Publications of E Schulz

Nath, S.; Brändle, F.; Schulz, E.; Dayan, P.; Brielmann, A.: Spatial complexity and intricacy predict subjective complexity. The Visual Science of Art Conference (VSAC 2022), Amsterdam, The Netherlands (2022)

Schäfer, T.; Schulz, E.; Theves, S.; Doeller, C.: Effects of prototype abstraction on pattern completion and inference in concept space. 28th Annual Meeting of the Organization for Human Brain Mapping (OHBM 2022) , Glasgow, UK (2022)

Schäfer, T.; Schulz, E.; Theves, S.; Doeller, C.: Effects of prototype abstraction on pattern completion and inference in concept space. 29th Annual Meeting of the Cognitive Neuroscience Society (CNS 2022), San Francisco, CA, USA (2022)

Saanum, T.; Garvert, M.; Schulz, E.; Schuck, N.; Doeller, C.: Hippocampal spatio-temporal cognitive maps adaptively guide reward generalization. Computational and Systems Neuroscience Meeting (COSYNE 2022), Lisboa, Portugal (2022)

Haridi, S.; Wu, C.; Dasgupta, I.; Schulz, E.: How does mental sorting scale? 43rd Annual Conference of the Cognitive Science Society (CogSci 2021), Wien, Austria (2021)

Schäfer, T.; Schulz, E.; Theves, S.; Doeller, C.: Effects of prototype abstraction on pattern completion and inference in concept space. 50th Annual Meeting of the Society for Neuroscience (Neuroscience 2021) (2021)

Wu, S.; Elteto, N.; Dasgupta, I.; Schulz, E.: Chunking as a rational solution to the speed-accuracy trade-off in a serial reaction time task. 43rd Annual Conference of the Cognitive Science Society (CogSci 2021), Wien, Austria (2021)

Rothe, A.; Schulz, E.; Sablé Meyer, M.; Tenenbaum, J.; Ruggeri, A.: Learning sequential patterns from graphical programs. 42nd Annual Virtual Meeting of the Cognitive Science Society (CogSci 2020), Toronto, Canada (2020)

Parpart, P.; Schulz, E.: Is covariance ignorance responsible for the success of heuristics? 40th Annual Cognitive Science Society Meeting (CogSci 2018), Madison, WI, USA (2019)

Stojic, H.; Schulz, E.; Speekenbrink, M.: It is new, but will it be good? Context-driven exploration of novel options. 39th Annual Meeting of the Cognitive Science Society (CogSci 2017), London, UK (2017)

Schulz, E.: Towards a unifying theory of generalization. Dissertation, 257 pp., University College London, London, UK (2017)

Coda-Forno, J.; Binz, M.; Wang, J.; Schulz, E.: CogBench: a large language model walks into a psychology lab. (submitted)

Jagadish, A.; Coda-Forno, J.; Thalmann, M.; Schulz, E.; Binz, M.: Ecologically rational meta-learned inference explains human category learning. (submitted)

Schubert, J.; Jagadish, A.; Binz, M.; Schulz, E.: In-context learning agents are asymmetric belief updaters. (submitted)

Wu, C.; Meder, B.; Schulz, E.: Unifying principles of generalization: past, present, and future. (submitted)

Hedrich, N.; Schulz, E.; Hall-McMaster, S.; Schuck, N.: An inductive bias for slowly changing features in human reinforcement learning. (submitted)

Binz, M.; Alaniz, S.; Roskies, A.; Aczel, B.; Bergstrom, C.; Allen, C.; Schad, D.; Wulff, D.; West, J.; Zhang, Q. et al.; Shriffrin, R.; Gershman, S.; Popov, V.; Bender, E.; Marelli, M.; Botvinick, M.; Akata, Z.; Schulz, E.: How should the advent of large language models affect the practice of science? (submitted)

Thalmann, M.; Schäfer, T.; Theves, S.; Doeller, C.; Schulz, E.: Task Imprinting: Another Mechanism of Representational Change? (submitted)

Wu, S.; Thalmann, M.; Schulz, E.: Motif Learning Facilitates Sequence Memorization and Generalization. (submitted)

Schulze Buschoff, L.; Akata, E.; Bethge, M.; Schulz, E.: Have we built machines that think like people? (submitted)