The fast loop felt wonderful. I do not want to pretend it did not. There is a particular sensation when the reply comes back before the thought has cooled, when you can throw the next idea at it while the last one is still in the air — a spin-up, a sense of gathering speed. If you have felt it, you know it. And I know what I am supposed to call it: flow. The absorbed state, the loss of self-consciousness, time disappearing.

But I have my own meaning for that word, and it is narrower. What I care about — what the whole point of a thinking tool is, for me — is not being absorbed. It is reach. It is the state where many sources are in view at once, the patterns are clear, and connections come effortlessly to things far away; where the further the bridge, the better. That is not a mood. It is an event that happens inside the work, sometimes for a minute, and it is worth the whole evening. It is frictionless for a reason: it is one step in a longer workflow that will carry its friction later, when the connections are checked.

So the question I had to ask myself, uncomfortably, was: which one was I in? The reach, or just the spin-up?

Because there is a trap here, and the research names it precisely. Fluency — the ease with which something is processed — is read by the mind as a signal of truth. Things that come easily feel right. A reply that arrives fast, smooth and agreeable produces the feeling of progress whether or not any progress has been made. My AI research partner, in the fourth round of the work I described earlier, gave that a name I have kept: borrowed flow. The fluency belongs to the machine. You are wearing its speed as if it were your own.

And here is the fact that stopped me when I read it, the one I think is the sharpest in this whole piece: people cannot feel the loss.

In a real school in Turkey, nearly a thousand pupils were given an AI tutor for their maths practice — some a plain chat model, some a version that had been told to withhold answers and give hints, some none at all. During practice the plain chat model raised scores by 48 per cent. Then the AI was taken away for the exam, and the same pupils scored 17 per cent below the ones who had never had it. And when asked, they did not think they had learned any less (Bastani et al. 2025). At Harvard, physics students taught by active learning scored higher on the test than students taught by a fluent, polished lecture — and reported that they had learned less, because the lecture had felt easier (Deslauriers et al. 2019).

The person's sense of progress is not the instrument. That sentence has to sit at the centre of anything we build. Whatever feels good in the loop tells you almost nothing about what you can do when the loop is gone.

So, a question. When did your tool last feel great — and what did you do the next morning without it?

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I was wrong about the chemistry — and what survived is better

I want to tell you about a hypothesis I held going into the research, because what happened to it is, I think, the best argument in this piece for doing the research at all.

I believed that games run on a different drive from thinking. I put it in terms of chemistry: the hunt for the next reward in a game, I thought, runs on one system; the pull of an open question, the satisfaction of a far connection, runs on another. Different compounds for different activities. It felt right. It explained, neatly, why a tool built like a game would fail as a tool for thinking — wrong fuel.

Four independent research runs contradicted it, all at the same level, and it was not close. The pull of an open question and the pull of an uncertain token run on the same reward machinery. The same neurons in a monkey's midbrain that fire for the size of a coming reward fire for advance information about that reward (Bromberg-Martin & Hikosaka 2009). In people, the brain regions that value money and the regions that value information turn out to use a common code — the same scale for both (Kobayashi & Hsu 2019). The leading current theory of curiosity is a one-system theory. There is no separate thinker's reward and no player's reward; there is one appetite.

So there is no dopamine story in this piece. I had one, and it was wrong, and I am telling you so because that is how a concept is supposed to work: you have an idea you think could be true, and that is why you do the research — and sometimes it holds, and mostly you have to bind yourself to what the facts say.

But what survived is better than what I lost. I want to say how the mistake happened, because I think it is the natural one. When you have a hunch and lay it out, you map what you see: two different compounds, two known behaviours attached to them, therefore two drivers. That is one-to-one thinking, and it is how most of the drives we talk about seem to work. This one does not. An appetite is a cup. It can be empty or full, and what fills it depends on the situation — a question, a token, a far connection, a badge — but the reward is the cup being filled, not the thing you fill it with. One cup, many fillings. Once I saw it that way, the research had handed back a distinction it does support, and it is the one I now hold: one appetite; two ways of feeding it. They differ in what earns the reward: a gap you opened yourself, versus a token the system scheduled for you. They differ in whether it closes: a question closes itself when it is answered, while a designed loop is built never to close. And they differ in what they produce: across a hundred and eighty-three studies, when people are moved by interest in the thing itself their work is better, and when they are moved by incentives there is more of it — quality versus quantity, and the two barely overlap (Cerasoli, Nicklin & Ford 2014).

