Publications of E Schulz

Schulz, E.; Tenenbaum, J.; Duvenaud, D.; Speekenbrink , M.; Gershman, S.: Compositional Inductive Biases in Function Learning. Cognitive Neuroscience 99, pp. 44 - 79 (2017)

Dasgupta, I.; Schulz, E.; Gershman, S.: Where Do Hypotheses Come From? Cognitive Psychology 96, pp. 1 - 25 (2017)

Cokely, E.; Galesic, M.; Schulz, E.; Ghazal, S.; Garcia-Retamero, R.: Measuring risk literacy: The Berlin numeracy test. Judgment and Decision Making 7 (1), pp. 25 - 47 (2012)

Schulz, E.; Cokely, E.; Feltz, A.: Persistent bias in expert judgments about free will and moral responsibility: A test of the expertise defense. Consciousness and Cognition 20 (4), pp. 1722 - 1731 (2011)

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)

Cokely, E.; Ghazal, S.; Galesic, M.; García-Retamero, R.; Schulz, E.: How to measure risk comprehension in educated samples. In: Transparent communication of health risks: Overcoming cultural differences, pp. 29 - 52. Springer, New York, NY, USA (2013)

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: Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024). Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024), Vancouver, Canada, December 11, 2024 - December 15, 2024. (2024)

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)

Haridi, S.; Thalmann, M.; Schulz, E.: The Effect of Set Size on Long-Term-Memory Retrieval Times in Cued Recall. 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)

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)

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)

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)

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)