Researcher Portfolio

 
   

Schulz, Eric William

Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Max Planck Society, External Organizations, Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society  

 

Researcher Profile

 
Position: Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Max Planck Society
Position: Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society
Position: External Organizations
Additional IDs: MPITUE: eschulz
ORCID: https://orcid.org/0000-0003-3088-0371
Researcher ID: https://pure.mpg.de/cone/persons/resource/persons139782

External references

 

Publications

 
  (1 - 25 of 191)
 : Demircan, C., Saanum, T., Pettini, L., Binz, M., Baczkowski, B., Doeller, C., Garvert, M., & Schulz, E. (2024). 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). [PubMan] : Hedrich, N., Schulz, E., Hall-McMaster, S., & Schuck, N. (2024). An inductive bias for slowly changing features in human reinforcement learning. PLOS Computational Biology, 20(11): e1012568. doi:10.1371/journal.pcbi.1012568. [PubMan] : Wu, S., Thalmann, M., Dayan, P., Akata, Z., & Schulz, E. (submitted). Building, Reusing, and Generalizing Abstract Representations from Concrete Sequences. [PubMan] : Demircan, C., Saanum, T., Jagadish, A., Binz, M., & Schulz, E. (submitted). Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models. [PubMan] : Binz, M., Akata, E., Bethge, M., Brändle, F., Callaway, F., Coda-Forno, J., Dayan, P., Demircan, C., Eckstein, M., Éltetö, N., Griffiths, T., Haridi, S., Jagadish, A., Ji-An, Kipnis, A., Kumar, S., Ludwig, T., Mathony, M., Mattar, M., Modirshanechi, A., Nath, S., Peterson, J., Rmus, M., Russek, E., Saanum, T., Scharfenberg, N., 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. (submitted). Centaur: a foundation model of human cognition. [PubMan] : Saanum, T., Schulze Buschoff, L., Dayan, P., & Schulz, E. (submitted). Next state prediction gives rise to entangled, yet compositional representations of objects. [PubMan] : Vélez, N., Wu, C., Gershman, S., & Schulz, E. (submitted). The rise and fall of technological development in virtual communities. [PubMan] : Binz, M., Dasgupta, I., Jagadish, A., Botvinick, M., Wang, J., & Schulz, E. (2024). Meta-learning: Data, architecture, and both. Behavioral and Brain Sciences, 47: e170. doi:10.1017/S0140525X24000311. [PubMan] : Thalmann, M., Schäfer, T., Theves, S., Doeller, C., & Schulz, E. (2024). Task Imprinting: Another Mechanism of Representational Change? Cognitive Neuroscience, 152: 101670. doi:10.1016/j.cogpsych.2024.101670. [PubMan] : Haridi, S., Thalmann, M., & Schulz, E. (2024). 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). [PubMan] : Brändle, F., Kessler, S., Ruggeri, A., & Schulz, E. (2024). 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). [PubMan] : Wu, S., Thalmann, M., & Schulz, E. (2024). Learning abstractions from discrete sequences. In 46th Annual Conference of the Cognitive Science Society (CogSci 2024). [PubMan] : Nath, S., Brändle, F., Schulz, E., Dayan, P., & Brielmann, A. (2024). Relating Objective Complexity, Subjective Complexity and Beauty in Binary Pixel Patterns. Psychology of Aesthetics, Creativity, and the Arts, Epub ahead. doi:10.1037/aca0000657. [PubMan] : Allen, K., Brändle, F., Botvinick, M., Fan, J., Gershman, S., Gopnik, A., Griffiths, T., Hartshorne, J., Hauser, T., Ho, M., de Leeuw, J., Ma, W., Murayama, K., Nelson, J., van Opheusden, B., Poundsy, T., Rafner, J., Rahwan, I., Rutledge, R., Sherson, J., Şimşek, Ö., Spiers, H., Summerfield, C., Thalmann, M., Vélez, N., Watrous, A., Tenenbaum, J., & Schulz, E. (2024). Using Games to Understand the Mind. Nature Human Behaviour, 8(6), 1035-1043. doi:10.1038/s41562-024-01878-9. [PubMan] : Saanum, T., Éltetö, N., Dayan, P., Binz, M., & Schulz, E. (2024). Reinforcement Learning with Simple Sequence Priors. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Advances in Neural Information Processing Systems 36: 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (pp. 61985-62005). Red Hook, NY, USA: Curran. [PubMan] : Salewski, L., Alaniz, S., Rio-Torto, I., Schulz, E., & Akata, Z. (2024). In-Context Impersonation Reveals Large Language Models’ Strengths and Biases. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Advances in Neural Information Processing Systems 36: 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (pp. 72044-72057). Red Hook, NY, USA: Curran. [PubMan] : Schäfer, T., Thalmann, M., Schulz, E., Doeller, T., & Theves, S. (submitted). The hippocampus supports interpolation into new states during category abstraction. [PubMan] : Coda-Forno, J., Binz, M., Akata, Z., Botvinick, M., Wang, J., & Schulz, E. (2024). Meta-in-context learning in large language models. In A. Ho, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Advances in Neural Information Processing Systems 36: 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (pp. 65189-65201). Red Hook, NY, USA: Curran. [PubMan] : Binz, M., & Schulz, E. (2024). Turning large language models into cognitive models. In Twelfth International Conference on Learning Representations (ICLR 2024). [PubMan] : Wu, S., Yoerueten, M., Wichmann, F., & Schulz, E. (2024). Normalized Cuts Characterize Visual Recognition Difficulty of Amorphous Image Sub-parts. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2024), Lisboa, Portugal. [PubMan] : Wu, C., Meder, B., & Schulz, E. (submitted). Unifying principles of generalization: past, present, and future. [PubMan] : Coda-Forno, J., Binz, M., Wang, J., & Schulz, E. (submitted). CogBench: a large language model walks into a psychology lab. [PubMan] : Binz, M., Dasgupta, I., Jagadish, A., Botvinick, M., Wang, J., & Schulz, E. (2024). Meta-Learned Models of Cognition. Behavioral and Brain Sciences, 47: e147. doi:10.1017/S0140525X23003266. [PubMan] : Ruggeri, A., Stanciu, O., Pelz, M., Gopnik, A., & Schulz, E. (2024). Preschoolers search longer when there is more information to be gained. Developmental Science, 27(1): e13411. doi:10.1111/desc.13411. [PubMan] : Schubert, J., Jagadish, A., Binz, M., & Schulz, E. (2024). In-Context Learning Agents Are Asymmetric Belief Updaters. In Proceedings of Machine Learning Research: PLMR (pp. 43928-43946). [PubMan]