Publikationen von E Schulz

Schulz, E.; Wu, C.; Ruggeri, A.; Meder, B.: Searching for rewards like a child means less generalization and more directed exploration. Psychological Science 30 (11), S. 1561 - 1572 (2019)

Schulz, E.; Bhui, R.; Love, B.; Brier, B.; Todd, M.; Gershman, S.: Structured, uncertainty-driven exploration in real-world consumer choice. Proceedings of the National Academy of Sciences of the United States of America 116 (28), S. 13903 - 13908 (2019)

Schulz, E.; Gershman, S.: The algorithmic architecture of exploration in the human brain. Current Opinion in Neurobiology 55, S. 7 - 14 (2019)

Wu, C.; Schulz, E.; Speekenbrink, M.; Nelson, J.; Meder, B.: Generalization guides human exploration in vast decision spaces. Nature Human Behaviour 2 (12), S. 915 - 924 (2018)

Schulz, E.; Wu, C.; Huys, Q.; Krause, A.; Speekenbrink, M.: Generalization and search in risky environments. Cognitive Science 42 (8), S. 2592 - 2620 (2018)

Dasgupta, I.; Schulz, E.; Goodman, N.; Gershman , S.: Remembrance of Inferences Past: Amortization in Human Hypothesis Generation. Cognition 178, S. 67 - 81 (2018)

Schulz, E.; Speekenbrink, M.; Krause, A.: A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions. Journal of Mathematical Psychology 85, S. 1 - 16 (2018)

Schulz, E.; Konstantinidis, E.; Speekenbrink, M.: Putting Bandits Into Context: How Function Learning Supports Decision Making. Journal of Experimental Psychology: Learning, Memory, and Cognition 44 (6), S. 927 - 943 (2018)

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

Dasgupta, I.; Schulz, E.; Gershman, S.: Where Do Hypotheses Come From? Cognitive Psychology 96, S. 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), S. 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), S. 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 S.

Brändle, F.; Binz, M.; Schulz, E.: Exploration beyond bandits. In: The Drive for Knowledge: The Science of Human Information-Seeking, S. 147 - 168 (Hg. 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, S. 279 - 290 (Hg. 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, S. 29 - 52. Springer, New York, NY, USA (2013)

Brändle, F.; Schulz, E.; Wu, C.: Learning progress and uncompensated rewards as motivational drivers of engagement. In: 2023 Conference on Cognitive Computational Neuroscience, P-2.114, S. 832 - 835. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, 24. August 2023 - 27. August 2023. (2023)

Haridi, S.; Thalmann, M.; Schulz, E.: Simulating the Scaling of Long-Term Memory Retrieval. In: 2023 Conference on Cognitive Computational Neuroscience, P-2.101, S. 784 - 787. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, 24. August 2023 - 27. August 2023. (2023)

Hedrich, N.; Hall-McMaster, S.; Schulz, E.; Schuck, N.: Faster learning from slow features: The temporal coherence prior in human reinforcement learning. In: 2023 Conference on Cognitive Computational Neuroscience, P-2.10, S. 458 - 461. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, 24. August 2023 - 27. August 2023. (2023)

Ludwig, T.; Siegel, M.; Schulz, E.: Human Multi-Task Learning: the Why and What. In: 2023 Conference on Cognitive Computational Neuroscience, P-1.119, S. 418 - 421. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, 24. August 2023 - 27. August 2023. (2023)