Friction Theory

Tomas Pødenphant Lund · Aarhus

You can always do more than one thing. Write the report, fetch coffee, check your phone. Those options race each other, and the one that meets least resistance is the one that turns into something. That is what the theory says behaviour is: the outcome of a race, not a decision you made. The resistance is what I call friction, and it can be measured.

Resistance comes from four places, and you settle each of them without noticing:

• Safety. What does doing this cost me, socially or otherwise?
• Meaning. Can I see what it is for?
• Ability. Do I know how, and am I up to it?
• Effort. What does it cost in time and hassle?

The first two are about value: is it safe, and is it worth doing? The last two are about capacity: can I, and what does it cost? That dividing line matters once we get to language models.

One of them is enough. Something can be safe, clear and easy, and still not happen, because the point of it is missing. So it rarely helps to turn up what is already fine, and that is usually where the effort goes.

They are settled in a fixed order: safety first, then meaning, then ability, and effort last. That is why process design on its own so often fails. It makes it easier to do something people still do not dare to do, or cannot see the point of.

The four also have to be handled separately, because they do not behave alike. Unsafety can be removed but never turned into a plus. Meaning has no upper limit. Ability has a best place in the middle, where the task is neither too easy nor too hard. And of effort there is simply best as little as possible.

Ability is the best-evidenced of the four. Boredom, flow and overload have been seen as three separate states in the same brain-imaging experiment (Ulrich, Keller and Grön, 2016), and learning research finds the same pattern.

The same race runs in a language model, and there you can watch it while it happens. That is why I work with them.

I almost stumbled into studying language models, the kind of artificial intelligence ChatGPT is built from. I wanted to see one concrete thing: could I make them better by letting them doubt, feel their own uncertainty, before they answered? What I found was something else, and bigger. Across area after area where we assume there's a deep difference between us and the machine, the difference actually isn't that large. We can recognise ourselves in it. But the difference that does remain, that one is beautiful, and it matters. This page is about both: how little really separates us, and what it is that does. The patterns reach further than the brain, but the human end is the easiest to recognise, so that's where we begin.

Three places to start

The rest of this page goes deeper. If you want a one-paragraph version: every system that has to choose under finite time and resources runs an internal race between alternatives, one wins, the others are suppressed at a cost. That cost is what I call friction, and the same structure shows up at every scale that's been examined.

What I'm actually studying

Every thought you have, every feeling, every movement of your hand: they are all expressions of computations your body performs.

That sounds odd put that way. "Computation" sounds cold and deliberate, like a calculator running, like someone sitting inside you working out the answer. That isn't what is happening. What is actually happening is closer to a sequence of probability outcomes.

A small example. Inside one of your cells, a stretch of DNA needs to pick up one starting molecule or another. Which one it picks up depends on what is floating nearby, and in what concentration. There is no decider. There is a distribution of molecules and a probability that one lands before another. The cell then produces whichever protein follows from that landing. Multiply that across millions of events per second across your body, and you have something that "computes" continuously without anyone running the show.

The same picture scales up. When you decide between two options, you are not running a deterministic calculation. You are letting competing candidate-answers race each other in your brain under conditions of finite time and finite resources. One of them wins. You experience the winner as "I chose X." You do not experience the race.

How learning actually works

Imagine dragging your finger through a thin layer of water on a tiled bathroom floor. The water shifts; a channel forms. Drag your finger through the same path again, and it's a fraction easier, because the channel is already there. Physicists call this hysteresis: the system carries traces of its own history.

Your brain works the same way. Routes you use a lot leave traces, and the traces make those routes more probable next time. That is what learning is, at the substrate level: not magic, not a uniquely biological mystery, just probability shifted through trace-accumulation.

This is what Friction Theory is about. The price the system pays every time it has to resolve competing candidates into one committed outcome (in time, energy, information) is the friction. Friction Theory is the formal name for the framework. Behavioural Friction Theory (BFT) is its biological version, applied to nervous systems and organised around four computational fields (Safety, Meaning, Capability, Effort) and five regulatory layers.

Why information-dumping doesn't teach anyone, anything

Even language models, which are literal computers designed to absorb information, cannot be taught by being shown more of it. Throw information at a model and you get a model that hallucinates confidently about whatever you threw at it.

If language models, designed to be teachable, cannot be taught by information-dumping, why do we assume humans can be? We have been thinking about it as motivation. It is physics. You do not learn information; you learn the trace information leaves.

The full version of that argument (what it means for how we teach, and why Bjork's "desirable difficulties" is physics rather than a pedagogical choice) is on the Memory page (plain) or the Learning page (more academic).

What I do, day to day

I study language models in order to understand humans, which is the opposite direction from what most people expect. The conventional view is that language models try to mimic human language, so studying them tells you about the mimic, not about humans themselves. I am arguing the opposite.