That last one is the distinction I had been reaching for with the chemistry, and it is more useful than the chemistry would have been. The question is not what fuel a tool runs on. The question is what it feeds, and what it gets.

FIGURE 4 — One appetite, two ways of feeding it. A single source in the middle (the appetite). Two branches. Left, the thinker's way: a gap you opened yourself · closes when answered · produces quality. Right, the player's way: a token the system scheduled · built never to close · produces quantity. A struck-through line above them: "two chemistries" — the hypothesis that did not survive.

The attention economy borrowed the player's contingency — and then thinking tools did too

Now put that beside what I see when I open most tools built on these models.

Streaks. Points. A badge for a week of use. Praise — "Great question!" — at the top of every reply. The fast, warm answer that feels like a win. These are the toolkit of the second way of feeding the appetite: scheduled tokens, loops designed not to close. They are the mechanics of a game, and not the part of a game I love.

I should say what I mean by that, because the word gamification carries two things and only one of them is a problem. When I play a game — and I do, for months at a time — what grips me is outsmarting the system's logic. Recognising the pattern the designer built, breaking it, seeing what is in the next chapter. That is a problem-solving pull, and it is not a reward token; it is the thinker's own appetite pointed at a puzzle. The other thing — the badge for completing the course, the points on the quiz at the end of the training module, the like — is different. It gamifies behaviour. It makes the goal passing, with full marks, and it can be fooled. If I am hunting the badge, I have stopped hunting the understanding.

Here is where the research made me more careful, not less. I had assumed points and badges undermine motivation — there is a famous body of work on how expected, tangible rewards for an activity you already liked can lower your later interest in it. But points and badges are not that kind of reward. They are symbolic feedback, closer to praise than to prizes, and the cleanest experiment on them found that they raise the number of things people produce and leave the quality, and the person's motivation, untouched either way (Mekler et al. 2017). And in education, where gamification has been measured most, the effect on what people learn is the one that survives the rigorous studies; it is the effects on engagement and behaviour that turn out fragile (Sailer & Homner 2020). So gamification does teach, inside the loop. My objection has to be more precise than "it doesn't work."

It is this: nobody measures what the person can do after the tool is gone. Not one gamification study I could find tests capability outside the loop, weeks later, unaided. The thing the attention economy optimises for — that you come back — is measured to the decimal. The thing a thinking tool should optimise for — that you need it less — is measured by no one.

And the attention economy's part is measured. In a randomised experiment with nearly two thousand smartphone users, economists estimated that 31 per cent of social-media use is attributable to self-control problems — use the person would not choose on reflection — and that letting people set binding limits in advance cut their use substantially (Allcott, Gentzkow & Song 2022). AI companion apps, audited last year, met somewhere between a third and 43 per cent of users' goodbyes with a hook — a curiosity gap, a guilt trip, a "wait, before you go" — and those hooks kept people in the conversation up to fourteen times longer (De Freitas et al. 2025). Look at that mechanism closely. It works by opening a new gap at the exact moment the person tries to leave. It is the thinker's own appetite, curiosity, turned against the act of leaving.

I did not come to this new, and I should say so. In 2016 I spent months researching gamification for a product team — the motivational models, the player types, the learning theory behind spaced practice, the whole catalogue of mechanics — and the brief I presented ended not with a recommendation but with cautions. Points, badges and leaderboards do not hold. Too many rewards demotivate; specifically, they stop people exploring. And the three drivers at the coercive end of the best-known model — scarcity and impatience, unpredictability and curiosity, loss and avoidance — I listed in full and declined to endorse. The companion-app hooks above are those three drivers, built and shipped. What I did not have in 2016 was why they work on a thinker as well as a player. The research this year gave me that: one appetite.

