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Maria K. Eckstein · SciRah

Researcher profile · Semantic Scholar

Maria K. Eckstein

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Maria K. Eckstein — researcher profile on SciRah

h-index: 15
Works: 36
Citations: 1,656

Most-cited papers

Beyond eye gaze: What else can eyetracking reveal about cognition and cognitive development?
Maria K. Eckstein, Belén C. Guerra-Carrillo, Alison T. Miller Singley, S. Bunge
2016Developmental Cognitive Neuroscience648 citations Open access
Semantic Scholar
A foundation model to predict and capture human cognition
Marcel Binz, Elif Akata, Matthias Bethge, Franziska Brändle +36 more
2025Nature258 citations Open access
Semantic Scholar
Distentangling the systems contributing to changes in learning during adolescence
Sarah L. Master, Maria K. Eckstein, N. Gotlieb, R. Dahl +2 more
2019bioRxiv93 citations Open access
Semantic Scholar
The interpretation of computational model parameters depends on the context
Maria K. Eckstein, Sarah L. Master, Liyu Xia, R. Dahl +2 more
2021bioRxiv89 citations Open access
Semantic Scholar
Computational evidence for hierarchically structured reinforcement learning in humans
Maria K. Eckstein, A. Collins
2019Proceedings of the National Academy of Sciences of the United States of America87 citations Open access
Semantic Scholar
What do Reinforcement Learning Models Measure? Interpreting Model Parameters in Cognition and Neuroscience
Maria K. Eckstein, L. Wilbrecht, Anne G. E. Collins
2021Current Opinion in Behavioral Sciences86 citations Open access
Semantic Scholar
Centaur: a foundation model of human cognition
Marcel Binz, Elif Akata, Matthias Bethge, Franziska Brändle +37 more
2024arXiv.org60 citations
Semantic Scholar
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning
A. Vezhnevets, Yu-Huai Wu, Maria K. Eckstein, R. Leblond +1 more
2020International Conference on Machine Learning48 citations
Semantic Scholar
Discovering Symbolic Cognitive Models from Human and Animal Behavior
Pablo Samuel Castro, Nenad Tomašev, Ankit Anand, Navodita Sharma +13 more
2025bioRxiv47 citations Open access
Semantic Scholar
Cognitive Model Discovery via Disentangled RNNs
Kevin J. Miller, Maria K. Eckstein, M. Botvinick, Z. Kurth-Nelson
2023bioRxiv40 citations Open access
Semantic Scholar
Modeling changes in probabilistic reinforcement learning during adolescence
Liyu Xia, Sarah L. Master, Maria K. Eckstein, Beth Baribault +3 more
2020bioRxiv39 citations Open access
Semantic Scholar
Reinforcement learning and Bayesian inference provide complementary models for the unique advantage of adolescents in stochastic reversal
Maria K. Eckstein, Sarah L. Master, Ronald E. Dahl, L. Wilbrecht +1 more
2022Developmental Cognitive Neuroscience36 citations Open access
Semantic Scholar
Reinforcement Learning and Bayesian Inference Provide Complementary Models for the Unique Advantage of Adolescents in Stochastic Reversal
Maria K. Eckstein, Sarah L. Master, R. Dahl, L. Wilbrecht +1 more
2020bioRxiv33 citations Open access
Semantic Scholar
Predictive and Interpretable: Combining Artificial Neural Networks and Classic Cognitive Models to Understand Human Learning and Decision Making
Maria K. Eckstein, C. Summerfield, N. Daw, Kevin J. Miller
2023bioRxiv21 citations Open access
Semantic Scholar
How the Mind Creates Structure: Hierarchical Learning of Action Sequences
Maria K. Eckstein, A. Collins
2021Annual Meeting of the Cognitive Science Society16 citations
Semantic Scholar
Hybrid neural–cognitive models reveal how memory shapes human reward learning
Maria K. Eckstein, Christopher Summerfield, N. D. Daw, Kevin J. Miller
2026Nature Human Behaviour15 citations Open access
Semantic Scholar
How the Inference of Hierarchical Rules Unfolds Over Time
Maria K. Eckstein, Ariel Starr, S. Bunge
2019Cognition11 citations Open access
Semantic Scholar
Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning
