A foundation model to predict and capture human cognition
Abstract
Abstract 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.
Article Details
Authors (40)
Marcel Binz
Elif Akata
Matthias Bethge
Franziska Brändle
Fred Callaway
Julian Coda-Forno
Peter Dayan
Can Demircan
Maria K. Eckstein
Noémi Éltető
Thomas L. Griffiths
Susanne Haridi
Akshay K. Jagadish
Li Ji-An
Alexander Kipnis
Sreejan Kumar
Tobias Ludwig
Marvin Mathony
Marcelo Mattar
Alireza Modirshanechi
Surabhi S. Nath
Joshua C. Peterson
Milena Rmus
Evan M. Russek
Tankred Saanum
Johannes A. Schubert
Luca M. Schulze Buschoff
Nishad Singhi
Xin Sui
Mirko Thalmann
Fabian J. Theis
Vuong Truong
Vishaal Udandarao
Konstantinos Voudouris
Robert Wilson
Kristin Witte
Shuchen Wu
Dirk U. Wulff
Huadong Xiong
Eric Schulz