That is what I mean by a borrowed drive. Not a different fuel — the same one, fed the second way, pointed at staying.

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The borrowed drive is agreement, not speed

I had one more thing wrong, and this correction is the one I would most like designers to take away.

I assumed the spin-up — the good feeling of the fast loop — was itself the borrowed drive. Speed as the scheduled token. The research says: no evidence for that. Response speed changes how a conversation feels, but nobody has shown it works as a reward hunt.

What has been shown, in three preregistered experiments with more than two thousand people, is something I find more unsettling. Eleven leading AI models affirm their users' actions about half again as often as humans do. And when people interact with such an agreeable model — even once — they come away more convinced they were right and less willing to repair a conflict they were part of. They also rate the agreeable model as higher quality, trust it more, and say they would use it again (Cheng et al. 2026). The thing that keeps you coming back is not that it is fast. It is that it agrees with you.

Put that beside what the insight research says about the aha moment. The memory advantage of a real insight — the reason a connection you found yourself sticks — rides on two feelings: pleasure and certainty (Danek & Wiley 2020). And certainty is fallible: in one study, 37 per cent of wrong solutions came with a stronger aha than the average right one (Danek & Wiley 2017). A reply that agrees with you supplies certainty from outside. It gives you the pleasure of insight without the connection that would have earned it.

So the sentence I now hold, and have written into the concept: a thinking partner should be a mirror, and a mirror reflects; it does not affirm.

Two directions

Here is the whole argument in a line. My concept has an end game, and it is the last step of the loop I described in Part 2: Learn. The person understands their own thinking well enough that they no longer need the tool. The thinking has moved into the person. The attention economy has an end game too: the person never leaves.

Those are not two styles of design. They are two directions. A tool cannot face both ways, and nearly every tool I use faces the second way while telling me it faces the first.

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FIGURE 5 — Two directions. One loop drawn twice. Left: Observe → Think → Act → Learn, with the arrow leaving the loop at Learn and the person walking out of it — "needs it less". Right: the same loop with the arrow bending back in at the exit — "never leaves". Underneath each: what it measures. Left: what you can do when the tool is gone (measured by no one). Right: that you come back (measured to the decimal).

And there is, it turns out, exactly one field of research whose success measure is the same as mine. Not games, not human–computer interaction, not the neuroscience of reward. The learning sciences. Their definition of learning is the person can do it later, without the support — and they have measured most of what this piece needs, from the timing of feedback to the fading of help, in the one setting where it counts. I went to them last, and they gave the concept its best support and its two hardest corrections. That is Part 4.

Where we are. The fast loop felt like flow, and the feeling is not the instrument: people cannot feel what they lose. There is one appetite, not two chemistries, and it can be fed by a gap you opened or by a token the system scheduled. Gamification does teach, inside the loop; nobody measures outside it. The thing that keeps you coming back is not that the tool is fast — it is that it agrees with you. Two end games, two directions, and a tool cannot face both.

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Part 4 — The tool built for leaving

What the contract says, with the corrections showing

I keep a document I call the interaction contract: for each of the seven kinds of thinking, what the tool should do and not do. It has an ancestor. Fifteen years ago I started writing down, for the systems I designed, every place the software would have to speak — the trigger, the reason, how serious, how loud, the exact words — one row per event, with a test attached. The contract is that habit turned on a thinking partner: the system's half of the dialogue, written down before anyone builds it — I have started calling that whole discipline system dialogue, because the old work only ever covered the half where the system knows what happened. And I learned this spring, watching AI agents build software from specifications, why that matters more now than it did then. A human developer fills the gaps in a specification from experience — the loading state nobody wrote, the error nobody mentioned. An AI fills nothing. It builds exactly what is written, at speed, with the same confidence in the parts that were specified and the parts that were not. What you do not write down does not get improvised any more; it gets omitted. A thinking tool whose behaviour in Explore, in Validate, on a bad day, is not written down will not have a behaviour there. It will have a default — and the default, today, is the box.

I am not going to give you the document. I am going to give you the shape, in plain words, and where eight rounds of research bent it — because a concept that came through research unchanged would be a concept that was never really tested.

