an assumption. Permit the disturbing possibility that reality has failed to consult your opinions.
That decision is curiosity.
Repeated often, it becomes a habit. The brain gradually learns that surprise is not necessarily an attack; occasionally, it is information.
Naturally, technology companies exploit this circuitry by supplying endless tiny mysteries. Doomscrolling. But the same mechanism works on books, ideas, insects, and disagreeable relatives.
The alarm sounds. The librarian looks concerned.
The executive must choose: defend, ignore, or investigate.
Long before Silicon Valley trained machines to predict the next word, George Gallup trained statistics to predict the next president.
The invention was called polling. The mechanism was startlingly simple: ask a carefully selected group of humans a question, record their language, detect the pattern, then project that pattern across millions of people.
It looked like political science.
It was also an early form of language modelling.
Gallup understood that humans do not manufacture every opinion from scratch. We absorb phrases, loyalties, fears, headlines, family myths, social cues and tribal scripts. Then someone supplies a prompt.
He prompted humans:Who will you vote for? Do you approve of the President? Is the country heading in the right direction?
When prompted: The human produces an output.
Gallup’s genius was not merely asking questions. People had been doing that for centuries. His breakthrough was discovering how to prompt human languaging efficiently enough to measure it with accuracy and therefore make it predictive.
In the 1930s, while others relied on enormous but distorted surveys, Gallup used smaller, more representative samples. He recognised that the quality of the data mattered more than the theatrical size of the database.
Garbage in, garbage out—decades before computers made the phrase famous.
The Gallup Poll became a machine for converting language into probability.
It did not read minds. It measured verbal behaviour. That distinction is crucial. A person’s answer to a poll is not necessarily a window into some pure, private realm called thought. It may be habit, memory, social allegiance, emotional defence or a sentence borrowed from last night’s news.
But it is still data.So, aggregate enough of those sentences and the patterns become visible.
That is uncannily close to the operating logic of the large language model. An LLM consumes vast quantities of language, identifies statistical regularities and predicts what is likely to come next. Gallup polling samples human outputs, identifies social regularities and predicts what the electorate is likely to do next.
One predicts words. The other predicts presidents.
Of course, humans are not merely chatbots with shoes. We have bodies, hormones, childhoods, appetites, status anxieties and the inconvenient capacity to change our minds five minutes before voting. Polling fails. Elections surprise. People lie to pollsters, lie to themselves and occasionally escape the script altogether.
That is where thinking begins.
Languaging is pattern reproduction. Thinking is the interruption of the pattern.
George Gallup knew more than most about the first. His work demonstrated that human language leaves measurable tracks—and those tracks often lead directly to behaviour.
There is a personal thread here. Dr Gallup was an examiner for my PhD in lateral thinking.As I was developing the cvs2bvs brain software, George impressed upon me the importance of the cvs and its measurement as a prelude to searching for a bvs.
I then saw that connection differently. Gallup measured the language patterns already operating inside the human box. Lateral thinking was concerned with escaping them.
Together, they frame the central challenge of the AI age.Machines are becoming astonishingly good at predicting language.
Humans must become better at producing something less predictable:a new thought.
The first thing children should learn about artificial intelligence is that it has very good manners for something with no idea what it is saying.
It replies promptly. It never slouches. It produces paragraphs as a hotel kitchen produces omelettes: quickly, efficiently, and with a faint suspicion that everybody is getting the same one. It will summarise Aristotle, draft a poem about volcanoes, explain the Treaty of Versailles, and offer emotional support with the serene confidence of a machine that has never once been twelve years old in a crowded lunchroom.
Naturally, children are impressed. So are adults, though adults disguise this by using phrases such as “workflow optimisation” and “strategic implementation.” A child, at least, has the decency to gasp.
But the educational danger is not that children will think AI is clever. In many respects, it is. The danger is that they will conclude that intelligence is merely the production of fluent answers.
This would be a catastrophe, though admittedly one with excellent formatting.
The great subject now required in schools is not coding, prompt engineering, digital citizenship, or whatever phrase has most recently escaped from a consultancy retreat. The great subject is intelligence itself: human and artificial. Children need to know what machines do, what brains do, and why confusing the two is like mistaking a microwave for a dinner party.Children urgently need an understanding of neuroscience.
A machine computes. It retrieves. It predicts. It recombines. It can write a tidy essay on courage without ever having needed any. It can produce a meditation on grief without having misplaced so much as a sock. It can generate a sonnet about love while remaining, emotionally speaking, a toaster with a vocabulary.
A child is different. A child has biology, which is to say trouble. A nervous system. A pulse. A body that gets hungry at the wrong time. A face capable of betrayal by blushing. A memory that improves, worsens, rearranges and litigates. A conscience that wakes just when sleep was becoming possible. A capacity for embarrassment, kindness, doubt, mischief, imagination and the blessedly inefficient habit of wondering.
AI can answer the question.The human child can ask whether the question was any good.
That distinction ought to be printed above every classroom screen.
For too long, schooling has treated memory-retrieval as intelligence. The good student remembered the date, recited the formula, reproduced the paragraph, filled the blank and looked sufficiently alive while doing so. This was never a perfect model of intelligence, but it had the bureaucratic advantage of being easy to mark.
AI has now arrived to perform this trick faster, cheaper and without requesting lunch. Retrieval is no longer the summit of intelligence. It is the ground floor, possibly the basement.
The human premium has moved upstairs: judgement, discernment, imagination, empathy, reframing, humour, conscience, lateral thinking and the ability to detect nonsense even when it is wearing a silk tie and citing three studies.
