By Benjamin Ransom
We build AI systems to mimic the human brain: writing emails, answering questions, and predicting what comes next. But new research aims to turn that relationship around—using large language models (LLMs) to explore how our brains anticipate and process stories.
"I think that the way that an LLM represents events is similar to how humans do. That's a really interesting part of our research," said Bella Summe, a fourth-year data science major currently involved in a research project in the Cognition, Attention and Brain Lab at the University of Chicago.
The project, directed by psychology Associate Professor Monica Rosenberg, is aimed to determine if large language models can predict a fundamental process in human cognition—surprise. Their approach was to compare how humans and AI respond to the same narrative moments.
“By comparing human brain and behavioral responses with surprise signals from large language models, we can identify where AI mirrors human understanding of narratives—and where it diverges,” said Ziwei Zhang, a doctoral student supervising Summe’s work.
To gather data on human surprise, participants listened to stories while researchers recorded their responses in real time using brain scans.
To determine corresponding surprise signals from AI, the researchers fed the same stories to the language model Llama, prompting it to predict what text would come next after each story chunk.
When the AI’s prediction and the actual story were mismatched, that gap served as a measure of surprise. The premise is simple—surprise is what happens when our predictions fail. If an AI’s prediction about future plot is different from reality, that mismatch might mirror the discrepancy human readers feel.
Modeling the mind with AI
The results showed a striking alignment: The AI's prediction errors correlated with both what participants reported feeling and the activity patterns in their brain scans.
In other words, the AI’s predictions diverged when participants reported surprise, and when their brain scans indicated surprise.
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