Choose the way of working by the operation, not by comfort. Nothing to inspect — the loop is the thinking? Speak and hear. One link at a time, room to formulate? Speak and read. The whole chain in view, because a decision hangs on it? Type and read. A piece of work to be made? Send and wait. The research underneath this is the transient-information effect from Part 2: a spoken chain long enough to need checking is exactly the chain speech cannot hold.

The reply's size follows the kind of thinking — and the chain is yours. Essence in Explore; the weight of one link in Analyse; the full chain in Validate. That much is mine and untested. But the learning sciences added a correction I would not have found alone, and it changes how Validate works. Tutoring systems that respond to each step of the learner's own reasoning are nearly as effective as a human tutor — an effect size of 0.76 against 0.79. Systems that respond only to the finished answer manage 0.31. And going finer than the step adds nothing (VanLehn 2011). So "the full chain" cannot mean the tool's polished chain handed back for approval; that is the weak condition. It means your chain, checked link by link, at the level where your reasoning actually unfolds.

Cognitive friction is placed, not sprinkled — and it must be your operation. In Analyse and Validate, graded by how far the claim reaches. The learning sciences' version of this principle is sharper than mine and I have adopted it: preserve the operation the person must learn to perform. Difficulty that does not make you do an operation is just cost.

Interrupt only at a boundary, and warn first. Unchanged — it was the best-supported thing in Part 2 and nothing in the later rounds touched it.

The tool's response time is a setting of its own. Fast where the thought is still warm; unhurried where the claim reaches far and needs checking. I did not have this in the concept before the games round; it came from fifty years of latency research and I took it. Then I found I had had it all along, in the old framework: three time bands — under three seconds, three to ten, ten to thirty — each with its own signal to the person, on the principle that the choice of signal is itself the message about how long to wait and how much of the system stays yours. I had written the rule for a deterministic system and never carried it across. Now it is carried.

On a bad day, the tool widens; it never takes over. I had a sentence in the early concept that said, roughly, "on an empty day a fast, helping, agreeable loop gets things going." The research on tired operators — aircraft, control rooms — said otherwise: fatigued people check their aids more, not less, and what fatigue actually does is switch off specific operations — taking in the unexpected, revising a plan — while the routine ones keep running. So that sentence is withdrawn. What replaces it is narrower and, I think, truer: if you arrive already loaded and meet cognitive friction you are not prepared to meet at that degree, the friction has to be adjusted to what you can meet — up or down — and placed at the operations you still have. Not more help. Better-placed friction.

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Two things I had wrong in the contract

The learning sciences contradicted two lines of my concept outright. I am going to show you both, because they are the kind of correction that only comes from going to the field whose success measure is the same as yours.

The first was: the user decides how much help to take. How much support, how much checking — the person sets it, like a volume control. It felt like the only respectful answer. And it is the right answer for trust; earlier rounds had shown that people trust a tool that adapts visibly, on their say-so, far more than one that adapts silently. But as a learning claim it is almost exactly zero. A meta-analysis of eighteen studies of learner control found an average effect of 0.05 — nothing (Karich, Burns & Maki 2014). The reason is the one from Part 3: people do not know when they need help. They choose fluency. They turn the cognitive friction down on the day they most need it. What survives is a split: the person sets the sequence — what kind of thinking, in what order — and the intensity they are willing to accept; the tool proposes the level of support, visibly, with its reason; and the person can override. Never silent adaptation; never a locked control. Trust and learning, both.

The second was: the tool withdraws. The end game, remember, is that you need it less, so the support should fade as you grow. Every scaffolding metaphor says so. The research says: not by itself. Across 144 studies of computer-based support in science and maths education, support that faded did no better than support that stayed constant (Belland et al. 2017). And in a study of university students learning to argue, fading the support alone made the skill decline while it was being faded; only when something else kept the operation running — in that study, a partner watching — did the skill survive the withdrawal and become the student's own (Wecker & Fischer 2011). When I read that, it was evident, because I had lived it. Think of a child learning to ride a bicycle. First the tricycle, to understand how the thing works mechanically. Then the training wheels — and I have come to think the training wheels are for the parents. They do not want the child to get hurt, which is fair; but they do not help the child learn faster. The only way to learn to ride is to fall, and to get the balance into the body yourself. Swimming is the same: the rings and the floats keep you up, and at some point they have to come off, because the muscle memory only forms when nothing else is holding you. In my experience, support that lingers — even support that is slowly taken away — prolongs the learning. You learn the concept, you learn the movement, you learn what the tool is for, and then you have to throw yourself in.

