Publications of E Schulz

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)

Modirshanechi, A.; Dayan, P.; Schulz, E.: An integrative framework for the human sense of control. (submitted)

Wu, S.; Alaniz, S.; Karthik, S.; Dayan, P.; Schulz, E.; Akata, Z.: Concept-Guided Interpretability via Neural Chunking. (submitted)

Rmus, M.; Jagadish, A.; Mathony, M.; Ludwig, T.; Schulz, E.: Towards Automation of Cognitive Modeling using Large Language Models. (submitted)

Binz, M.; Akata, E.; Bethge, M.; Brändle, F.; Callaway, F.; Coda-Forno, J.; Dayan, P.; Demircan, C.; Eckstein, M.; Éltetö, N. et al.; 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.: Centaur: a foundation model of human cognition. (submitted)

Demircan, C.; Saanum, T.; Jagadish, A.; Binz, M.; Schulz, E.: Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models. (submitted)

Vélez, N.; Wu, C.; Gershman, S.; Schulz, E.: The rise and fall of technological development in virtual communities. (submitted)

Brändle, F.; Wu, C.; Schulz, E.: Leveling up fun: Learning progress, expectations, and success influence enjoyment in video games. (submitted)

Schäfer, T.; Thalmann, M.; Schulz, E.; Doeller, T.; Theves, S.: The hippocampus supports interpolation into new states during category abstraction. (submitted)

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

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)

Thalmann, M.; Schäfer, T.; Theve, S.; Doeller, C.; Schulz, E.: What changes with representational change? (submitted)

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