Publikationen von E Schulz

Thalmann, M.; Schaefer, T.; Theves, S.; Doeller, C.; Schulz, E.: Do Mental Representations Adapt to Task Specific Goals? 63rd Annual Meeting of the Psychonomic Society (PS 2022), Boston, MA, USA (2022)

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 S., University College London, London, UK (2017)

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

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. (eingereicht)

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

Saanum, T.; Schulze Buschoff, L.; Dayan, P.; Schulz, E.: Next state prediction gives rise to entangled, yet compositional representations of objects. (eingereicht)

Wu, S.; Thalmann, M.; Dayan, P.; Akata, Z.; Schulz, E.: Building, Reusing, and Generalizing Abstract Representations from Concrete Sequences. (eingereicht)

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

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

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