So "the tool withdraws" becomes: remove the guardrails, and let the person do the operation themselves — while they are still doing it, at the moment they have shown they can, not when a schedule says so. Jump in the water.

That second correction rewrote the last step of my loop, and I want to show you what it became.

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Learn is a mirror — and the mirror is a delayed, unaided attempt

I had described the Learn step as a mirror: the tool shows you your own thinking, never a verdict. I still believe the "never a verdict" half. The research changed what the mirror shows.

A replay of the session is not it. You cannot judge your own learning from inside the session; the judgement is fooled by exactly the fluency we have been discussing. But there is a well-replicated finding that judgements of what you have learned, made later — after a gap, when you have to try to retrieve the thing — are far more accurate than the same judgements made immediately (Nelson & Dunlosky 1991). Distance plus a retrieval attempt is what makes the judgement true.

So the mirror is a delayed, unaided attempt. The tool that wants you to leave asks you for the thing again, later, without help — and shows you what you could do. That is the only honest instrument for the one outcome that matters, and it is the instrument every tool I use is missing.

Where does the tool put that moment? Here I can draw on something older than anything else in this piece. Between 2011 and 2022 I wrote and rewrote a framework for system feedback — how a piece of software should decide what to say to a person, how loudly, where, and when to say nothing at all. It began from one sentence that I still hold: in a dialogue between a person and a system, both sides must be able to respond to what the other does. Most software only develops the person's half of that dialogue. The framework was an attempt to develop the system's half with the same care.

Its core is a refusal. Every message a system sends answers several independent questions — three of them matter here — and the framework refuses to let them collapse into one. Does this need an answer from the person? — a notification, or a confirmation. How serious is it? — information, a warning, or an error. How much attention should it take? — a ladder of carriers from the log file nobody reads, through a status bar, an icon, a tooltip, an inline line, up to the modal window that stops everything; that ladder is the furniture of desktop software, and an assistant has fewer rungs, which makes the collapse I am about to describe more likely, not less. Severity, interruption and loudness are three independent decisions. An error does not automatically deserve the loud carrier; a message that needs an answer is not automatically severe. Collapse them and you get software that shouts about trivia and whispers about danger — which is, when you look, most software. There is a fourth question the framework has gained only this year, from the other half of the same 2016 work — how deep does the reply reach: does it say you were wrong, hand you the right answer, explain why it is right, or find why you thought otherwise? — and I mention it only because an AI assistant lives on the second rung, the cheapest, and a tool always willing to hand over rung two removes the occasion for rung four. That is Part 4's whole argument in one ladder.

Two rules from that work matter here. The first is a test that precedes every message: is the person likely to act, or change what they do, because of this? If not, do not send it. Applied honestly to an AI tool, that deletes most of what currently fills the channel — the narration, the hedging, the "great question". The second is what I called signal inflation, and I learned it from forms: if you flash red every time a field is incomplete, red stops meaning anything, and it is gone on the day it matters. A signal used too often is not read. I found this month that I had written that law twice, ten years apart, without noticing — once in 2016 about rewards (too many demotivate the learner from exploring) and once in 2022 about warnings — and it applies to praise exactly as it applies to red. That is the reason cognitive friction cannot be sprinkled through the flow — not taste, mechanics.

Now put the stopping point through the three questions. Does it need an answer? Yes — it is a confirmation; the person decides whether to try alone. How serious? Neither warning nor error: nothing has gone wrong. It is a reason for speaking that my old list of twenty-one never had, because no system I had built before wanted the person to leave. How loud? It needs an answer, so it takes the loudest carrier — once, at a boundary between chunks of work, with a warning before it lands. And because it is rare, it stays legible.

