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Home » The new “Centaur” AI model can predict how we behave with unprecedented accuracy
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The new “Centaur” AI model can predict how we behave with unprecedented accuracy

userBy userJuly 9, 2025No Comments4 Mins Read
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New AI (AI) models can predict and simulate human thoughts and behaviors with incredible accuracy. A linguistic model called Centaur helps researchers improve their understanding of human cognition.

This model was trained on over 10 million actual decisions made by participants in psychological experiments. Using this dataset, Centaur predicted and simulated how people think and behave with 64% accuracy, researchers reported in Nature on July 2.

Previous AI models could predict human behavior, but not to this extent. “The centaur is a step beyond predictive ability,” Brenden Lake, a researcher at New York University in psychology and data science and not involved in the research, told Live Science in an email.

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What is Centaur?

Centaur is an AI model of human cognition trained on a curated dataset called Psych-101, which contains data from 60,000 people who made more than 10 million personal choices in 160 psychological experiments. Researchers say it may be the world’s largest human behavior data set.

“Essentially, we show the model the complete transcription of a psychological experiment from participants. We’ve seen everything they were told and did it,” Marcel Binz, a research scientist at the Helmholtz Institute in Germany, told Live Science in an email.

The team then lets the model predict the experiments selected by participants in a particular context. If a model predicts a choice a person has not made in real life, researchers fine-tune the model by modifying the choice. They repeated this process many times until Centaur regularly made the correct predictions.

Related: Scientists discover a big difference between how humans and AI think – and their meaning can be important

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Research shows that when tested against several established AI models of human thinking, Centaur predicted behavior more accurately each time. According to a statement from the German Association of Research Centres, the model is unique because it can predict human choices in situations that have never been encountered during training.

In particular, Centaur can also adapt to changing circumstances and predict human reaction times.

“We have created tools that can predict human behavior in any situation explained in natural language, like a virtual laboratory,” Binz said in a statement.

Researching the brain “on an unprecedented scale”

Binz and his team will continue to improve the new model. They want to expand Psych-101 to include demographic and psychological data such as age, socioeconomic status, and personality traits to enhance their training capabilities. These details allow Centaur to predict behavior based on individual characteristics.

Their next goal is to use Centaur as a proxy for the human brain to determine whether certain patterns seen in programmatic data processing correlate with specific decisions. This information can help answer questions about how people process information and how decisions differ with or without mental health, for example, according to the statement.

“Now we essentially have a big black box model that predicts human behavior very well,” Binz told Live Science. Thanks to Centaur, researchers can predict how humans behave, but they still have no insight into how they reach a particular decision.

Currently, one big unknown is whether Centaur predicts human thoughts, whether it replicates the underlying cognitive processes, Lake explained. “Is it modeling human mental processes or is it just mimicking the outcome?”

Centaur opens the door to many improvements in psychology and health research. Applications may include predicting human behavior in clinical contexts or prototyping and analysis of psychological experiments. Scientists can, for example, find ways to use models to design experiments that demonstrate the phenomenon most clearly.

The lake is most excited about the possible educational applications. “In the long term, if students can predict how they learn and why through the problem, they can simulate the effects of different educational strategies – potential game-changers,” he said.

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