Evidence for hierarchically-structured reinforcement learning in humans. - Maria K. Eckstein; Anne Collins (CogSci, 2018)

Learning under uncertainty changes during adolescence. - Liyu Xia; Sarah L. Master; Maria K. Eckstein; Linda Wilbrecht; Anne Collins (CogSci, 2020)

OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning. - Alexander Vezhnevets; Yuhuai Wu; Maria K. Eckstein; Rémi Leblond; Joel Z. Leibo (ICML, 2020)

Computational Models of Learning and Hierarchy - Maria K. Eckstein (2020)

How the Mind Creates Structure: Hierarchical Learning of Action Sequences. - Maria K. Eckstein; Anne G. E. Collins (CogSci, 2021)

Modeling changes in probabilistic reinforcement learning during adolescence. - Liyu Xia; Sarah L. Master; Maria K. Eckstein; Beth Baribault; Ronald E. Dahl; Linda Wilbrecht; Anne Gabrielle Eva Collins (PLoS Comput. Biol., 2021)

Predictive and Interpretable: Combining Artificial Neural Networks and Classic Cognitive Models to Understand Human Learning and Decision Making. - Maria K. Eckstein; Christopher Summerfield; Nathaniel D. Daw; Kevin J. Miller (CogSci, 2023)

Cognitive Model Discovery via Disentangled RNNs. - Kevin J. Miller; Maria K. Eckstein; Matt M. Botvinick; Zeb Kurth-Nelson (NeurIPS, 2023)

Centaur: a foundation model of human cognition. - Marcel Binz; Elif Akata; Matthias Bethge; Franziska Brändle; Fred Callaway; Julian Coda-Forno; Peter Dayan; Can Demircan; Maria K. Eckstein; Noémi Élteto; Thomas L. Griffiths 001; Susanne Haridi; et al. (CoRR, 2024)

A foundation model to predict and capture human cognition. - Marcel Binz; Elif Akata; Matthias Bethge; Franziska Brändle; Fred Callaway; Julian Coda-Forno; Peter Dayan; Can Demircan; Maria K. Eckstein; Noémi Élteto; Thomas L. Griffiths 001; Susanne Haridi; et al. (Nat., 2025)