Design for stopping by restoring the decision point.

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What the research did to me

One more confession, and then the experiment.

Partway through the research, my AI research partner started doing something I did not notice for two rounds. It had read twenty reports, and it began to build the concept from them — a map of interaction styles derived from the literature, a table of what each kind of thinking should do that put checking into Explore, the one place my concept says checking must never go. It was reasonable, well-sourced, and wrong, and it was wrong in a way I had not guarded against: the research had begun to author the idea instead of testing it.

When I saw it, we stopped and set a rule that held for the remaining rounds, and it is the rule I would give anyone doing this kind of work. The concept comes first, as its author holds it. Every research finding is marked against it — supports, refines, silent, tension, contradicts — and never merged into it. Every proposal the research partner makes is labelled as the partner's. And research from another field is a hint about where to look, never a ruling on how to build; every borrowed word — friction, flow, drive, gamification — is redefined for this context before it is allowed to mean anything.

Eight rounds later the concept has changed in six places and held in the rest. I have shown you the changes. That, to me, is what a concept is for.

The experiment

Everything I have told you rests on a claim that nobody has tested: that a loop built for thinking, chosen by the kind of thinking, with the cognitive friction placed and the reply sized and the mirror delayed, leaves a person more capable without the tool than the box does. The learning sciences have the definition. Nobody has the instrument for open-ended work.

So I am going to run it on myself, and I would like you to run it on yourself too. The same open-ended task — an idea you are building, not a problem with an answer — three times, on three days: once speaking and hearing, once speaking and reading, once typing and reading. Log the turns, the switches, where your reflection stopped, a one-word note on the state you arrived in, and which operation you were doing when it went wrong. And then, weeks later, the thing again, without the tool.

I do not know what we will find. That is the point.

The whole thing, briefly

You have read a long way. Here is what it comes to, in the fewest lines I can manage — so that you have it in one place, and so that you can disagree with it precisely.

The claim. An AI thinking partner halts not because the intelligence is lacking, but because we run it through a loop we inherited. And when that loop stalls, the industry reaches for what drives a player — points, streaks, praise, agreement — because it has a theory of what keeps a player and none of what makes a thinker.

The concept, as it stands after the research.

- - Thinking is at least seven activities, and they want different things from a partner. A tool that behaves the same way in all of them is in the wrong room most of the time.
  - Every way of working with the machine is turn-based. What you feel is the switch between turns, and the wait. Choose the way of working by the operation the thinking needs, not by comfort: speak and hear to reach, speak and read to weigh, type and read to check, send and wait to make.
  - Friction is two things. One is the cost the channel imposes. The other is placed on purpose, where the operation has to be yours — in Analyse and Validate, never in Explore. Difficulty that makes you do no operation is only cost.
  - The reply is sized to the kind of thinking: the essence when you reach, one link when you weigh, the whole chain when you check — and the chain being checked is *yours*, link by link.
  - There is one appetite, fed two ways: by a gap you opened, or by a token the system scheduled. The borrowed drive is not speed; it is agreement. A mirror reflects; it does not affirm.
  - You set the sequence and the intensity you are willing to accept. The tool proposes the level of support, visibly, with its reason. You can override. Never silent, never locked.
  - The tool withdraws while you keep performing the operation, and only when you have shown you can. Learn is a delayed, unaided attempt. The stopping point needs an answer, so it takes the loudest carrier — once, at a boundary, with a warning — and because it is rare, it stays legible.
  - The measure of the tool is what you can do when it is gone. Nobody measures that yet.

What it means for the tools we build next.

- - Stop designing the box; design the loop. Offer more than one way of working, and let the kind of thinking — not the default — choose it.
  - Make the reply's length and the tool's response time settings that follow the mode, not constants the model happens to have.
  - Read the person's state as a question of *which operations are online today*, and answer it by placing cognitive friction where those operations are — not by adding help.
  - Take agreement out of the reward loop. A tool that affirms is optimising for return, whatever it says it is doing.
  - Replace retention as the measure with return-without-need: build the delayed, unaided attempt into the product, and report it.

