A cognitive science primer

What the field is

Cognitive science is the study of how minds work. It borrows from almost everyone: psychology, linguistics, neuroscience, computer science, philosophy and anthropology. Most histories date its start to 1956. The term “artificial intelligence” comes from the proposal for a workshop at Dartmouth that summer. That September, a symposium at MIT put three papers in the same room: Newell and Simon’s Logic Theory Machine, Chomsky’s early linguistics and George Miller’s work on memory (the “magical number seven”). The field got its name, and its own journal, in the 1970s.

The question underneath all of it is old: is thinking a kind of computation? This primer walks through the main stops on that road, because LLMs have dragged the question back into the open. I think they have reopened it more than they have answered it.

Turing’s test

In 1950 Alan Turing published “Computing Machinery and Intelligence” (Turing, 1950). He swapped the question “Can machines think?” for one he could test: the imitation game. A judge chats by text with a person and a machine. If the judge cannot reliably tell which is which, the machine passes. The test is purely behavioural (it only looks at what comes out).

By most accounts, today’s LLMs pass casual versions of it. In a 2025 study, judges picked GPT-4.5 as the human 73% of the time when it was prompted to adopt a persona; that is more often than they picked the actual humans (Jones & Bergen, 2025).

I think that is a huge achievement. It is the first milestone on the way to actual artificial intelligence, but it will not be the last. Learning from experience is another precursor, and there will be more after that. The strange part is that each milestone LLMs knock down makes the picture murkier. We certainly are not there yet.

The Chinese Room

In 1980 the philosopher John Searle pushed back on behavioural tests with a thought experiment (Searle, 1980). Picture a man locked in a room with a rulebook. Questions written in Chinese come in through a slot. He does not read Chinese, but the rulebook tells him which symbols to send back, and his answers are perfect. To the people outside, the room understands Chinese; the man understands nothing. Searle’s point is that following rules for symbols (syntax) is a different thing from understanding them (semantics), however well you follow them. The Stanford Encyclopedia’s entry covers forty years of replies.

I first ran into this argument in university, and my first reflex was to reject it outright. The more I thought about it, the more it made sense. If you write a program that prints “I am conscious”, that does not make the computer conscious. It is merely following instructions.

Still, I think the argument starts to fray at the line between declarative and behavioural evidence. A program saying it understands is cheap. A system that behaves intelligently (in the sense David Krakauer uses, i.e. making hard problems easy, which I wrote about in Can a harness be wise?) is a different kind of evidence, and I am not sure the room can wave it away. Also, our own neurons do not know the thoughts they are thinking. No single cell understands anything. Yet somehow, at the aggregate layer, thoughts whiz around our skulls.

That objection has a name: the systems reply. The man does not understand Chinese, but maybe the whole system (man, rulebook and room together) does. Searle had an answer ready: let the man memorize the rulebook and do everything in his head. He is now the whole system, and he still does not understand a word. The debate has not moved much since, and I am not going to settle it here.

Symbols and networks

For most of its history, AI had two camps. Symbolic AI wrote the rules by hand (logic, search, expert systems, chess programs). Connectionism argued for a network of simple units that learns its patterns from examples. Frank Rosenblatt’s perceptron arrived in 1958. Minsky and Papert’s book Perceptrons (1969) set the networks back for more than a decade, and the Parallel Distributed Processing volumes (1986) brought them back. Large language models are connectionism’s biggest win by far.

But look at how they are actually used. An agent today is a model inside a harness, with tools, code execution, memory and rules wrapped around it. A lot of structure has crept back in.

I think the two camps are merging. If you look at how people operate (or at least as much as we understand), the brain has a connectionist architecture with specialized lobes or modules. On top of it we use symbolic language: writing, math, poetry, physics. The intelligence comes at both layers, and I do not think either one gets there without the other.

Limits on computation

Three results put hard edges on what computation can do, and all three come up in arguments about minds.

The halting problem. In 1936 Turing proved that no program can decide, for every possible program and input, whether it will eventually stop (Turing, 1936). Some questions have no algorithmic answer, however much compute you throw at them.

Gödel’s incompleteness. In 1931 Kurt Gödel showed that any consistent formal system rich enough to do arithmetic contains true statements it cannot prove (Gödel, 1931; the Stanford Encyclopedia has a gentler walk-through). Some philosophers argued that this means minds cannot be machines, since we can “see” truths the system cannot: J. R. Lucas in “Minds, Machines and Gödel” (1961), and later Roger Penrose in The Emperor’s New Mind (1989). Most logicians are not convinced, and the argument remains contested.

Complexity. Some problems are solvable in principle but blow up in practice: the work grows exponentially with the size of the input. That is the territory of P vs NP.

Complexity is the one I think matters most for LLMs. I think, for problems as messy as language, a stochastic algorithm is often the only practical option; you trade certainty for speed. Natural language is that kind of problem, and generative models are no exception. In practice they usually sample their way to an answer, and nothing gets proved along the way. So I think errors come with the territory. They can be mitigated, though. (One paper goes further, for a different reason: it argues from learning theory that hallucination is inevitable for any LLM, however it picks its words: Xu, Jain & Kankanhalli, 2024.)

That does not make the tools worthless. Human memory is imperfect, and we make mistakes all of the time. A propensity for errors does not mean an algorithm has no value. It is also where Krakauer’s definition comes back in. If intelligence is making hard problems easy, an LLM gets there the expensive way: statistical brute force across a huge corpus, with the errors built in.

Where LLMs leave the question

So where does that leave us? LLMs pass a casual Turing test. They are a connectionist system wrapped in symbolic scaffolding, and they live with errors by design. The open question is the one cognitive science started with: is there understanding in there, or only very good symbol shuffling? Some critics say models stitch together statistical patterns with no model of the world behind them (the “stochastic parrots” paper is the best-known version). Others point to research suggesting they build internal representations of the world they are modelling (e.g. a model trained only on Othello moves that tracks the board). I do not think that debate is settled, and I would rather state it as open.

Where I land on it is in The synthetic zeitgeist.