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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.20268 |
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| _version_ | 1866909595197243392 |
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| author | Binz, Marcel Akata, Elif Bethge, Matthias Brändle, Franziska Callaway, Fred Coda-Forno, Julian Dayan, Peter Demircan, Can Eckstein, Maria K. Éltető, Noémi Griffiths, Thomas L. Haridi, Susanne Jagadish, Akshay K. Ji-An, Li Kipnis, Alexander Kumar, Sreejan Ludwig, Tobias Mathony, Marvin Mattar, Marcelo Modirshanechi, Alireza Nath, Surabhi S. Peterson, Joshua C. Rmus, Milena Russek, Evan M. Saanum, Tankred Schubert, Johannes A. Buschoff, Luca M. Schulze Singhi, Nishad Sui, Xin Thalmann, Mirko Theis, Fabian Truong, Vuong Udandarao, Vishaal Voudouris, Konstantinos Wilson, Robert Witte, Kristin Wu, Shuchen Wulff, Dirk Xiong, Huadong Schulz, Eric |
| author_facet | Binz, Marcel Akata, Elif Bethge, Matthias Brändle, Franziska Callaway, Fred Coda-Forno, Julian Dayan, Peter Demircan, Can Eckstein, Maria K. Éltető, Noémi Griffiths, Thomas L. Haridi, Susanne Jagadish, Akshay K. Ji-An, Li Kipnis, Alexander Kumar, Sreejan Ludwig, Tobias Mathony, Marvin Mattar, Marcelo Modirshanechi, Alireza Nath, Surabhi S. Peterson, Joshua C. Rmus, Milena Russek, Evan M. Saanum, Tankred Schubert, Johannes A. Buschoff, Luca M. Schulze Singhi, Nishad Sui, Xin Thalmann, Mirko Theis, Fabian Truong, Vuong Udandarao, Vishaal Voudouris, Konstantinos Wilson, Robert Witte, Kristin Wu, Shuchen Wulff, Dirk Xiong, Huadong Schulz, Eric |
| contents | Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational models, we currently do not have one model that captures the human mind in its entirety. A first step in this direction is to create a model that can predict human behavior in a wide range of settings. Here we introduce Centaur, a computational model that can predict and simulate human behavior in any experiment expressible in natural language. We derived Centaur by finetuning a state-of-the-art language model on a novel, large-scale data set called Psych-101. Psych-101 reaches an unprecedented scale, covering trial-by-trial data from over 60,000 participants performing over 10,000,000 choices in 160 experiments. Centaur not only captures the behavior of held-out participants better than existing cognitive models, but also generalizes to new cover stories, structural task modifications, and entirely new domains. Furthermore, we find that the model's internal representations become more aligned with human neural activity after finetuning. Taken together, our results demonstrate that it is possible to discover computational models that capture human behavior across a wide range of domains. We believe that such models provide tremendous potential for guiding the development of cognitive theories and present a case study to demonstrate this. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20268 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Centaur: a foundation model of human cognition Binz, Marcel Akata, Elif Bethge, Matthias Brändle, Franziska Callaway, Fred Coda-Forno, Julian Dayan, Peter Demircan, Can Eckstein, Maria K. Éltető, Noémi Griffiths, Thomas L. Haridi, Susanne Jagadish, Akshay K. Ji-An, Li Kipnis, Alexander Kumar, Sreejan Ludwig, Tobias Mathony, Marvin Mattar, Marcelo Modirshanechi, Alireza Nath, Surabhi S. Peterson, Joshua C. Rmus, Milena Russek, Evan M. Saanum, Tankred Schubert, Johannes A. Buschoff, Luca M. Schulze Singhi, Nishad Sui, Xin Thalmann, Mirko Theis, Fabian Truong, Vuong Udandarao, Vishaal Voudouris, Konstantinos Wilson, Robert Witte, Kristin Wu, Shuchen Wulff, Dirk Xiong, Huadong Schulz, Eric Machine Learning Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational models, we currently do not have one model that captures the human mind in its entirety. A first step in this direction is to create a model that can predict human behavior in a wide range of settings. Here we introduce Centaur, a computational model that can predict and simulate human behavior in any experiment expressible in natural language. We derived Centaur by finetuning a state-of-the-art language model on a novel, large-scale data set called Psych-101. Psych-101 reaches an unprecedented scale, covering trial-by-trial data from over 60,000 participants performing over 10,000,000 choices in 160 experiments. Centaur not only captures the behavior of held-out participants better than existing cognitive models, but also generalizes to new cover stories, structural task modifications, and entirely new domains. Furthermore, we find that the model's internal representations become more aligned with human neural activity after finetuning. Taken together, our results demonstrate that it is possible to discover computational models that capture human behavior across a wide range of domains. We believe that such models provide tremendous potential for guiding the development of cognitive theories and present a case study to demonstrate this. |
| title | Centaur: a foundation model of human cognition |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.20268 |