Albert J. Qü, Lung-Hao Tai, Christopher D. Hall, Emilie M. Tu +8 more
2025PLoS Comput. Biol.10 citations Open access
Semantic Scholar
Post-training makes large language models less human-like
Marcel Binz, Elif Akata, Abdullah Almaatouq, Mohammed Alsobay +75 more
2026arXiv.org8 citations
Semantic Scholar
Learning under uncertainty changes during adolescence
Liyu Xia, Sarah L. Master, Maria K. Eckstein, L. Wilbrecht +1 more
2020Annual Meeting of the Cognitive Science Society8 citations
Semantic Scholar
Functional reorganization of motor cortex connectivity during learning
Kayvon Daie, Kyle Aitken, Márton Rózsa, Matthew S. Bull +13 more
2026bioRxiv3 citations Open access
Semantic Scholar
The Unique Advantage of 1 Adolescents in Probabilistic Reversal: 2 Reinforcement Learning and 3 Bayesian Inference Provide 4 Adequate and Complementary 5 Models 6
Maria K. Eckstein, Sarah L. Master, R. Dahl, L. Wilbrecht
20213 citations
Semantic Scholar
AI-Discovered Cognitive Models Reveal Novel Insights into Human and Animal Learning
Daniel Kasenberg, Pablo Samuel Castro, Maria K. Eckstein, N. Éltető +12 more
2026bioRxiv2 citations Open access
Semantic Scholar
Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning
Albert J. Qü, Lung-Hao Tai, Christopher D. Hall, Emilie M. Tu +8 more
2023bioRxiv2 citations Open access
Semantic Scholar
Reasoning with programs in replay
S. Veselic, Nour Mohsen, Lennart Luettgau, Elena Gutierrez +5 more
2025bioRxiv1 citations
Semantic Scholar
Corrigendum to "Disentangling the systems contributing to changes in learning during adolescence" [Dev. Cogn. Neurosci. 41, 2020, 100732].
Sarah L. Master, Maria K. Eckstein, N. Gotlieb, R. Dahl +2 more
2020Developmental Cognitive Neuroscience1 citations Open access
Semantic Scholar
Modeling the development of decision making in volatile environments using strategies, reinforcement learning, and Bayesian inference
Maria K. Eckstein, Sarah L. Master, R. Dahl, L. Wilbrecht +1 more
20192019 Conference on Cognitive Computational Neuroscience1 citations Open access
Semantic Scholar
Evidence for hierarchically-structured reinforcement learning in humans
Maria K. Eckstein, A. Collins
2018Annual Meeting of the Cognitive Science Society1 citations
Semantic Scholar
Reuse of learned subtask representations during human planning
L. C. U. Seak, Lennart Luettgau, Maria K. Eckstein, Rani Moran +1 more
2026bioRxiv0 citations Open access
Semantic Scholar
Hybrid Neural-Cognitive Models Reveal Flexible Context-Dependent Information Processing in Reversal Learning
Yi-Fei Cao, Maria K. Eckstein
2025bioRxiv0 citations Open access
Semantic Scholar
Physical reasoning during motor learning aids people in transferring mass, but not motor control mappings
Fabian Tatai, Dominik Ürüm, Maria K. Eckstein, C. Rothkopf
2025Annual Meeting of the Cognitive Science Society0 citations
Semantic Scholar
Low dimensional latent structure underlying the choices of mice
Sebastian A. Bruijns, Maria K. Eckstein, P. Dayan
2025bioRxiv0 citations Open access
Semantic Scholar
The role of subgoals in hierarchical reinforcement learning
Milena Rmus, Maria K. Eckstein, Anne G. E. Collins
20232023 Conference on Cognitive Computational Neuroscience0 citations Open access
Semantic Scholar
The Unique Advantage of 1 Adolescents in Probabilistic Reversal: 2 Reinforcement Learning and 3 Bayesian Inference Provide 4 Adequate and Complementary 5 Models 6
Maria K. Eckstein, Sarah L. Master, Ronald E. Dahl, Linda Wilbrecht +1 more
20210 citations
Semantic Scholar
Computational Models of Learning and Hierarchy
Maria K. Eckstein
20200 citations
Semantic Scholar
Models Don’t Know What You Want: Evaluating Personalization in Deep Research Needs Real Users . Association for Computational Linguistics
Nishant Balepur, Malachi Hamada, V. Kishore, Sergey Feldman +355 more
0 citations
Semantic Scholar

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