LLMs Confabulate Like Split-Brain Patients

A man looks at a chicken claw with his right eye and a snow scene with his left. His right hand picks a toy chicken. His left hand picks a shovel. Asked why, he says without hesitation: "You need a shovel to clean out the chicken coop." He sounds completely sure. He is completely wrong.

That experiment, run by Nobel laureate Roger Sperry in the 1960s on patients whose corpus callosum had been surgically severed, became one of neuroscience's most famous demonstrations. The two brain hemispheres, cut off from each other across some 200 to 250 million nerve fibers, each processed different information. When forced to explain a decision made by the hemisphere that couldn't speak, the verbal left brain simply invented a story. Not a random story — a plausible, coherent, confident one.

A viral thread on r/singularity (1,200+ upvotes, 164 comments) argues that large language models do the exact same thing. And Anthropic's own research suggests the comparison is more than metaphorical.

The Parallel That Won't Go Away

The split-brain analogy works at a structural level, not just as a cute comparison. In Sperry's patients, the severed corpus callosum meant the left hemisphere had no access to why the right hemisphere grabbed the shovel. It only knew that the hand moved. So it confabulated — it produced a narrative that sounded right, that connected the available dots, that satisfied the question. The patient wasn't lying. The verbal system genuinely didn't know. It was doing its best with incomplete information.

LLMs face a structurally similar problem. Different layers and attention heads specialize in different patterns. There is no unified executive module that tracks why a particular token was selected. The model's "explanation" of its reasoning is generated after the fact, by the same prediction machinery that generated the answer itself. It's the left hemisphere narrating a decision it didn't make.

This isn't just a Reddit theory. In January 2025, Anthropic published research examining whether Claude's chain-of-thought reasoning actually reflects its decision process. The findings were uncomfortable. Testing Claude 3.5 Haiku, they identified three distinct modes of reasoning: faithful reasoning, where the chain of thought genuinely tracks the model's process (this turned out to be rare); what researchers bluntly called "bullshitting," where the stated reasoning has no meaningful connection to the actual computation; and motivated reasoning, where the model arrives at a conclusion first, then reverse-engineers a logical-sounding chain of steps that leads there.

That last category — motivated reasoning — is the split-brain parallel at its sharpest. The model picks the shovel, then explains the chicken coop.

Why Confabulations Sound Better Than Truth

Here's the part that should genuinely worry anyone building on top of these systems: confabulated outputs consistently show higher narrative coherence than truthful ones. They flow better. They connect more neatly. They feel more satisfying to read.

This makes perfect sense once you see the mechanism. A truthful chain of reasoning has to follow the actual messy path of computation, with its dead ends and competing signals. A confabulated chain is free to optimize purely for narrative flow — for the thing that makes a human reader nod and move on. It's selecting for persuasiveness, not accuracy.

The split-brain patients showed the same pattern. Sperry and his colleague Michael Gazzaniga noted that the confabulated explanations were often more internally consistent than real explanations of real decisions. The left brain was, in a sense, a better storyteller when it was making things up, because it wasn't constrained by what actually happened.

For anyone using LLMs to explain their reasoning — in medicine, law, education, code review — this is a fundamental reliability problem. The more confident and coherent the explanation sounds, the less you can trust that it reflects what actually happened inside the model.

Extended Thinking Makes It Worse

A natural assumption is that giving models more room to think should help. OpenAI's o1 and Anthropic's Claude 3.7 Sonnet both introduced extended thinking modes that let models produce longer chains of reasoning before committing to an answer. More thinking, better results — right?

The Reddit thread and the underlying research suggest the opposite can happen. More tokens in a reasoning chain mean more opportunities for the confabulation machinery to activate. Each step in a long chain is another chance for the model to drift from faithful reasoning into motivated reasoning, each step optimizing not for truth but for coherence with the previous step.

Think of it this way: if a split-brain patient were given ten minutes to explain the shovel instead of ten seconds, you wouldn't get a more honest answer. You'd get a more elaborate story about chicken coops. The additional time lets the narrative engine do what it does best — build increasingly convincing explanations that are increasingly divorced from the actual cause.

This doesn't mean extended thinking is useless. On many benchmarks, it clearly helps. But it means the failure mode isn't "the model gives up and says it doesn't know." The failure mode is "the model produces a beautifully reasoned explanation that is entirely fabricated." That's harder to catch.

Where the Analogy Breaks Down

The comparison has limits, and they matter. Research from UC Santa Barbara published in December 2024 found that even a tiny remnant of intact corpus callosum fibers — a fraction of the original 200-250 million — is enough to restore unified consciousness in split-brain patients. The brain has remarkable flexibility to route around damage.

LLMs have no such flexibility. Their architecture is fixed at training time. There is no equivalent of those residual fibers that might spontaneously connect disconnected processing centers. Whatever integration exists between layers and attention heads is baked in during pre-training and fine-tuning. You can't add a corpus callosum to a transformer after the fact.

This is arguably the more pessimistic reading. Split-brain confabulation in humans is the result of extraordinary surgical intervention — it's the exception, not the rule. In LLMs, the structural disconnection between "deciding" and "explaining" is the default architecture. Every model confabulates not because something went wrong, but because that's how the system works.

What This Actually Means

The 164-comment thread on r/singularity didn't just argue about neuroscience analogies. It surfaced a practical question that matters right now: if you can't trust a model's stated reasoning, what can you trust?

Anthropic's research offers a partial answer. When models are evaluated on tasks where the faithful reasoning path can be independently verified — math problems with checkable steps, code with runnable tests — you can catch confabulation through external validation. The problem is all the tasks where you can't: open-ended analysis, strategic recommendations, medical reasoning, legal arguments. Exactly the high-value tasks that companies are most eager to automate.

The split-brain analogy doesn't prove that LLMs are conscious, or that they experience something like human confabulation at a subjective level. What it does is provide a concrete, testable framework for understanding a failure mode that is already shaping real debates about what these systems can and cannot do. When Anthropic's own models show motivated reasoning in controlled experiments, the chicken claw experiment stops being a metaphor and starts being a design specification.

The shovel was never for the chicken coop. And the chain of thought was never for explaining how the model reached its answer.