| Beyond eye gaze: What else can eyetracking reveal about cognition and cognitive development? MK Eckstein, B Guerra-Carrillo, ATM Singley, SA Bunge Developmental cognitive neuroscience 25, 69-91, 2017 | 771 | 2017 |
| Disentangling the systems contributing to changes in learning during adolescence SL Master, MK Eckstein, N Gotlieb, R Dahl, L Wilbrecht, AGE Collins Developmental cognitive neuroscience 41, 100732, 2020 | 84 | 2020 |
| What do reinforcement learning models measure? Interpreting model parameters in cognition and neuroscience MK Eckstein, L Wilbrecht, AGE Collins Current opinion in behavioral sciences 41, 128-137, 2021 | 82 | 2021 |
| Computational evidence for hierarchically structured reinforcement learning in humans MK Eckstein, AGE Collins Proceedings of the National Academy of Sciences 117 (47), 29381-29389, 2020 | 82 | 2020 |
| The unique advantage of adolescents in probabilistic reversal: Reinforcement learning and Bayesian inference provide adequate and complementary models MK Eckstein, SL Master, RE Dahl, L Wilbrecht, AGE Collins | 59* | 2021 |
| Options as responses: Grounding behavioural hierarchies in multi-agent reinforcement learning A Vezhnevets, Y Wu, M Eckstein, R Leblond, JZ Leibo International Conference on Machine Learning, 9733-9742, 2020 | 59 | 2020 |
| The interpretation of computational model parameters depends on the context MK Eckstein, SL Master, L Xia, RE Dahl, L Wilbrecht, AGE Collins Elife 11, e75474, 2022 | 58 | 2022 |
| Modeling changes in probabilistic reinforcement learning during adolescence L Xia, SL Master, MK Eckstein, B Baribault, RE Dahl, L Wilbrecht, ... PLoS computational biology 17 (7), e1008524, 2021 | 34 | 2021 |
| Beyond eye gaze: what else can eyetracking reveal about cognition and cognitive development? Dev. Cogn. Neurosci. 25, 69–91 MK Eckstein, B Guerra-Carrillo, AT Miller Singley, SA Bunge | 19 | 2017 |
| Cognitive model discovery via disentangled RNNs K Miller, M Eckstein, M Botvinick, Z Kurth-Nelson Advances in Neural Information Processing Systems 36, 2024 | 15 | 2024 |
| Predictive and interpretable: Combining artificial neural networks and classic cognitive models to understand human learning and decision making MK Eckstein, C Summerfield, ND Daw, KJ Miller BioRxiv, 2023.05. 17.541226, 2023 | 15 | 2023 |
| Learning rates are not all the same: The interpretation of computational model parameters depends on the context MK Eckstein, SL Master, L Xia, RE Dahl, L Wilbrecht, AGE Collins BioRxiv, 2021 | 15 | 2021 |
| How the mind creates structure: Hierarchical learning of action sequences MK Eckstein, AGE Collins CogSci... Annual Conference of the Cognitive Science Society. Cognitive …, 2021 | 14 | 2021 |
| Learning under uncertainty changes during adolescence L Xia, SL Master, M Eckstein, L Wilbrecht, AGE Collins | 13 | 2020 |
| How the inference of hierarchical rules unfolds over time MK Eckstein, A Starr, SA Bunge Cognition 185, 151-162, 2019 | 13 | 2019 |
| Beyond eye gaze: What else can eyetracking reveal about cognition and cognitive development? Dev Cogn Neurosci. 2017; 25: 69–91 MK Eckstein, B Guerra-Carrillo, AT Miller Singley, SA Bunge | 9 | |
| Nucleus accumbens dopamine release reflects bayesian inference during instrumental learning AJ Qü, LH Tai, CD Hall, EM Tu, MK Eckstein, K Mishchanchuk, WC Lin, ... bioRxiv, 2023.11. 10.566306, 2024 | 3 | 2024 |
| Evidence for hierarchically-structured reinforcement learning in humans MK Eckstein, AGE Collins Proceedings of the Annual Meeting of the Cognitive Science Society 40, 2018 | 3 | 2018 |
| Centaur: a foundation model of human cognition M Binz, E Akata, M Bethge, F Brändle, F Callaway, J Coda-Forno, ... arXiv preprint arXiv:2410.20268, 2024 | 2 | 2024 |
| Learning agents that acquire representations of social groups JZ Leibo, AS Vezhnevets, MK Eckstein, JP Agapiou, EA Duéńez-Guzmán Behav. Brain Sci. 45, e111, 2022 | 2 | 2022 |
Linda WilbrechtUniversity of California, BerkeleyVerified email at berkeley.edu