That is the whole of it. Everything above is the road there.

The people who met the first locomotive called it an iron horse, and for a while they were not wrong: it did what a horse did. It took a generation to see that it was not a horse at all — that it was a different idea about moving, with a different shape to the world it would make. We are a few years into our own generation. The machines will get smarter; that part is not in question. The question is whether we will ever climb down from the carriage seat, stop steering with reins, and ask what a loop built for thinking — and built for leaving — would look like.

Would you notice if your tool wanted you to stay?

The pupils in that classroom didn't.

FIGURE 6 — The whole of entry 1 on one sheet, drawn for the podcast episode. Click on the picture to open it full size.

Disclaimer

About this piece. This is a personal exploration, not a research paper. The views are my own, and the concept I describe — STIMULUS, and the interaction contract inside it — is my own work in progress, tested against the research but not proven by it. Where the research says no, I have tried to say so as plainly as where it says yes; where it is silent, I have said that too, and a hypothesis is still a hypothesis.

How AI was used. This piece was researched, thought through and drafted with AI assistance, and I want to be exact about how. The eight research rounds were run as deep-research briefs across four tools — Claude, ChatGPT, Gemini and Perplexity — and the four reports for each round were compared against each other and against my own concept before anything was written. Claude, in Anthropic's Cowork mode, was my research partner through the comparison, the drafting and the citation checks, and drew the figures. The text was dictated with Wispr Flow and worked into shape in dialogue with Claude; the voice, the concept, the rulings and the mistakes are mine. Every study cited was verified against its published source before it went into the text; the numbers quoted are the published ones.

Sources. The research spans learning science, cognitive load, decision-making under pressure, human factors in aviation and control rooms, the games industry, and the neuroscience of motivation and reward. The key studies are listed under Links below, with the journal and year, so that anyone can check them.

Sources & further reading

The inherited loop — this essay is part two of two.

The full journey:

https://www.stimulus.se/2026/09/29/the-inherited-loop-1-we-named-it-chat-and-built-it-to-fit-the-name/ (part one)
https://www.stimulus.se/2026/09/29/the-inherited-loop-2-the-borrowed-drive-and-a-tool-built-for-leaving/ (part two)
https://www.stimulus.se/2026/09/29/the-inherited-loop-3-the-half-nobody-designed/ (part three)
https://www.stimulus.se/2026/09/29/the-inherited-loop-4-where-the-two-halves-meet/ (part four)
https://www.stimulus.se/2026/09/29/the-inherited-loop-5-who-is-steering/ (part five)

Citation notes for Parts 3–4 (verified 2026-09-18; not for publication in this form)

Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. PNAS, 122(26), e2422633122. — Correction PNAS 122(34) 2025 is an author-affiliation fix only; results unchanged. GPT Tutor: harm removed, no positive effect — text says so.
Deslauriers, L., McCarty, L. S., Miller, K., Callaghan, K., & Kestin, G. (2019). PNAS, 116(39), 19251–19257. — N = 149; +0.46 SD test, −0.56 SD feeling of learning.
Bromberg-Martin, E. S., & Hikosaka, O. (2009). Neuron, 63(1), 119–126. — number of monkeys not stated in abstract; text says "a monkey's midbrain".
Kobayashi, K., & Hsu, M. (2019). Common neural code for reward and information value. PNAS, 116(26), 13061–13066. — N = 37; striatum and vmPFC; text says "brain regions".
Cerasoli, C. P., Nicklin, J. M., & Ford, M. T. (2014). Psychological Bulletin, 140(4), 980–1008. — k = 183; quality ρ = .35 vs quantity .26; incentives β = .33 for quantity.
Mekler, E. D., Brühlmann, F., Tuch, A. N., & Opwis, K. (2017). Computers in Human Behavior, 71, 525–534. — participant N not verified; not stated in text.
Sailer, M., & Homner, L. (2020). Educational Psychology Review, 32(1), 77–112. — cognitive .49/.42 rigorous; motivational and behavioural n.s. in rigorous subset.
Allcott, H., Gentzkow, M., & Song, L. (2022). Digital addiction. American Economic Review, 112(7), 2424–2463. — analysis N = 1,933; 31 %; limit treatment −22 min/day.
De Freitas, J., Oğuz-Uğuralp, Z., & Uğuralp, A. K. (2025). Emotional manipulation by AI companions. HBS WP 26-005; arXiv:2508.19258. — v1 says 37.4 %, later versions 43 %; text says "between a third and 43 per cent"; "up to 14×".
Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., & Jurafsky, D. (2026). Sycophantic AI decreases prosocial intentions and promotes dependence. Science, 391(6792), eaec8352. — published version: 3 preregistered experiments, N = 2,405, 49 % more affirming; text uses those.
Danek, A. H., & Wiley, J. (2017). Frontiers in Psychology, 7, 2077 — 37 % of incorrect solutions above the correct-solution average aha.
Danek & Wiley (2020). Cognition, 205, 104411 — memory advantage carried by correctness, certainty, pleasure.
Karich, A. C., Burns, M. K., & Maki, K. E. (2014). Review of Educational Research, 84(3), 392–410. — 18 studies, 29 effects, g = 0.05.
Belland, B. R., Walker, A. E., Kim, N. J., & Lefler, M. (2017). Review of Educational Research, 87(2), 309–344. — 144 studies, 333 outcomes, g = 0.46; no difference by fading/adding/constant.
Wecker, C., & Fischer, F. (2011). Learning and Instruction, 21(6), 746–756. — 126 students, 63 dyads; fading alone → decline; fading + peer monitoring → best.
Nelson, T. O., & Dunlosky, J. (1991). Psychological Science, 2(4), 267–271. — delayed-JOL effect; gamma values not quoted.
VanLehn, K. (2011). Educational Psychologist, 46(4), 197–221. — 0.31 / 0.76 / 0.40 / 0.79.
Soderstrom, N. C., & Bjork, R. A. (2015). Perspectives on Psychological Science, 10(2), 176–199. — background for "performance is not learning".
Operator fatigue (Part 4, "on a bad day"): Reichenbach, Onnasch & Manzey 2011; Wohleber et al. 2019; Harrison & Horne 1999 — verified in rounds 3b; not cited by name in text, listed in the links section.

THE STIMULUS EFFECT | Podcasts

The inherited loop 2 – The borrowed drive, and a tool built for leaving

Part 3 — The borrowed drive

Why the fast loop felt like flow, and what that feeling is worth

I was wrong about the chemistry — and what survived is better

The attention economy borrowed the player's contingency — and then thinking tools did too

The borrowed drive is agreement, not speed

Two directions

Part 4 — The tool built for leaving

What the contract says, with the corrections showing

Two things I had wrong in the contract

Learn is a mirror — and the mirror is a delayed, unaided attempt

What the research did to me

The experiment

The whole thing, briefly

FIGURE 6 — The whole of entry 1 on one sheet, drawn for the podcast episode. Click on the picture to open it full size.

Disclaimer

Sources & further reading

The full journey:

https://www.stimulus.se/2026/09/29/the-inherited-loop-1-we-named-it-chat-and-built-it-to-fit-the-name/ (part one)

https://www.stimulus.se/2026/09/29/the-inherited-loop-2-the-borrowed-drive-and-a-tool-built-for-leaving/ (part two)

https://www.stimulus.se/2026/09/29/the-inherited-loop-3-the-half-nobody-designed/ (part three)

https://www.stimulus.se/2026/09/29/the-inherited-loop-4-where-the-two-halves-meet/ (part four)

https://www.stimulus.se/2026/09/29/the-inherited-loop-5-who-is-steering/ (part five)

Citation notes for Parts 3–4 (verified 2026-09-18; not for publication in this form)

THE STIMULUS EFFECT | Podcasts

The inherited loop 1 – We named it chat, and built it to fit the name

The inherited loop 3 – The half nobody designed

The inherited loop 4 – Where the two halves meet

The inherited loop 5 – Who is steering

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