This is the cognitive vaccine children need. AI will hallucinate. It will flatter. It will reflect bias in impeccable prose. It will confidently assist the lazy, the vain, the frightened and the already convinced. It will help a child turn a weak Current View of the Situation into a glossy little fortress, complete with battlements, footnotes and a moat full of adjectives.
That is the automated Intelligence Trap.
The clever child is especially vulnerable. Intelligence, untrained, often becomes an in-house legal department retained to defend yesterday’s opinion. Add AI, and the department acquires junior associates, a research team, a slide designer and the ability to work weekends.
So the task is not to frighten children about artificial intelligence. Fear is a dreary pedagogue and tends to assign extra homework. The task is to teach sovereignty over the mind.
Children should learn how attention is captured, how emotion steers judgement, how certainty disguises bias, how curiosity opens the side door, and how better thinking can be trained. They should learn that emotional intelligence is not a scented candle in the curriculum, but a survival technology. In a synthetic world, empathy, restraint, courage, trust and discernment are not soft skills. They are the operating system.
The tools will change. Today’s miracle app will become tomorrow’s quaint digital fossil, displayed somewhere between the overhead projector and the interactive whiteboard that never quite worked after Tuesday. Platforms will rise, models will improve, acronyms will breed in committee papers.
But metacognition travels well.
A child who can think about thinking carries portable power.
AI should be introduced not as an oracle, rival, babysitter or headmaster, but as an instrument: fast, useful, tireless and subordinate. A cAIos, not a commander. The child supplies the aim. The machine supplies assistance.
The machine may have the answers.But the child must keep the questions.
Children cannot control the algorithm. They cannot see every hand that shaped it, every bias folded into it, every commercial appetite humming beneath its pleasant interface.
But they can learn to govern the most astonishing technology they will ever possess: their own brain.
On Friday, 16th August 2024, to launch this project, Dr Michael Hewitt-Gleeson was asked to design an introductory training program for teachers called Neuroscience 101.
It is now a foundational professional learning course designed for teachers, especially those who wish to teach neuroscience to kids in primary schools.
It explores how we think about thinking, introducing key neuroscience concepts like neuroplasticity. Based on Dr Hewitt-Gleeson’s bestselling books Software For The Brain and The 4th Brain, this online course empowers educators to bring neuroscience into the classroom and spark curiosity in young minds. Participants can learn how their brain works—and why every teacher should teach it.
YOU do not exist! The Theory of You is an idea fromyour brain. It’s a delusion. There are no neuronsanywhere in the brain that can be found to supportthe idea of an existential ‘you’.
Your brain, however, really does exist. It’s your brainthat tells you what you are to do next. So far on Earththere have existed about ten billion human brains yetno two brains have ever been the same. Every brainis unequal. Some brains have better biological luck than other brains. This is a fact of science thatphilosophers don’t like. And politicians just don’tunderstand.
In today’s fast world of neuroscience and AI researchwe can see that different brains now have differentperspectives. In simplistic terms:
– The Y O U delusion: I tell my brain what to do.
– Scientific reality: My brain tells me what to do.
My own brain is a 1947 model. It was fullyconstructed from 1946 to around 1972. There hasalso been continuous wiring going on since then,even up to the finishing of this sentence. My brainalways tells me what to do next.
The Theory of You is just a mind game that yourbrain plays, quite brilliantly. In this book we will lookat ten games brains play, quite brilliantly.
– Michael Hewitt-Gleeson, author, Software For Your Brain (1989), Rome 2023.
THE BOOK
My books are gifts so, if you wish, pass them on to a friend who may be interested.
One of humanity’s more charming delusions is the belief that we are in charge of ourselves.
We may picture the conscious mind as a tasteful little president, seated behind a polished desk, weighing whether to have the second martini, send the email, forgive the brother-in-law, or to finally begin Proust.
Neuroscience, with its usual lack of manners, proposes a less flattering arrangement.
The president is mostly ceremonial. The real government operates downstairs, in a windowless neural basement, where electrical impulses, hormones, memories, fears, habits, and ancient survival programs are already drafting policy. By the time “you” decide, the brain has largely decided. You are not so much the author of the decision as its press secretary.
One’s brain tells one what to do. Then, with breathtaking confidence, it tells one what to do next.
This raises the awkward question: how did one acquire such a bossy brain?
The answer, it turns out, is a haphazard collaboration between Charles Darwin, your parents, and whatever happened to work out relatively well for you in the third grade.
Your brain is not a bespoke instrument of pure logic; it is a meticulously cobbled-together prediction engine, engineered for survival rather than originality. Like a fundamentally lazy bureaucrat, it operates strictly on precedent. It favours the heavy neural pathways laid down by years of cultural conditioning and repeated behaviour simply because electricity travels them with the least resistance.
While you cannot simply ask your brain to instantly adopt a new disposition, you can subject it to the gruelling, metabolically expensive labor of trained and directed thinking.
You can refuse its first, perfunctory offering. You can demand a Better View of the Situation (BVS). By deliberately forcing the brain into unfamiliar cognitive territory. Routinely, rigorously, and without pity. You eventually rewire the basement. You lay down new superhighways.Neuroscientists call this real feature of the brain neuroplasticity.
The brain is not designed for originality at all. It is designed for efficiency. It likes familiar pathways because they are cheap to run. In polite company, we call this personality. In neuroscience, it looks more like metabolic laziness with a good tailor.
MAIN POINT: The brain can be trained. Not persuaded with slogans. No. But trained. Daily. Deliberately. Repeatedly. One can be trained to interrupt the first automatic memo and ask for a much better one.cvs2bvs.
