Publications of M Binz

Binz, M.; Akata, E.; Bethge, M.; Brändle, F.; Callaway, F.; Coda-Forno, J.; Dayan, P.; Demircan, C.; Eckstein, M.; Éltetö, N. et al.; Griffiths, T.; Haridi, S.; Jagadish, A.; Li, J.-A.; Kipnis, A.; Kumar, S.; Ludwig, T.; Mathony, M.; Mattar, M.; Modirshanechi, A.; Nath, S.; Peterson, J.; Rmus, M.; Russek, E.; Saanum, T.; Schubert, J.; Schulze Buschoff, L.; Singhi, N.; Sui, X.; Thalmann, M.; Theis, F.; Truong, V.; Udandarao, V.; Voudouris, K.; Wilson, R.; Witte, K.; Wu, S.; Wulff, D.; Xiong, H.; Schulz, E.: A foundation model to predict and capture human cognition. Nature 644 (8078), pp. 1002 - 1009 (2025)

Binz, M.; Alaniz, S.; Roskies, A.; Aczel, B.; Bergstrom, C.; Allen, C.; Schad, D.; Wulff, D.; West, J.; Zhang, Q. et al.; Shiffrin, R.; Gershman, S.; Popov, V.; Bender, E.; Marelli, M.; Botvinick, M.; Akata, Z.; Schulz, E.: How should the advancement of large language models affect the practice of science? Proceedings of the National Academy of Sciences of the United States of America 122 (5), e2401227121 (2025)

Hussain, Z.; Binz, M.; Mata, R.; Wulff, D.: A tutorial on open-source large language models for behavioral science. Behavior Research Methods 56 (8), pp. 8214 - 8237 (2024)

Binz, M.; Dasgupta, I.; Jagadish, A.; Botvinick, M.; Wang, J.; Schulz, E.: Meta-learning: Data, architecture, and both. Behavioral and Brain Sciences 47, e170 (2024)

Binz, M.; Dasgupta, I.; Jagadish, A.; Botvinick, M.; Wang, J.; Schulz, E.: Meta-Learned Models of Cognition. Behavioral and Brain Sciences 47, e147 (2024)

Binz, M.; Schulz, E.: Reconstructing the Einstellung effect. Computational Brain & Behavior 6 (3), pp. 526 - 542 (2023)

Binz, M.; Schulz, E.: Using cognitive psychology to understand GPT-3. Proceedings of the National Academy of Sciences of the United States of America 120 (6), e2218523120 (2023)

Binz, M.; Gershman, S.; Schulz, E.; Endres, D.: Heuristics From Bounded Meta-Learned Inference. Psychological Review 129 (5), pp. 1042 - 1077 (2022)

Brändle, F.; Binz, M.; Schulz, E.: Exploration beyond bandits. In: The Drive for Knowledge: The Science of Human Information-Seeking, pp. 147 - 168 (Eds. Cogliati Dezza, I.; Wu, C.; Schulz, E.). Cambridge University Press, Cambridge, UK (2022)

Binz, M.; Jagadish, A.; Rmus, M.; Schulz, E.: Automated scientific minimization of regret. In: AI4Mat-NeurIPS-2025: NeurIPS 2025 Workshop on AI for Accelerated Materials Design. AI4Mat-NeurIPS-2025: NeurIPS 2025 Workshop on AI for Accelerated Materials Design, San Diego, CA, USA, December 06, 2025. (2025)

Demircan, C.; Saanum, T.; Pettini, L.; Binz, M.; Baczkowski, B.; Doeller, C.; Garvert, M.; Schulz, E.: Evaluating alignment between humans and neural network representations in image-based learning tasks. In: NIPS '24: Proceedings of the 38th International Conference on Neural Information Processing Systems, 3890, pp. 122406 - 122433 (Eds. Globerson, A.; Mackey, L.; Belgrave, D.; Fan, A.; Paquet, U. et al.). Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024), Vancouver, Canada, December 11, 2024 - December 15, 2024. Curran, Red Hook, NY, USA (2025)

Demircan, C.; Saanum, T.; Jagadish, A.; Binz, M.; Schulz, E.: Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models. In: Thirteenth International Conference on Learning Representations (ICLR 2025). Thirteenth International Conference on Learning Representations (ICLR 2025), Singapore, April 24, 2025 - April 28, 2025. (2025)

Jagadish, A.; Dasgupta, I.; Lerousseau, J.; Binz, M.: In-context learning in natural and artificial intelligence. In: 46th Annual Conference of the Cognitive Science Society (CogSci 2024), pp. 6 - 7. In-Context Learning in Natural and Artificial Intelligence: Workshop at CogSci 2024, Rotterdam, The Netherlands, July 24, 2024. (2024)

Binz, M.; Schulz, E.: Turning large language models into cognitive models. In: Twelfth International Conference on Learning Representations: ICLR 2024. Twelfth International Conference on Learning Representations (ICLR 2024), Wien, Austria, May 07, 2024 - May 11, 2024. (2024)

Coda-Forno, J.; Binz, M.; Akata, Z.; Botvinick, M.; Wang, J.; Schulz, E.: Meta-in-context learning in large language models. In: Advances in Neural Information Processing Systems 36: 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 2844, pp. 65189 - 65201 (Eds. Ho, A.; Naumann, T.; Globerson, A.; Saenko, K.; Hardt, M. et al.). Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, LA, USA, December 10, 2023 - December 16, 2023. Curran, Red Hook, NY, USA (2024)

Saanum, T.; Éltetö, N.; Dayan, P.; Binz, M.; Schulz, E.: Reinforcement Learning with Simple Sequence Priors. In: Advances in Neural Information Processing Systems 36: 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 2710, pp. 61985 - 62005 (Eds. Oh, A.; Naumann, T.; Globerson, A.; Saenko, K.; Hardt, M. et al.). Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, LA, USA, December 10, 2023 - December 16, 2023. Curran, Red Hook, NY, USA (2024)

Coda-Forno, J.; Binz, M.; Wang, J.; Schulz, E.: CogBench: a large language model walks into a psychology lab. In: Proceedings of Machine Learning Research: PLMR, Vol. 235, pp. 9076 - 9108. 41st International Conference on Machine Learning (ICML 2024), Wien, Austria, July 21, 2024 - July 27, 2024. (2024)

Ichien, N.; Bhatia, S.; Ivanova, A.; Webb, T.; Griffiths, T.; Binz, M.: Higher cognition in large language models. In: 46th Annual Meeting of the Cognitive Science Society (CogSci 2024), pp. 34 - 35 (Eds. Samuelson, L.; Frank, S.; Toneva, M.; Mackey, A.; Hazeltine, E.). 46th Annual Meeting of the Cognitive Science Society (CogSci 2024), Rotterdam, The Netherlands, July 24, 2024 - July 27, 2024. (2024)

Jagadish, A.; Coda-Forno, J.; Thalmann, M.; Schulz, E.; Binz, M.: Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks. In: Proceedings of Machine Learning Research: PLMR, Vol. 235, pp. 21121 - 21147. 41st International Conference on Machine Learning (ICML 2024), Wien, Austria, July 21, 2024 - July 27, 2024. (2024)

Schubert, J.; Jagadish, A.; Binz, M.; Schulz, E.: In-Context Learning Agents Are Asymmetric Belief Updaters. In: Proceedings of Machine Learning Research: PLMR, Vol. 235, pp. 43928 - 43946. 41st International Conference on Machine Learning (ICML 2024), Wien, Austria, July 21, 2024 - July 27, 2024. (2024)