Publications
* denotes equally contributing authors
Peer-reviewed
Song M., Baah P.A., Cai M.B. & Niv Y. (2022). Humans combine value learning and hypothesis testing strategically in multi-dimensional probabilistic reward learning. PLoS computational biology, 18(11), e1010699. [pdf] [code]
Song M., Jones C.E., Monfils M.H., & Niv Y. (2022). Explaining the effectiveness of fear extinction through latent-cause inference. Neurons, Behavior, Data analysis, and Theory, https://doi.org/10.51628/001c.35660 [pdf] [code]
Song M., Takahashi Y.K., Burton A.C., Roesch M.R., Schoenbaum G., Niv, Y., & Langdon A.J. (2022). Minimal cross-trial generalization in learning the representation of an odor-guided choice task. PLoS computational biology, 18(3), e1009897. [pdf] [code]
Langdon A.J., Song M., Niv Y. (2019) Uncovering the “state”: tracing the hidden state representations that structure learning and decision-making. Behavioural Processes, 103891 [pdf]
Song M.*, Bnaya Z.*, Ma W.J. (2019) Sources of suboptimality in a minimalistic explore-exploit task. Nature Human Behaviour 3, 361–368. [pdf] [code]
Song M.*, Wang X.*, Zhang H., Li J. (2019) Proactive information sampling in value-based decision-making: deciding when and where to saccade. Frontiers in Human Neuroscience, 13, 35. [pdf]
Conference Proceedings
Song M., Niv Y., Cai M. B. (2021) Using Recurrent Neural Networks to Understand Human Reward Learning. 43rd Annual Meeting of the Cognitive Science Society [poster] [video] [paper]
Song M., Niv Y., Cai M.B. (2020) Learning multi-dimensional rules with probabilistic feedback via value-based serial hypothesis testing. Workshop on Biological and Artificial Reinforcement Learning, Thirty-fourth Conference on Neural Information Processing Systems (Neurips) [talk] [paper]
Song M., Niv Y., Cai M.B. (2020) Learning what is relevant for rewards via value-based serial hypothesis testing. 42nd Annual Virtual Meeting of the Cognitive Science Society (CogSci) [talk] [paper]
Song M., Cai M.B, Niv Y. (2019) Learning what is relevant for rewards via value learning and hypothesis testing. Computational and Cognitive Neuroscience Conference (CCN), Berlin, Germany [poster]
Song M., Langdon A., Takahashi Y.K., Schoenbaum G., Niv Y. (2019) Not smart enough: most rats fail to learn a parsimonious task representation. The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), Montreal, QC, Canada [poster & spotlight presentation]
Song M., Bnaya Z., Ma W.J. (2017) History effects in a minimalistic explore-exploit task. Computational and Cognitive Neuroscience Conference (CCN), New York, NY [talk]
Dissertation
Song M. (2022) Learning to Discover Structure in Animal and Human Decision Tasks. Princeton University [link] [pdf]
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