“Our understanding of human behavior, including decision making, can be deepened by asking people to elaborate on their thought processes,” says lead author Dr. Kamil Fuławka, researcher at SynoSys. “However, the systematic analysis of such free-text data requires scalable and rigorous analytical frameworks — an endeavor that can now be supported by LLMs”
In the experiment, participants took part in gambling and had to explain each decision in their own words. To analyze these explanations, the researchers drew on existing theories and models of decision making to develop a large set of possible decision reasons, such as focusing on the best possible outcome or avoiding a big loss. Large language models (LLMs) identified which of these reasons appeared in participants’ free-text explanations, while mathematical modeling of people’s choices provided validation.
The combination of verbal reports, LLMs, and rigorous mathematical modeling clearly demonstrated that people’s own insights are a valuable source of data. It also showed that the reasons people rely on are not fixed, but shift systematically with the structure of the decision problem.
“Many important decisions — from financial planning and medical choices to social dilemmas, technology use, and public policy — involve complex trade-offs that cannot be fully understood from observing choices alone,” says Kamil Fuławka, emphasizing the relevance of the study’s findings: “In such settings, people’s own explanations may be especially valuable for revealing how they simplify complex problems, focus on particular pieces of information, and adaptively use simple decision strategies.” The framework presented in the study shows how LLMs can help researchers analyze these explanations at scale, opening new opportunities to study human decision making in more realistic and complex environments.”
The framework presented in the study also demonstrates how LLMs can help researchers analyze these explanations on a large scale, thereby opening up innovative possibilities for studying human decision-making processes in more realistic and complex environments.
“Large language models accurately identify decision reasons in verbal reports” by Kamil Fuławka, Ralph Hertwig, and Dirk U. Wulff was published in the Proceedings of the National Academy of Sciences (PNAS) on June 30, 2026. Read the article: https://www.pnas.org/doi/10.1073/pnas.2526798123
Kamil Fuławka
Center Synergy of Systems (SynoSys)
TUD Dresden University of Technology
Email: kamil.fulawka@tu-dresden.de
TUD’s Press Office
Phone: +49 351 463-32398
Email: pressestelle@tu-dresden.de
Proceedings of the National Academy of Sciences
Large language models accurately identify decision reasons in verbal reports
30-Jun-2026