Brändle, F.; Wu, C.; Schulz, E.: What are we curious about? Trends in Cognitive Sciences 24 (9), pp. 685 - 687 (2020)

Wu, C.; Schulz, E.; Garvert, M.; Meder, B.; Schuck, N.: Similarities and differences in spatial and non-spatial cognitive maps. PLoS Computational Biology 16 (9) (2020)

Schulz, E.; Quiroga, F.; Gershman, S.: Communicating compositional patterns. Open Mind: Discoveries in Cognitive Science 4, pp. 25 - 39 (2020)

Cogliati Dezza, I.; Wu, C.; Schulz, E.: The Drive for Knowledge: The Science of Human Information-Seeking. Cambridge University Press, Cambridge, UK (2022), 292 pp.

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)

Cogliati Dezza, I.; Schulz, E.; Wu, C.: Future challenges. In: The drive for knowledge: The science of human information seeking, pp. 279 - 290 (Eds. Cogliati Dezza, I.; Schulz, E.; Wu, C.). Cambridge University Press, Cambridge, UK (2022)

Modirshanechi, A.; Brändle, F. (Eds.): Surprise in the brain: Theory and Experiment. Bernstein 2022 Satellite Workshop: Surprise in the brain: Theory and Experiments, Berlin, Germany, September 13, 2022. (2022)

Brändle, F.; Kessler, S.; Ruggeri, A.; Schulz, E.: Uncertainty-driven little alchemists: Differences in exploration strategies between adults and children in an online game. In: 46th Annual Conference of the Cognitive Science Society (CogSci 2024). 46th Annual Conference 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: 41st International Conference on Machine Learning (ICML 2024). 41st International Conference on Machine Learning (ICML 2024), Wien, Austria, July 21, 2024 - July 27, 2024. (2024)

Wu, S.; Thalmann, M.; Schulz, E.: Learning abstractions from discrete sequences. In: 46th Annual Conference of the Cognitive Science Society (CogSci 2024). 46th Annual Conference of the Cognitive Science Society (CogSci 2024), Rotterdam, The Netherlands, July 24, 2024 - July 27, 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)

Salewski, L.; Alaniz, S.; Rio-Torto, I.; Schulz, E.; Akata, Z.: In-Context Impersonation Reveals Large Language Models’ Strengths and Biases. In: Advances in Neural Information Processing Systems 36: 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 3152, pp. 72044 - 72057 (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)

Schreiber, A.; Wu, S.; Wu, C.; Indiveri, G.; Schulz, E.: Biologically-plausible hierarchical chunking on mixed-signal neuromorphic hardware. In: NeurIPS 2023 Workshop: Machine Learning with New Compute Paradigms. NeurIPS 2023 Workshop: Machine Learning with New Compute Paradigms, New Orleans, LA, USA, December 16, 2023. (2023)

Brändle, F.; Schulz, E.; Wu, C.: Learning progress and uncompensated rewards as motivational drivers of engagement. In: 2023 Conference on Cognitive Computational Neuroscience, P-2.114, pp. 832 - 835. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, August 24, 2023 - August 27, 2023. (2023)

Haridi, S.; Thalmann, M.; Schulz, E.: Simulating the Scaling of Long-Term Memory Retrieval. In: 2023 Conference on Cognitive Computational Neuroscience, P-2.101, pp. 784 - 787. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, August 24, 2023 - August 27, 2023. (2023)

Hedrich, N.; Hall-McMaster, S.; Schulz, E.; Schuck, N.: Faster learning from slow features: The temporal coherence prior in human reinforcement learning. In: 2023 Conference on Cognitive Computational Neuroscience, P-2.10, pp. 458 - 461. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, August 24, 2023 - August 27, 2023. (2023)

Ludwig, T.; Siegel, M.; Schulz, E.: Human Multi-Task Learning: the Why and What. In: 2023 Conference on Cognitive Computational Neuroscience, P-1.119, pp. 418 - 421. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, August 24, 2023 - August 27, 2023. (2023)

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)