Schulz, Eric

A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions

Schulz, E., Speekenbrink, M., & Krause, A. (2018) · Journal of Mathematical Psychology

Gaussian Processes and Bayesian InferenceAdvanced Bandit Algorithms Research

Cited by 1,735

Using cognitive psychology to understand GPT-3

Binz, M., & Schulz, E. (2023) · Proceedings of the National Academy of Sciences

Topic ModelingPsychiatry, Mental Health, Neuroscience

Cited by 559

A foundation model to predict and capture human cognition

Binz, M., Akata, E., Bethge, M., Brändle, F., Callaway, F., Coda-Forno, J., Dayan, P., Demircan, C., Eckstein, M. K., Éltető, N., et al. (2025) · Nature

Cognitive Science and Education ResearchDecision-Making and Behavioral Economics

Cited by 119

Playing repeated games with Large Language Models

Akata, E., Schulz, L., Coda-Forno, J., Oh, S. J., Bethge, M., & Schulz, E. (2023) · arXiv

Game Theory and ApplicationsEvolutionary Game Theory and Cooperation

Cited by 99

Visual cognition in multimodal large language models

Schulze Buschoff, L. M., Akata, E., Bethge, M., & Schulz, E. (2025) · Nature Machine Intelligence

Multimodal Machine Learning ApplicationsLogic, Reasoning, and Knowledge

Cited by 83

How should the advancement of large language models affect the practice of science?

Binz, M., Alaniz, S., Roskies, A., Aczel, B., Bergstrom, C. T., Allen, C., Schad, D., Wulff, D., West, J. D., Zhang, Q., et al. (2025) · Proceedings of the National Academy of Sciences

Artificial Intelligence ApplicationsEthics and Social Impacts of AI

Cited by 74

A theory of learning to infer.

Dasgupta, I., Schulz, E., Tenenbaum, J. B., & Gershman, S. J. (2020) · Psychological Review

Bayesian Modeling and Causal InferenceDecision-Making and Behavioral Economics

Cited by 70

Meta-learned models of cognition

Binz, M., Dasgupta, I., Jagadish, A. K., Botvinick, M., Wang, J. X., & Schulz, E. (2024) · Behavioral and Brain Sciences

Cognitive Science and Education ResearchBayesian Modeling and Causal Inference

Cited by 38

Empowerment contributes to exploration behaviour in a creative video game

Brändle, F., Stocks, L. J., Tenenbaum, J. B., Gershman, S. J., & Schulz, E. (2023) · Nature Human Behaviour

Digital Games and MediaEducational Games and Gamification

Cited by 34

Heuristics from bounded meta-learned inference.

Binz, M., Gershman, S. J., Schulz, E., & Endres, D. (2022) · Psychological Review

Decision-Making and Behavioral EconomicsCategorization, perception, and language

Cited by 31

Meta-in-context learning in large language models

Coda-Forno, J., Binz, M., Akata, Z., Botvinick, M., Wang, J., & Schulz, E. (2023) · Advances in Neural Information Processing Systems 36

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 20

CogBench: a large language model walks into a psychology lab

Coda-Forno, J., Binz, M., Wang, J. X., & Schulz, E. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 15

Amortized Hypothesis Generation

Dasgupta, I., Schulz, E., Goodman, N. D., & Gershman, S. J. (2017) · bioRxiv (Cold Spring Harbor Laboratory)

Bayesian Modeling and Causal InferenceCognitive Science and Education Research

Cited by 7

metabench -- A Sparse Benchmark of Reasoning and Knowledge in Large Language Models

Kipnis, A., Voudouris, K., Buschoff, L. M. S., & Schulz, E. (2024) · arXiv

Topic ModelingMachine Learning and Data Classification

Cited by 1