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Binz, M. ; Akata, E. ; Bethge, M.* ; Brändle, F.* ; Callaway, F.* ; Coda-Forno, J. ; Dayan, P.* ; Demircan, C. ; Eckstein, M.K.* ; Éltető, N.* ; Griffiths, T.L.* ; Haridi, S. ; Jagadish, A.K. ; Ji-An, L.* ; Kipnis, A. ; Kumar, S.* ; Ludwig, T.* ; Mathony. M. ; Mattar, M.* ; Modirshanechi, A. ; Nath, S.S.* ; Peterson, J.C.* ; Rmus, M. ; Russek, E.M.* ; Saanum, T. ; Schubert, J.A.* ; Schulze Buschoff, L.M. ; Singhi, N.* ; Sui, X.* ; Thalmann, M. ; Theis, F.J. ; Truong, V.* ; Udandarao, V.* ; Voudouris, K. ; Wilson, R.* ; Witte, K. ; Wu, S. ; Wulff, D.U.* ; Xiong, H.* ; Schulz, E.

A foundation model to predict and capture human cognition.

Nature, DOI: 10.1038/s41586-025-09215-4 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Establishing a unified theory of cognition has been an important goal in psychology1,2. A first step towards such a theory is to create a computational model that can predict human behaviour in a wide range of settings. Here we introduce Centaur, a computational model that can predict and simulate human behaviour in any experiment expressible in natural language. We derived Centaur by fine-tuning a state-of-the-art language model on a large-scale dataset called Psych-101. Psych-101 has an unprecedented scale, covering trial-by-trial data from more than 60,000 participants performing in excess of 10,000,000 choices in 160 experiments. Centaur not only captures the behaviour of held-out participants better than existing cognitive models, but it also generalizes to previously unseen cover stories, structural task modifications and entirely new domains. Furthermore, the model's internal representations become more aligned with human neural activity after fine-tuning. Taken together, our results demonstrate that it is possible to discover computational models that capture human behaviour across a wide range of domains. We believe that such models provide tremendous potential for guiding the development of cognitive theories, and we present a case study to demonstrate this.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 0028-0836
e-ISSN 1476-4687
Zeitschrift Nature
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540011-001
G-503800-001
Förderungen Google PhD Fellowship
Helmholtz Association's Initiative and Networking Fund on the HAICORE@FZJ partition
Else Kroner Medical Scientist Kolleg 'ClinbrAIn: Artificial Intelligence for Clinical Brain Research'
Machine Learning Cluster of Excellence (EXC)
NOMIS Foundation
Volkswagen Foundation
Humboldt Foundation
Max Planck Society
DOI 10.1038/s41586-025-09215-4
WOS ID 001521959200001
Scopus ID 105009611087
PubMed ID 40604288
Erfassungsdatum 2025-07-11
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