Many of the things we thought were uniquely human turn out to appear in language models too. The reason isn't that the models were trained to imitate human cognition. The same race runs in both: parallel candidates racing under finite resources, with one winning. That same race produces the same characteristic behaviours:

• Information overload — too much context hurts, just as it hurts you
• Anchoring — the first word shapes the rest
• Reactance — instructions activate the routes they try to prevent
• Inverted-U on challenge — too little and too much both hurt

→ The full tour: LLMs aren't calculators — a dedicated page on all the surprising things about language models. All the things you would expect to be typically human.

When language models reproduce these phenomena, they are not faking. They are telling us the phenomena are not specifically biological. They are structural consequences of any architecture that lets competing options race toward a deadline, with only one winner. The brain is one such system. A transformer is another. They look alike where their architecture is alike, and diverge where their architecture diverges.

What humans have that language models do not

The architecture is shared, but that isn't the whole story, and this is where it turns beautiful. The places where we and the language models differ are the most important of all. Because when so much of what we thought was distinctly human turns out to be something we share with a machine, what remains, the part the machine does not have, stands out all the more sharply. That is where the human sits.

They do not have loss aversion: the human tendency to fear losses about twice as much as we enjoy equivalent gains. This is because loss aversion fundamentally comes from the fact that you can die if you choose wrong too often. Humans, mice, and bees all have mortality; language models do not. This is a new explanation for an old finding: loss aversion is not a universal cognitive law. It is a consequence of having a body that can die.

They do get surprised: words they did not see coming draw measurably more attention, and breaking the format makes them measurably resist. Both are measured directly. The still-open question is not whether they react, but whether they can tell the two apart: whether there is a layer that distinguishes "new information I should update on" from "something I want to push back against". That layer has not yet been seen in a language model.

They do not have memory between conversations. When you start a new conversation, the language model starts over. This means a huge area of human cognition (from Ebbinghaus's forgetting curve to spaced repetition) literally cannot be tested on language models, because they lack the basic substrate for it.

Could they doubt? Yes

The question I started with, whether a model can feel its own uncertainty, turned out to have a concrete answer. The model often knows the right answer, but a slightly more probable wrong one wins. Ask it to think again, with the strategy that suits that particular model, and performance rises noticeably, with no retraining. And the surprise was that the lift comes from the strategy, not from the signal in the model’s output, which I had expected to pick the answer. That is the practical end of all this, and it is in Paper 3.

Where humans and language models meet, and where they diverge

Short version:

They meet on: anchoring, hysteresis, confirmation bias, mode-shift cost (the cost of switching between modes of thinking), expertise reversal effect (instructions that help beginners hurt experts), surprise-weighted encoding, and many classical cognitive biases.

They diverge on: loss aversion (requires mortality), spaced repetition (requires between-session memory), and the two value fields, safety and meaning, which need a history and a self. Ability and effort, by contrast, can be read off anything that has a capacity and pays a cost, a language model included. The function of safety can be trained into a model, but the feeling does not come with it (Paper 30).

The pattern shows up almost everywhere

Once you start looking for the race-architecture signature, you see it at scales that have nothing to do with brains or computers. The same characteristic curve (performance peaks in the middle, drops off at both ends) appears in:

• Quantum particles deciding their state (the qubit decoherence window, 10⁻¹⁵ seconds)
• Electrons drifting through metals (Ohm's law and Drude transport)
• Chemical reactions choosing products (reaction kinetics)
• Detectors picking up faint signals from noise (stochastic resonance)
• Students learning new material (Bjork's "desirable difficulties" zone)
• Whole organisms under stress (the Yerkes-Dodson curve)

Seven different things, spread across roughly forty orders of magnitude in time. They all show the same shape because they all face the same constraint: multiple options have to resolve into one outcome under finite resources. Paper 10 walks through the seven phenomena and the race-framework behind them.

The suggestive implication: biological cognition and physical systems may share the same race-structure, differing in substrate not in shape. Humans hit a performance peak in the middle of the challenge range; qubits hit one between coherence and decoherence; the proposal is that both can be read under the same race-vocabulary, with the substrate differing and the constraint shared. Behavioural patterns we usually think of as "psychological" (the inverted U, hysteresis, anchoring, mode-shift cost) may turn out to be describable in the same race-vocabulary as the physical ones. This is a shared lens, not a claim that the substrates are identical.

That is the program. The papers develop the formal apparatus, the empirical signatures, the testable predictions, and the falsification criteria.

My papers

All papers are open-access preprints on Zenodo:

In progress, not yet out

Several papers are in preparation and will be released in the coming weeks:

• Paper 32: Optimal, Not Perfect — the architecture behind an AI system that follows its rules, can check itself on doing so, and knows where it does not reach

Contact

Email: tomas.lund@frictiontheory.org
ORCID: 0009-0000-4724-2427
LinkedIn: linkedin.com/in/tomasplund