| Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience A Fengler, LN Govindarajan, T Chen, MJ Frank Elife 10, e65074, 2021 | 73 | 2021 |
| Beyond drift diffusion models: Fitting a broad class of decision and reinforcement learning models with HDDM A Fengler, K Bera, ML Pedersen, MJ Frank Journal of cognitive neuroscience 34 (10), 1780-1805, 2022 | 22 | 2022 |
| A Hitchhiker’s Guide to Bayesian Hierarchical Drift-Diffusion Modeling with docker-HDDM W Pan, H Geng, L Zhang, A Fengler, M Frank, R Zhang, H Chuan-Peng | 7 | 2022 |
| Predicted utility modulates working memory fidelity in the brain EJ Levin, JA Brissenden, A Fengler, D Badre Cortex 160, 115-133, 2023 | 6 | 2023 |
| Encoder-Decoder Neural Architectures for Fast Amortized Inference of Cognitive Process Models. A Fengler, LN Govindarajan, MJ Frank CogSci, 2020 | 5 | 2020 |
| ABC-NN: Approximate Bayesian Computation with Neural Networks to learn likelihood functions for efficient parameter estimation A Fengler, M Frank | 1 | 2019 |
| Modelling History-Dependent Evidence Accumulation across Species A Urai, ZG Gunes, K Fernandez, A Fengler Proceedings of the Annual Meeting of the Cognitive Science Society 46, 2024 | | 2024 |
| The Perils of Omitting Omissions when Modeling Evidence Accumulation X Leng, A Fengler, A Shenhav, MJ Frank Proceedings of the Annual Meeting of the Cognitive Science Society 46, 2024 | | 2024 |
| Likelihood Approximations for Bayesian Analysis of Sequential Sampling Models A Fengler Brown University Providence, Rhode Island, 2023 | | 2023 |
| Beyond Drift Diffusion Models: Fitting a broad class of decision and RL models with HDDM A Fengler, K Bera, ML Pedersen, MJ Frank bioRxiv, 2022.06. 19.496747, 2022 | | 2022 |
| Likelihood Approximation Networks enable fast estimation of generalized sequential sampling models as the choice rule in RL K Bera, A Fengler, MJ Frank | | |
Michael J FrankProfessor, Director Center for Computational Brain Science, Brown University在 brown.edu 的电子邮件经过验证
