AI That Thinks Like Us – and Could Help Explain How We Think

For decades, psychology has aspired to explain the full complexity of human thought. Yet traditional models could either offer a transparent explanation of how people think – or reliably predict how they behave. Achieving both has long seemed out of reach. The team led by Dr. Marcel Binz and Dr. Eric Schulz, both researchers at the Institute for Human-Centered AI at Helmholtz Munich, has now developed a model that combines both. Centaur was trained using a specially curated dataset called Psych-101, which includes over ten million individual decisions from 160 behavioral experiments.

What makes Centaur unique is its ability to predict human behavior not only in familiar tasks, but also in entirely new situations it has never encountered before. It identifies common decision-making strategies, adapts flexibly to changing contexts – and even predicts reaction times with surprising precision. “We’ve created a tool that allows us to predict human behavior in any situation described in natural language – like a virtual laboratory,” says Marcel Binz, who is also the study’s lead author. Potential applications range from analyzing classic psychological experiments to simulating individual decision-making processes in clinical contexts – for example, in depression or anxiety disorders. The model opens up new perspectives in health research in particular – for example, by helping us understand how people with different psychological conditions make decisions. The dataset is set to be expanded to include demographic and psychological characteristics.

Centaur: Bridging Theory and Prediction

Centaur bridges two previously separate domains: interpretable theories and predictive power. It can reveal where classical models fall short – and provide insights into how they might be improved. This opens up new possibilities for research and real-world applications, from medicine to environmental science and the social sciences. “We’re just getting started and already seeing enormous potential,” says institute director Eric Schulz. Ensuring that such systems remain transparent and controllable is key, Binz adds – for example, by using open, locally hosted models that safeguard full data sovereignty.

Next, the researchers aim to take a closer look inside Centaur: Which computational patterns correspond to specific decision-making processes? Can they be used to infer how people process information – or how decision strategies differ between healthy individuals and those with mental health conditions? The researchers are convinced: “These models have the potential to fundamentally deepen our understanding of human cognition – provided we use them responsibly.” That this research is taking place at Helmholtz Munich rather than in the development departments of major tech companies is no coincidence. “We combine AI research with psychological theory – and with a clear ethical commitment,” says Binz. “In a public research environment, we have the freedom to pursue fundamental cognitive questions that are often not the focus in industry.”

What is Psych-101?

Psych-101 is a dataset specifically compiled by the team led by Marcel Binz for training the Centaur AI model. It contains over ten million individual decisions made by more than 60,000 participants across 160 psychological experiments. These experiments cover a wide range of human behavior – from risk-taking and reward learning to moral dilemmas.The researchers manually processed and standardized all the data to ensure that it could be interpreted by a language model. As such, Psych-101 represents a unique resource for systematically modeling human behavior based on natural language inputs.