Publications of M Binz

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)

Schubert, J.; Jagadish, A.; Binz, M.; Schulz, E.: A Rational Analysis of the Optimism Bias using Meta-Reinforcement Learning. In: 2023 Conference on Cognitive Computational Neuroscience, P-2.37, pp. 557 - 559. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, August 24, 2023 - August 27, 2023. (2023)

Schulze Buschoff, L.; Schulz, E.; Binz, M.: The Acquisition of Physical Knowledge in Generative Neural Networks. In: 40th International Conference on Machine Learning, pp. 30321 - 30341 (Eds. Krause, A.; Brunskill, E.; Cho, K.; Engelhardt, B.; Sabato, S. et al.). Fortieth International Conference on Machine Learning (ICML 2023), Honolulu, HI,USA, July 23, 2023 - July 29, 2023. (2023)

Binz, M.; Schulz, E.: Modeling Human Exploration Through Resource-Rational Reinforcement Learning. In: Advances in Neural Information Processing Systems 35: 36th Conference on Neural Information Processing Systems (NeurIPS 2022), pp. 31755 - 31768 (Eds. Koyejo, S.; Mohamed, S.). Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, USA, November 28, 2022 - December 09, 2022. Curran, Red Hook, NY, USA (2023)

Demircan, C.; Pettini, L.; Saanum, T.; Binz, M.; Baczkowski, B.; Doeller, C.; Garvert, M.; Schulz, E.: Decision-Making with Naturalistic Options. In: 44th Annual Meeting of the Cognitive Science Society (CogSci 2022): Cognitive Diversity, pp. 976 - 982 (Eds. Culbertson, J.; Perfors, A.; Rabagliati, H.; Ramenzoni, V.). 44th Annual Meeting of the Cognitive Science Society (CogSci 2022): Cognitive Diversity, Toronto, Canada, July 27, 2022 - July 30, 2022. (2022)

Jagadish, A.; Saanum, T.; Wang, J.; Binz, M.; Schulz, E.: Probing Compositional Inference in Natural and Artificial Agents. In: 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2022), 1.67, pp. 275 - 279. 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2022), Providence, RI, USA, June 08, 2022 - June 11, 2022. (2022)

Binz, M.; Schulz, E.: Using cognitive psychology to understand GPT-3. In Subjective Probability, Utility & Decision Making (SPUDM 2023), p. 31. Subjective Probability, Utility & Decision Making (SPUDM 2023), Wien, Austria, August 20, 2023 - August 24, 2023. (2023)

Tutorial 2: Meta-Learned Models of Cognition. 15th Biannual Conference of the German Society for Cognitive Science (KogWis 2022), Freiburg (Breisgau), Germany (2022)

Truong, V.; Binz, M.; Bartels, A.: Fear and anxiety influences on probabilistic learning: A pilot online study and computational modeling. 23rd Conference of Junior Neuroscientists (NeNa 2022), Bad Urach, Germany (2022)

Jagadish, A.; Binz, M.; Saanum, T.; Wang, J.; Schulz, E.: Zero-shot compositional reinforcement learning in humans. (submitted)

Binz, M.; Schulz, E.: Turning large language models into cognitive models. (submitted)

Demircan, C.; Saanum, T.; Pettini, L.; Binz, M.; Baczkowski, B.; Kaanders, P.; Doeller, C.; Garvert, M.; Schulz, E.: Language Aligned Visual Representations Predict Human Behavior in Naturalistic Learning Tasks. (submitted)

Coda-Forno, J.; Binz, M.; Akata, Z.; Botvinick, M.; Wang, J.; Schulz, E.: Meta-in-context learning in large language models. (submitted)

Saanum, T.; Éltetö, N.; Dayan, P.; Binz, M.; Schulz, E.: Reinforcement Learning with Simple Sequence Priors. (submitted)

Binz, M.; Dasgupta, I.; Jagadish, A.; Botvinick, M.; Wang, J.; Schulz, E.: Meta-Learned Models of Cognition. (submitted)

Coda-Forno, J.; Witte, K.; Jagadish, A.; Binz, M.; Akata, Z.; Schulz, E.: Inducing anxiety in large language models increases exploration and bias. (submitted)

Binz, M.; Schulz, E.: Modeling Human Exploration Through Resource-Rational Reinforcement Learning. (submitted)